crm software research paper

Evolution of customer relationship management to data mining-based customer relationship management: a scientometric analysis

• Published: 27 August 2022
• Volume 57 , pages 3241–3272, ( 2023 )

Cite this article

• Minnu F. Pynadath 1 , 2 ,
• T. M. Rofin   ORCID: orcid.org/0000-0003-2777-4658 3 &
• Sam Thomas 4  

6574 Accesses

5 Citations

6 Altmetric

Explore all metrics

Scores of researchers have paid attention to empirical and conceptual dimensions of Customer relationship management (CRM). A few studies summarise the research output of CRM focusing on a specific industry. Nevertheless, there is scant literature summarising the research output of CRM in contrast to the data mining-based CRM. This study presents a scientometric analysis that evaluates CRM research output with a special focus on data mining-based CRM. Bibliometric data were extracted for the period 2000–2020 from the Web of Science database to apply descriptive analysis and scientometric analysis to obtain the bibliometric profile of CRM research. Further, we generated the conceptual structure map using multiple correspondence analysis and clustering for CRM and data mining-based CRM research fields. Interestingly, the analysis revealed that the future trendfi of CRM research would be based on techniques associated with machine learning and artificial intelligence. The study provides extensive insight into the basic structure of the CRM and data mining-based CRM research domain and identifies future research areas.

Similar content being viewed by others

Big Data Analytics for Customer Relationship Management: A Systematic Review and Research Agenda

Comparative study of customer relationship management research from East Asia, North America and Europe: A bibliometric overview

Overlaps Between Business Intelligence and Customer Relationship Management – Is There a Place for E-Commerce?

Avoid common mistakes on your manuscript.

1 Introduction

Customer relationship management comprises a set of processes and enabling systems supporting a business strategy to build profitable long-term relationships with specific customers (Azadeh et al. 2017 ; Capuano et al. 2021 ; Tsou. 2022 ). The objective of CRM is the personalised handling of customers (Li et al. 2006 ) by identifying and understanding their heterogeneous needs, interests, and preferences to develop customer loyalty using information systems (Khan et al. 2022 ). Although the objective of CRM has remained somewhat consistent over the past three decades, the way CRM has been implemented in organisations has been radically transformed on account of the advancement in internet and computing technologies (Nguyen et al. 2020 ; Herman et al. 2021 ). This transformation in CRM implementation is warranted by the significant growth in sales volume across all industries and the evolution of distribution channels brought about by digitalisation (Mahdavi et al. 2011 ).

The digitalisation trend, facilitated by technologies such as IoT, digital twin, and control tower, resulted in the generation of enormous volumes of customer data. i.e., big data (Anshari et al. 2019 ) further adds to the complexity of business decision-making. To make effective business decisions in such a dynamic and complex business environment, CRM has undergone a phase shift into data mining-based CRM (DCRM) or analytical CRM (Liou 2009 ; Ngai et al. 2009 ; Tu and Yang 2013 ). DCRM is characterised by the application of various data mining techniques such as classification (Lessmann and Vob, 2009 ; Tu and Yang, 2013 ; Keramati et al. 2014 ), clustering (Carpaneto et al. 2006 ; Hosseini et al. 2010 ; Wang 2010 ), regression (Yap et al. 2011 ; De Caigny et al. 2018 ; Biswas et al. 2020 ), association (Liao et al. 2010 ; Shim et al. 2012 ) and prediction (Lee et al. 2017 ; Ahmad et al. 2019 ; Martínez et al. 2020 ). These techniques help extract valuable information from large data sets for synthesising useful customer information (Liao et al. 2008 ; Tsiptsis and Chorianopoulos, 2009 ).

With the increasing practical applications of data mining techniques for CRM, there is a corresponding increase in the number of research articles addressing DCRM across various industries (Ledro et al. 2022 ). There are a few studies that summarise the recent research output of the CRM domain (Ngai et al. 2009 ; Sota et al. 2020 ; Soltani and Navimipour, 2016 ) and some studies focus on industries such as healthcare (Baashar et al. 2020 ) and hospitality (Sota et al. 2020 ). These studies, despite their valuable contribution, have some limitations (i) None of these studies has explored the possibility of scientometric techniques to understand the CRM domain (ii) these studies have considered the research articles only from selected publishers than considering a citation database of peer-reviewed literature such as Scopus or Web of Science. This implies that the datasets analysed currently are not comprehensive in representation (iii). These studies have not captured the evolving nature of CRM with advancements in data-analytics tools and techniques. Thus, a critical and rigorous review is warranted, considering the limitations of the extant studies in this area. Considering the above-mentioned research gaps, we pose the following research questions.

What is the current status of CRM research? How does it compare with the DCRM research?

What are the trends in CRM and DCRM research concerning the output and citations?

What is the future trajectory and evolving areas of CRM and DCRM research?

Since CRM systems are predicted to evolve at an accelerated pace with the integration of technologies like artificial intelligence (AI) (Deb et al. 2018 ) and with the radical change in consumer behaviour due to pandemics like COVID-19 (Wright, 2020 ), it is imperative to quantitatively analyse CRM research field to understand its current state and to predict the future directions. To address this research gap, we focus on the following research objectives in this study.

Carry out a bibliometric search to identify relevant articles from the research field of CRM and DCRM for the time horizon of 2000–2020.

Apply descriptive analysis to identify the leading journals, leading universities, and leading authors based on the research output in the domain of CRM and DCRM

Apply scientometric analysis to identify the leading journals, contemporary topics, important topics, leading institutes, and leading countries based on the number of citations in the CRM and DCRM.

Develop the conceptual structure of CRM and DCRM research domain using a word-co-occurrence network.

The meet the objectives mentioned above, scientometric analysis (SA) was carried out to deeply explore and compare the research domains of CRM and DCRM. SA is an analysis technique using bibliometric data to plot the scientific landscape of any research field. It helps a researcher quantify the academic impact and academic scholars’ profiling (Mingers and Leydesdorff 2015 ; Sarkar and Maiti, 2020 ). The rationale for applying SA tools is that the conventional literature search using keywords may not always provide a good insight to researchers when the topic for research is complex and massive (Rodrigues et al. 2014 ). SA generates a data-driven version of scientific research output, and its ability to visualise the research output can help the researchers perceive the research status. The application of SA is not restricted to engineering or business rather, this technique has been applied extensively to domains such as healthcare (Fang, 2015 ), disaster management (Sahil and Sood, 2021 ; Sood and Rawat, 2021 ), education (Rawat and Sood, 2021 ) to name a few. SA can be viewed as a combination of various scientometric techniques, information visualisation techniques and text mining to study the development and growth of a research field (Darko et al. 2019 ). In this study, we have adopted a triangulation methodology (Chandra, 2018 ) by simultaneously considering the following techniques (i) topic mapping, (ii) journal co-citation analysis and (iii) overlay visualisation.

By comparing the research landscape of CRM and DCRM, we note that there are both similarities and differences. For instance, while there are differences concerning influential journals, there are similarities in the case of leading universities. It is interesting to note the additional insights obtained from the citation-based analysis compared to the output-based analysis. Further, the conceptual structure map developed from the co-word analysis of keywords gives an overall picture of distinct groups in the research domain of CRM and DCRM.

This paper aims to contribute in the following ways (i) The study contributes to the existing body of knowledge by analysing the current status of CRM and its evolution into DCRM. (ii) This study compares the CRM research domain with the DCRM research domain in terms of several parameters with the help of bibliometric and scientometric analysis techniques (iii) This study identifies the future research directions in the domain of CRM and DCRM.

The remaining part of this paper is organised as follows. In Sect. 2, we present the Research Methodology. Section 3 reports the results obtained from descriptive analysis and scientometric analysis. Section 4 deals with the implications of the study, and the study concludes with limitations in Sect. 5.

2 Research methodology

This section outlines the research methods and tools used in conducting this study. Figure  1 shows a flowchart of the research methodology.

Flowchart of the four-phase literature review process

2.1 Step 1: Bibliometric search

Time Horizon —The time horizon selected for the systematic review process is 2000–2020. In other words, we have considered only those papers published between 2000 and 2020. Though the CRM and DCRM research domain is growing, we have set the upper limit for the time horizon as 2020 since the data was collected in January 2021.

Database selection —The data used for the analysis is extracted from the Web of Science (WoS) database, which has been commonly used for mining bibliometric data. Further, since CRM systems are essentially software systems and are inclined towards the engineering domain, we selected WoS as a suitable database. The comprehensive coverage of this database for bibliographical and SA (Moed, 2010 ; Larsen and Von Ins, 2010 ) has been discussed by many researchers (Michels and Schmoch, 2012 ; Rubbo et al. 2019 ). WoS was the preferred choice of the database not only due to the comprehensive coverage but also owing to its frequent appearance in previous literature reviews (Bangsa and Schlegelmilch, 2020 ) and the availability of refined search options (Prieto-Sandoval et al. 2016 )

Selection of journals and articles —It was observed that CRM frequently appears in journals focusing on domains such as business, management, marketing, consumer behaviour, management science and industrial engineering. A search in the WoS database using the keyword “CUSTOMER RELATIONSHIP MANAGEMENT” within the 2000–2020 period has yielded 6710 documents. After screening this dataset of documents to consider only research articles, a set of 3878 research articles were obtained as publications for analysis. Similarly, another search conducted with ‘DATA MINING and CRM’ as keywords within the 2000–2020 period resulted in a total of 357 documents and screening them for research articles led to 203 research papers. Both the datasets containing information such as Title, Abstract, Keywords and References were extracted in CSV (comma-separated values) file format for further analysis. Conference proceedings, conference papers and books were excluded by limiting the search to the document type “Article”. We have considered only those articles which belong to peer-reviewed journals published in English.

2.2 Step 2: Descriptive analysis

We carried out a descriptive analysis to identify the following (i) Annual publication trend of CRM and DCRM from 2000 to 2020, (ii) Leading Journals in terms of the number of articles from 2000 to 2020, (iii) Leading Universities/Institutes in CRM and DCRM research in terms of the number of articles from 2000 to 2020 (iv) Leading Authors in CRM and DCRM in terms of the number of articles and their year-wise output from 2000 to 2020.

2.3 Step 3: Scientometric analysis

The central idea behind SA is the knowledge integration of a domain and understanding of the structure and pattern of the field with the help of quantitative and statistical analysis (Van Eck and Waltman, 2011 ; Rawat and Sood 2021 ). It provides an interesting way of understanding topics that emerged in a dynamic research field, taking bibliometric data as an input to the analysis (Saini and Sood, 2021 ). It is mainly used to establish relationships between nodes such as publications, authors, sources/journals or keywords. Relations among the nodes are indicated through edges connecting the weighted nodes. Edges show not only the existence of a relationship but also the strength of the relationship. The most popular type of relationships among the nodes is studied using citation analysis, co-occurrence of keywords, co-citation analysis, bibliographic coupling and co-authorship analysis (Van Eck and Waltman, 2014 ).

Two software tools are used to conduct SA, viz., R programming and VOSviewer. VOSviewer is freely accessible software based on Java used to construct and visualise large bibliometric networks based on natural language processing and text mining algorithms (Van Eck and Waltman 2011 ). VOSviewer yields a two-dimensional map where the similarity of items is demonstrated by their position on the map (Van Eck and Waltman 2011 ). These maps could include any journals, author’s name or documents using various bibliometric techniques like citation, co-citation, bibliometric coupling, keyword co-occurrence and co-authorship. The unique feature of VOSviewer is to zoom the large networks helps examine the network closely and quickly obtain information. Therefore, we have selected VOSviewer for visualising and evaluating the CRM and DCRM research domain.

In this study, we have employed VOSviewer for executing (i) Time-based overlay visualisation map, (iii) Citation-based overlay visualisation map, (iv) Author-based co-citation analysis, (v) Journal-based co-citation analysis, (vi) Institute-based co-author analysis.

Further, we relied on the open-source R-package ‘Bibliometrix’ for generating the conceptual structure map. The function ‘conceptualStructure’ map in R-package is based on multiple correspondence analysis and K-means clustering. Multiple correspondence analysis, an extension of correspondence analysis, is used to project observations in a continuous space (Le Phan and Tortora 2019 ). To compare CRM and DCRM as prospective fields of research, a set of analysis were conducted, and maps were created using VOSviewer. In the following section, we report the data analysis and the results obtained along with their interpretation.

3 Data analysis and results

3.1 descriptive analysis.

Firstly, in Fig.  2 , we present the growth in the number of research papers in the domain of CRM and DCRM.

Annual publication trend of CRM and data-driven CRM from 2000 to 2020

It can be observed that the number of research articles in the domain of CRM is steadily increasing, and specifically, there is a noticeable growth in the number of research articles post-2009. Further, there is a cyclical trend with respect to the number of publications in the domain of DCRM. Nevertheless, recent years have shown an increasing trend. Since the data was collected in June 2020, the number of publications on CRM and DCRM is not entirely captured, which explains the lower number of publications compared to the previous years. Next, we identified five leading journals in the area of CRM and DCRM in terms of the number of articles, as shown in Fig.  3 .

Leading Journals in terms of number of articles from 2000 to 2020

In the CRM area, ‘Industrial Marketing Management’ leads the pack with more than 250 publications, followed by ‘Journal of Business and Industrial Marketing’. The difference between these two journals in terms of the number of articles is noticeable, making ‘Industrial Marketing Management’ a clear leader. There is no discernible difference among the journals in the third, fourth and fifth positions. It can be deduced from the nature of the journals that CRM articles appear primarily in the Marketing domain, followed by Operations. In the area of DCRM, the journal ‘Expert Systems with Applications’ holds significant share of the articles with a huge difference in terms of the number of articles between the leader and the immediate follower. Further, it can be observed that the leading journals in the domain of DCRM belong to the domain of Operations Research/Industrial Engineering and Systems. This can be attributed to the nature of the tools and techniques coming under DCRM. Next, we identified the leading universities/institutes in terms of research articles in the area of CRM and DCRM, as shown in Fig.  4 .

Leading Universities/Institutes in terms of number of articles from 2000 to 2020

It can be observed that Hong Kong Polytechnic University is the leading university in the case of both CRM and DCRM research output. In the case of CRM research output, the difference between ‘Hong Kong Poly Technic University and ‘Georgia State University’ is minimal. However, in the case of DCRM, the difference between ‘Hong Kong Poly Technic University’ and ‘Aletheia University’ which comes in the second position, is noticeable. Next, we identify the leading authors in the domain of CRM and DCRM, respectively. First, we present ten leading authors in the field of CRM and their output over the years in Fig.  5 .

Leading Authors in CRM in terms of number of articles and their year wise output from 2000 to 2020

In Fig.  5 , the starting point of the purple line indicates the year in which the author has started publishing in the CRM area of research. The ending point of the line can be interpreted in two ways (i) It is the year in which the author has published finally in the area of CRM (ii) It is the year up to which the data has been collected. In the former interpretation, the line can be continued with a break if the authors start publishing articles in the CRM area further. In the latter interpretation, the line can be continued without any break if the authors continue their contribution in the form of research articles.

Prof. V. Kumar, followed by Prof. D. Van den Poel are the leading authors in the domain of CRM research. It can be observed that some researchers produce relatively a smaller number of articles but keep the consistency over a period. In contrast, some researchers produce a more significant number of articles in a short duration and switch to other research domains. The patterns observed in the case of DCRM are random in that there is relatively minimal overlapping among the lines representing the researchers’ seen in Fig.  6 .

Leading Authors in DCRM in terms of number of articles and their year wise output

Prof. D. Van den Poel is the leading author in terms of his contribution to research articles in the area of DCRM. The emergence of new researchers in this domain, like Prof. WY Chiang, indicates the dynamics of this domain.

3.2 Scientometric Analysis

Scientometric analysis was carried out in VOSviewer. The same settings were applied for all the techniques. For instance, the frequency of keyword occurrence was set four times, and sixty percentage of the most relevant keywords were included in the network maps.

3.2.1 Leading journals in the field of CRM and DCRM research in terms of citations

It is important to identify the leading journals in a research domain as it can benefit researchers to obtain the most relevant and latest content and to publish their works further. It can also help the editors reconsider and refine their policies to improve the journal's citation performance (Hosseini et al. 2018 ). Leading journals can be identified by employing the technique of journal co-citation analysis. This technique quantifies and visualises the number of citations imported and exported between a pair of journals (Hsiao and Yang, 2011 ). To identify the leading journals in CRM domain, journal co-citation analysis has been carried out, and the results are shown in Fig.  7 . It shows the leading journals in terms of the number of citations received by research articles focusing on CRM. The filtering criteria set for identifying the journal is that at least five research articles should have been published having the keyword CRM and a minimum number of citations is 10.

Leading journals in CRM based on the number of citations from 2000 to 2020

The node’s size in the network represents the number of citations received by a particular journal for the articles published in it. It can be seen that ‘Journal of Marketing’, published by Sage publishers, is the leading journal in which a maximum number of citations are received for CRM-based research articles during 2000–2020. The journal ‘Industrial Marketing Management’ published by Elsevier publishers comes in the second position and Journal of Operations Management, published by Wiley publishers, comes in the third position with respect to citations. In the network, the line between the two nodes demonstrates the academic link between the two nodes and indicates the academic link between two journals (Guo et al. 2019 ). Shorter the line stronger the relationship. It is obvious from Fig.  7 that ‘Journal of Marketing’ and ‘Supply Chain Management-An International Journal’ are located at a noticeable distance on account of their difference in focus whereas ‘Journal of Marketing’ and ‘Journal of the academy of marketing science’ are located together. The number of times a research article is cited as a reference in another research article indicates its scientific impact, which is indicated by the node's size. Nevertheless, Fig.  7 does not convey the difference in citations among the leading journals. Therefore, we report Table 1 , in which the number of articles, number of citations, average citations, links, total link strength, and average normal citation are presented. The data for the above-mentioned statistics in the case of 15 leading journals have been extracted to have a deeper understanding of the citations’ differential.

From Table 1 , it can be deduced that ‘Journal of Marketing’ is the leading journal in the domain of CRM research based on a number of citations, followed by the journal ‘Industrial Marketing Management. ‘Average citations’ is the ratio of ‘total citations’ to ‘the number of articles’. It can be observed that ‘Journal of Marketing’ is also leading in terms of ‘average citations’ followed by ‘Journal of Operations Management’. Thus, it can be stated that the ranking of the journal varies with respect to the ranking criterion such as ‘total citations’ or ‘average citations.’ In Table 1 , link means the connection a journal has with other journals primarily based on the subject domain. For instance, the links for ‘Journal of Marketing’ is 121 i.e., ‘Journal of Marketing’ is linked to 121 other journals forming a cluster which can be verified from Fig.  4 . Link strength as a positive numerical value that indicates the number of citations received by the articles belonging to a specific journal by other journals in the cluster. Higher the link strength thicker will be the lines in the network diagram. This can be verified by the thickness of the lines emerging from the node representing ‘Journal of Marketing’ and the journal ‘Industrial Marketing Management’, which are the two leading journals in terms of link strength.

Next, we present the journal co-citation analysis for obtaining leading journals regarding the number of citations in DCRM in Fig.  8 . The filtering criteria used for identifying the journal is that at least two research articles should have been published having the keyword CRM and minimum number of citations is 5. It can be observed from Fig.  8 that ‘Expert Systems with Applications’ published by Elsevier publishers, is the leading journal in the field of DCRM. ‘European Journal of Operations Research’ published by Elsevier publishers appears in the second position. Owing to the lower number of research articles in DCRM compared to the number of research articles under the CRM area, clusters are dispersed with a more significant average distance among the nodes.

Leading journals in DCRM based on the number of citations from 2000 to 2020

To obtain a deeper understanding, we present Table 2 , which ranks the leading journals in the area of DCRM in terms of the number of citations. The difference between the leading journal, i.e., ‘Expert Systems with applications’ and the second leading journal, i.e., ‘European Journal of Operational Research’, is noticeable not only in terms of the number of citations but also in terms of the number of articles, links and link strength. Though the ‘Average Citations’ is comparable to ‘Expert Systems with applications’ and ‘Information & management, the small number of research articles in the latter makes the former a clear leader in this field.

3.2.2 Contemporary and important topics of CRM and DCRM research

In this section, we employ the overlay visualisation technique (OVT) to identify contemporary (latest) and important topics in the field of CRM and DCRM. The general idea behind overlay visualization technique is to make a map based on relations of a type of element (e.g., journal, author) and then overlaying on each element information such as number of articles, growth etc. (Rafols et al. 2010 ). OVT is a network visualization technique with different colours assigned for item under consideration with the colour indicating the score of the item. The importance of the topic is operationalized as those topics which have appeared in highly cited journals.

Contemporary Topics of CRM and DCRM Research: A time-based OVT is used to identify the contemporary topics. The complete bibliometric data extracted has been employed as an input to this technique. The keywords that have occurred at least 5 times have been included by the algorithm. By analysing the clusters that are formed out of recurring terms, a set of topics can be identified. The year 2017, is set as the average mid-point at 0.0 of the scale (green). Contemporary topics under the area of CRM are visualized using colours based on the colour bar as shown in Fig.  9 . Latest topics were illustrated using colour ranging from yellow (relatively latest) to red (latest), while older topics were illustrated using green (relatively old) to Blue (oldest) based on a normalized scale of -1 to 1. In other words, terms that were used more towards 2020 are shown in red colour whereas the terms that were used more towards 2000 are shown in blue colour.

Contemporary topics under the area of CRM

From the data set emerged from the citation based-OVT, it was observed that some of the most occurred terms in the highly cited journals related to CRM are strategic integration, firm performance, organizational performance, competitive advantage, value creation, innovation, product development, loyalty, customer satisfaction, trust, service quality, product quality, manufacturing integration and supply chain management. It is interesting to observe the nature of the terms that emerged as most occurred terms in the CRM literature. By examining the terms, it can be understood that there are terms related to customers perception and there are terms related to operations. Therefore, it can be deduced that CRM is critical link between the operations and the desirable customer perception outcomes. Further, the terms such as firm performance, organizational performance and competitive advantage indicates the significance of CRM in organizations.

To identify the contemporary topics under DCRM, we carried out OVT in the respective data set and the result is shown in Fig.  10 .

Contemporary topics in DCRM

The results from the time-based OVT shows that the contemporary topics in the recent literature of DCRM are data mining, text mining, RFM model, churn prediction, segmentation, and satisfaction. These terms throw light on the recent data analytics tools and techniques applied for the purpose of segmenting the customers such as neural networks, text mining and association rule mining. It can also be observed that the techniques are applied in the areas of churn prediction, customer satisfaction and customer value. Thus, it can be deduced that recent data analytics techniques have helped to improve the effectiveness of CRM with accurate segmentation of customers. The presence of the node ‘e-commerce’ indicates the applicability of data mining techniques in the e-commerce industry.

Important Topics under CRM and DCRM Research: In this section, we report important topics in CRM and DCRM using citation-based OVT. The ‘importance’ is defined by the number of occurrences in highly cited research papers (Chandra, 2018 ). Under citation-based OVT, the topics are matched with the citation score of the research papers where the topics have appeared. The data was normalized by dividing the difference between each research publication’s number of citations and average number of citations with the standard deviation of citations. Thus, a score of 0 means that the number of citations obtained by a research publication is equal to the average number of citations received by all publications that appeared in the same year. The normalized citation scores were then plotted with red colour indicating topics high average citation impact and blue colour indicating topics with low average citation impact, In Fig.  8 , we plot the important topics under CRM.

From Fig.  11 , the following topics have been identified as the important topics under CRM in the order of their average citation impact. (i) strategic integration (ii) PLS-SEM (iii) store loyalty (iv) sustained competitive advantage (v) supply chain collaboration (vi) information integration (vii) confirmatiory factor analysis (viii) survey research (ix) marketing strategy (x) Manufacturing integration. The topics can be classified into application areas or outcomes and the tools, techniques and methodology employed in the research articles. From the recurrence of the term ‘integration’, it can be stated that CRM plays a crucial role in the integration of functions in an organization as corroborated in the theory. It can also be observed that the research articles in the realm of CRM have primarily employed ‘Survey Research’ and analyzed the results using multi-variate statistical techniques such as Confirmatory Factor Analysis and Structural Equation Modelling. Next, we present the important topics under DCRM in Fig.  12 .

Important topics in CRM

Important topics in DCRM

From Fig.  12 , it can be deduced that the important topics i.e., the topics with high average citation impact in the order of importance are (i) customer segmentation (ii) knowledge management (iii) word of mouth (iv) association rule mining (v) customer life time value (vi) text mining (vii) data mining (viii) classifiers (ix) clustering (x) support vector machines. It can be deduced that the important topics come under three categories (i) Techniques (ii) Application (iii) Industry. The presence of terms such as neural networks and classification shows the important techniques that are applied to segment the customers or to extract inputs from the customers for the new product development. The increased availability and accessibility of customer sentiments from social media platforms justifies the term ‘social media’ under high average citation impact. This also indicates the emerging trend of social CRM (Dewnarain et al. 2021 ).

3.2.3 Leading countries of CRM and DCRM research

In this section, we report the leading coutries and their network of CRM and DCRM research area in terms of the citations received by research articles published by the universities or institutes belonging to a country. In Fig.  13 , leading countries in the CRM research area has been presented. Inclusion criteria of the country is that only those countries are included from which a minimum of five documents are published. We have not set a lower limit for number of citations.

Leading countries and their network in CRM

It can be observed that USA leads the list of countries. Nevertheless, the nodes representing the citations are overlapped leading to difficulty in making out the node size and representing country. Therefore, we extracted the data in tabular format and a representative sample of 10 leading countries and the other characteristics of the network diagram are shown in Table 3 .

It can de deduced from Fig.  13 and Table 3 that England is in second position in the list of leading countries in terms of the number of citations. The difference in number of citations between USA and England is quite large that makes USA a clear leader in this domain. The difference in the number of citations and the number of articles is somewhat proportional. It is interesting to notice that the countries Germany, Peoples’ Republic of China and Australia are comparable in terms of number of citations though there are noticeably greater number of research articles published by universities from Peoples’ Republic of China. Thus, it can be deduced that the number of published research articles is not always a predictor of citation impact. Next, we examine the leading countries based on number of citations in DCRM research field. To obtain deeper insights, we report both the network diagram as well as the Table as follows.

From Fig.  14 and Table 4 , it can be observed that USA leads the list in terms of number of citations followed by Peoples’ Republic of China. Nevertheless, both countries are at par with respect to the number of research articles published. It is interesting to notice that the countries South Korea and Taiwan, which were in the 8th and 9th position respectively in terms of number of citations under CRM research, are in 3rd and 4th position respectively based on the number of citations in DCRM research area. Further, the 18 articles published from South Korea have received 766 citations whereas 43 articles published from Taiwan have received only 730 citations indicating the expertise of South Korea in DCRM research domain. This is corroborated by the higher Average Normal Citation score of South Korea compared to USA and Peoples’ Republic of China. It can be further observed that England is also doing very well in terms of the metrics and is the leader based on Average Normal Citation score.

Leading countries and their network in DCRM

3.2.4 Leading universities/institutions of CRM and DCRM research

In this section, we report the leading universities/institutions and their network of CRM and DCRM research area in terms of the citations received by research articles published by the universities/institutions. Co-citation of universities/institutions occurs when research articles from two universities/institutions reference research articles from a third common university/institution (Mas-Tur et al. 2021 ). In Fig.  15 , the co-citation nework of leading universties/institutes in the CRM research area has been presented (Fig. 16 ). Inclusion criteria of the University/Institute is that only those institutes/universities are included which have published a minimum of five documents that have received a minimum of five citations.

Leading universities/institutions and their network in CRM

It can be observed that University of Maryland leads the list of universities/institutions. Nevertheless, the nodes representing the citations are overlapped making it difficult to assess the node size and representing country. Therefore, we extracted the data in tabular format and a representative sample of ten leading universities and the other characteristics of the network diagram are shown in Table 5 .

It can be observed from Table 5 that there is a noticeable difference between University of Maryland and Texas University which comes in the second position making University of Maryland a clear leader in the field of CRM research. Newcastle University takes the leading position in terms of average normalized citations and Nova University Lisbon takes the leading position in terms of average citations. It is interesting to note that the leading authors in the CRM research area are not from the leading universities. This indicates that in the leading universities a team of researchers are engaged in advancing the literature of CRM. Next, we examine the leading universities/institutions based on number of citations in DCRM research field. The inclusion criteria are minimum of a publication from the university/institution without any lower limit on number of citations. To obtain deeper insights, we report both the network diagram as well as the Table as follows (Fig. 16 , Table 6 ).

Leading universities/institutions and their network in DCRM

It can be observed that South Korean universities are in the leading position when it comes to DCRM with Korea Advanced Institute Science and Technology in the leading position and Korea University in the second position. This is evidence of the advancement of South Korea in the digital space and the adoption of advanced data-mining techniques for CRM. This trend is complemented by the increased number of South Korean scholars publishing in English language. This finding supports the finding obtained under leading countries under DCRM and validates the supremacy of South Korea in terms of Average Normal Citation Score.

3.2.5 Conceptual structure map of CRM and DCRM

Conceptual Structure Map has been generated with the help of the function “conceptualStructure” in Bibliometrix package. The “conceptualStructure” function in executes multiple correspondence analysis (MCA) of the keywords (Meghana et al. 2021 ) first for obtaining the conceptual structure of the field and then carry out a K-means clustering (Aria and Cuccurullo, 2017 ) to group the keywords that express common concepts. MCA is an extension of Correspondence Analysis (CA) to tackle a multiway matrix. In this study, the multiway matrix is the co-word matrix formed by the keywords extracted from the research articles.

In other words, the conceptual structure map is the output of co-word analysis (Aria and Cuccurullo, 2017 ) i.e., word co-occurrence network.

The general formula for a co-word network is \(B_{coc} = A^{\prime} \times A\) .

Where \(B_{coc}\) is a non-negative symmetric matrix representing keyword co-occurrence.

And \(A\) is a \(Document \times Keyword\) matrix.

Here the MCA is applied to the matrix \(A\) ( \(Document \times Keyword\) ) to plot the keywords on a 2-D map. Further, K-means clustering algorithm based on hierarchical clustering method is applied to position the keywords in a 2D map (Cuccurullo et al. 2016 ; Xie et al. 2020 ). Under the hierarchical clustering, each cluster of keywords are treated as a class, which will be merged with other cluster to form a larger cluster based on the degree of similarity. This process is repeated until the optimal number of clusters are formed.

In the Bibliometrix package the following settings are used to generate the conceptual structure map.

There are three fields in the “conceptualStructure” function viz. (i) method (ii) clust and (iii) k.max. Under the field method ‘MCA’ has been selected to perform multiple correspondence analysis from the three options i.e., correspondence analysis, multiple correspondence and multidimensional analysis. Further, in the field for selecting the number of clusters, we have selected the option ‘AUTO’ leaving the package to select the optimal number of clusters rather than specifying a number. Furthermore, we have a selected the upper limit for the number of clusters by setting k.max = 5 which was the highest possible number of clusters as per the options given by the package.

We obtained three distinct clusters for CRM research area as shown in Fig.  17 . In the blue-cluster, we can observe terms such as customer satisfaction, service quality, profitability, and loyalty indicating the outcomes of CRM implementation. Within a cluster, proximity among the keywords corresponds to shared substance i.e., closer keywords were treated together in a large proportion of articles. Thus, in the blue-cluster, we can see a sub-segment in which the keywords customer satisfaction, quality and profitability are very close. Therefore, it can be interpreted that the outcomes of CRM implementation are better customer satisfaction and quality leading to improved profitability. In the red cluster, which is the largest, we can notice terms such as firm performance, innovation and information technology are forming a very logical sub-segment. Another logical sub-segment comprises of the terms such as technology, customer, performance, and impact.

Conceptual structure map of CRM research area

The conceptual structure map of the DCRM is presented in Fig.  18 . As can be observed, there are three major clusters. The blue-cluster contains terms such as feature selection, segmentation data mining techniques, and RFM (random forest model) model indicating classification techniques used in customer segmentation applications in data mining-based CRM. It is interesting to note the proximity of complementary terms retention and defection in the green-cluster. The topics such as churn prediction, classification, selection, and models form another logical sub-cluster within the green cluster. In the large and dense red-cluster, one can observe several sub-clusters. For instance, the sub-cluster comprising of keywords prediction, neural networks and optimization is logical in terms of their dependence. Similarly, another logical sub-cluster that can be observed comprises of terms such as model, big-data, association rules and patterns.

Conceptual structure map of DCRM research area

4 Discussions and future research directions

The three-phase sequential methodology of bibliometric search, descriptive analysis, and scientometric analysis adopted in this study yields several insights on the evolution of CRM into DCRM. Further, the methodological contribution of the study includes the application of novel techniques such as citation-based OVT and time-based OVT in addition to the development of conceptual structure map.

Compared to the extant research in this field, there are several interesting findings that have emerged in this study. For instance, it was observed that the focus of CRM research varies significantly with time and technological advancement though the primary objective of any CRM system is to improve customer loyalty and to thereby enhance the repeat purchase. Ngai ( 2005 ) considered a period from 1992–2005 for conducting systematic literature review on CRM and reported that the CRM literature focus on information technology (IT) or information systems (IS) related aspects. This observation of Ngai ( 2005 ) can be corroborated with the rapid developments in technology and internet in the period considered in his study. The finding of the study, conducted by Sota et al. ( 2018 ) by considering a period from 2007 to 2016, is that the focus of CRM research is customer loyalty. By combining the conclusions of Ngai ( 2005 ) and Sota et al. ( 2018 ), it can be inferred that CRM research never focuses wholly on either IT/IS or customer loyalty. It has been and will always be a mix of these two aspects.

Though customer loyalty is a fundamental concept, the way it is being measured is subjected to change. The introduction and wide acceptance of metrics like Net Promoter Score is evidence for the dynamic nature of customer loyalty assessment. On the other hand, with technological advancement, the way the customer data is acquired and analysed, is undergoing phenomenal changes. The frequent introduction of effective algorithms and techniques for handling huge volume and variety of data is giving new dimensions for CRM from the IT/IS perspective. From the rate of change of technological advances, it can be deduced that the evolution of CRM as an information system is much faster than the evolution of CRM with respect to the changes in customer loyalty metrics. This is the rationale behind exploring the domain of DCRM in this study and appearance of terms such as text mining, association rule mining, neural networks, big data, classification, and prediction shows the directions for future research in terms of their applications to different industries.

Text mining is the process of transforming unstructured text into a structured format to identify meaningful patterns to derive new insights. Text mining tools like sentiment analysis can be applied to the huge chunk of text data that are generated in social media and e-commerce platforms for understanding the customer emotions and devise appropriate CRM strategies. Customer centre call records and customer grievance emails are valuable sources of text content that can be mined for actionable insights. Neural networks process training data by mimicking the interconnectivity of the human brain through layers of nodes. The capabilities of convolutional neural network (CNN) for image recognition and natural language processing have several applications in understanding the customer preferences deeply. AI based computer vision has already been integrated into CRM systems for improved customer service and has immense potential for future applications in service sector. Another term appeared in the conceptual structure map i.e., association rule mining (ARM) a rule-based method is used for finding relationships between variables in a dataset. ARM can be applied to large scale databases of sales transactions, either generated from a point of sale or from an e-commerce platform, to carry out techniques like market basket analysis for effective product recommendations.

Broadly, the terms appeared in the citation analysis and conceptual structure map can be related to machine learning (ML) and AI tools and techniques. This observation is supported by the recently published articles in AI integrated CRM systems (Chatterjee et al. 2020 , 2021 ) and ML for CRM (Singh et al. 2020 ; Chen et al. 2021). It is reported by Singh et al. ( 2020 ) that the utilization of supervised learning techniques for CRM is 48.48% whereas the utilization of unsupervised techniques for CRM is only 15.15% and there is a shift from ML to deep learning. This finding shows shift from ML to AI and justifies the appearance of AI integrated CRM in the recent literature. These observations are complemented by the emergence of “Expert Systems with Applications” as the leading journal in terms of citations. The focus of the journal on expert and intelligent systems technology is in alignment with recent advancement in the ML and AI area in terms of their applications.

Further, the contemporary topics identified from time-based overlay visualization map of CRM are service experience, customer engagement, hospitality, brand experience and customer journey. This shows the significance of CRM in the service sector and the shift towards customer experience. This finding is a strong recommendation for the managers in the service industry to take appropriate measures to enhance the customer experience. This is evidence for market transformation into areas such as customer experience management and sustainability especially in the service sector. The contemporary topics in the DCRM area such as churn prediction, association rules, text mining, business intelligence, RFM model and social media throws light on the techniques that are currently applied to process customer data to support managerial decision making.

The study contributes to the literature by presenting the scholarly landscape of CRM and DCRM and thereby provides a deeper understanding on the development and state of the art of CRM and DCRM. Some of the interesting findings derived using descriptive and scientometric analysis techniques can help the research scholars to identify and pursue most relevant and promising topics under CRM and DCRM. It can be stated that the outcomes of this scientometric study have significant implications for evaluation and understanding of scientific output CRM and DCRM.

The study has major implications for the practising managers. For instance, it can be deduced from the results of scientometric analysis that application of data mining techniques to deploy CRM has positive impact on the firm performance. This finding reinforces the significance of both CRM and data mining-based CRM and the summary of the specific techniques reported under contemporary topics, important topics, and conceptual structure map such as RFM model, association rule mining, text mining and neural networks reinforces the managers on the requirement for understanding and adopting the novel techniques for improving the retention and loyalty of their customers. Further, the application areas identified in this study such as prediction, churn management, segmentation, and classification throw light on the areas where managers have to work for optimizing the customer lifetime value.

CRM has evolved its way from its origin as a simple mechanism to manage contacts of customers to a level where it enables prediction of what the customers are going to buy to trigger predictive shipping in which products are shipped to customer location before they place the order. The integration of predictive analytics into CRM is a promising application area for practicing managers and research area for scholars in this domain. The penetration of cloud-based CRM systems makes them vulnerable to cyber-attacks. This signals the need for research studies addressing the cyber security of cloud-based CRM systems and issues such as data theft and ransomware attacks. Further, the emerging concepts like control tower in supply chain, which improves the supply chain visibility based on real time data, treats customer as an integral part of the digital supply chain network. Such radical changes and resulting generation of huge volumes of customer information mandate the application of advanced analytics to enable managerial decision making on a real-time basis.

The future CRM solutions will be based on single source of truth (SSOT) that is the practice of aggregating the customer data from multiple locations to a single a single location to enable a system level understanding of customer sentiments. For processing such massive volume of data, ML and AI technologies are needed and to store and retrieve customer data, cloud solutions are necessary. For enhancing the speed of decision making, the synthesized data should be accessible through mobile devices. To summarize it can be stated that the future of CRM will be focusing on customer experience, and it will be facilitated by cloud-based, AI-optimized platforms that can be accessed via mobile devices.

5 Conclusions and limitations

This study explores and compares the landscape of scholarly works that have emerged in the area of CRM and DCRM. The descriptive analysis gave an overview on the growth of the number of research articles, the leading journals, leading universities or institutions and the leading authors in the domain of CRM and DCRM. The scientometric analysis identified the leading journals, leading countries, contemporary topics, important topics, and leading universities or institutions based on the citation analysis. Finally, the distinct clusters in the field of CRM and DCRM are presented under the conceptual structure map. This study provides several recommendations for the researchers in the field of CRM and insights for practicing managers.

One limitation of the study is that it has only considered research articles as the input document for the scientometric analysis. Other documents such as conference proceedings, conference papers, reports, books, and surveys have been ignored that might have addressed the CRM and DCRM. Another limitation is the choice of only one database i.e., Web of Science for the study for the purpose of obtaining uniform references. Since the data has been extracted from only one database, there is a possibility that some of the research articles indexed in another database such as Scopus, Google Scholar and PubMed are excluded. This work can be extended further by carrying out scientometric analysis techniques such as document co-citation network analysis, co-authorship network analysis and keyword co-occurrence network analysis for obtaining deeper insights in the field of CRM and DCRM.

Ahmad, A.K., Jafar, A., Aljoumaa, K.: Customer churn prediction in telecom using machine learning in big data platform. J. Big Data 6 (1), 1–24 (2019). https://doi.org/10.1186/s40537-019-0191-6

Article   Google Scholar  

Anshari, M., Almunawar, M.N., Lim, S.A., Al-Mudimigh, A.: Customer relationship management and big data enabled: personalization & customization of services. Appl. Comput. Inf. 15 (2), 94–101 (2019). https://doi.org/10.1016/j.aci.2018.05.004

Aria, M., Cuccurullo, C.: bibliometrix: An R-tool for comprehensive science mapping analysis. J. Informet. 11 (4), 959–975 (2017). https://doi.org/10.1016/j.joi.2017.08.007

Azadeh, A., Foroozan, H., Ashjari, B., Motevali Haghighi, S., Yazdanparast, R., Saberi, M., Torki Nejad, M.: Performance assessment and optimisation of a large information system by combined customer relationship management and resilience engineering: a mathematical programming approach. Enterp. Inf. Syst. 11 (9), 1401–1415 (2017). https://doi.org/10.1080/17517575.2016.1251618

Baashar, Y., Alhussian, H., Patel, A., Alkawsi, G., Alzahrani, A.I., Alfarraj, O., Hayder, G.: Customer relationship management systems (CRMS) in the healthcare environment: a systematic literature review. Comput. Stand. Interfaces 71 , 103442 (2020). https://doi.org/10.1016/j.csi.2020.103442

Bangsa, A.B., Schlegelmilch, B.B.: Linking sustainable product attributes and consumer decision-making: Insights from a systematic review. J. Clean. Prod. 245 , 118902 (2020). https://doi.org/10.1016/j.jclepro.2019.118902

Biswas, B., Sengupta, P., Chatterjee, D.: Examining the determinants of the count of customer reviews in peer-to-peer home-sharing platforms using clustering and count regression techniques. Decis. Support Syst. 135 , 113324 (2020). https://doi.org/10.1016/j.dss.2020.113324

Capuano, N., Greco, L., Ritrovato, P., Vento, M.: Sentiment analysis for customer relationship management: an incremental learning approach. Appl. Intell. 51 (6), 3339–3352 (2021). https://doi.org/10.1007/s10489-020-01984-x

Carpaneto, E., Chicco, G., Napoli, R., Scutariu, M.: Electricity customer classification using frequency–domain load pattern data. Int. J. Electr. Power Energy Syst. 28 (1), 13–20 (2006). https://doi.org/10.1016/j.ijepes.2005.08.017

Chandra, Y.: Mapping the evolution of entrepreneurship as a field of research (1990–2013): a scientometric analysis. PLoS ONE 13 (1), e0190228 (2018). https://doi.org/10.1371/journal.pone.0190228

Chatterjee, D., Ghosh, S., Brady, P.R., Kapadia, S.J., Miller, A.L., Nissanke, S., Pannarale, F.: A machine learning-based source property inference for compact binary mergers. Astrophys. J. 896 (1), 54 (2020)

Chatterjee, S., Goyal, D., Prakash, A., Sharma, J.: Exploring healthcare/health-product ecommerce satisfaction: a text mining and machine learning application. J. Bus. Res. 131 , 815–825 (2021). https://doi.org/10.1016/j.jbusres.2020.10.043

Cuccurullo, C., Aria, M., Sarto, F.: Foundations and trends in performance management. A twenty-five years bibliometric analysis in business and public administration domains. Scientometrics 108 (2), 595–611 (2016). https://doi.org/10.1007/s11192-016-1948-8

Darko, A., Chan, A.P., Huo, X., Owusu-Manu, D.G.: A scientometric analysis and visualization of global green building research. Build. Environ. 149 , 501–511 (2019). https://doi.org/10.1016/j.buildenv.2018.12.059

Deb SK, Jain R, Deb V (2018) Artificial intelligence―creating automated insights for customer relationship management. In: 2018 8th International Conference on Cloud Computing, Data Science & Engineering (Confluence), pp 758–764. IEEE. https://doi.org/10.1109/CONFLUENCE.2018.8442900

De Caigny, A., Coussement, K., De Bock, K.W.: A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees. Eur. J. Oper. Res. 269 (2), 760–772 (2018). https://doi.org/10.1016/j.ejor.2018.02.009

Dewnarain, S., Ramkissoon, H., Mavondo, F.: Social customer relationship management: a customer perspective. J. Hosp. Market. Manag. 30 (6), 673–698 (2021). https://doi.org/10.1080/19368623.2021.1884162

Fang, Y.: Visualizing the structure and the evolving of digital medicine: a scientometrics review. Scientometrics 105 (1), 5–21 (2015). https://doi.org/10.1007/s11192-015-1696-1

Guo, Y.M., Huang, Z.L., Guo, J., Li, H., Guo, X.R., Nkeli, M.J.: Bibliometric analysis on smart cities research. Sustainability 11 (13), 3606 (2019). https://doi.org/10.3390/su11133606

Herman, L.E., Sulhaini, S., Farida, N.: Electronic customer relationship management and company performance: exploring the product innovativeness development. J. Relatsh. Mark. 20 (1), 1–19 (2021). https://doi.org/10.1080/15332667.2019.1688600

Hosseini, S.M.S., Maleki, A., Gholamian, M.R.: Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty. Expert Syst. Appl. 37 (7), 5259–5264 (2010). https://doi.org/10.1016/j.eswa.2009.12.070

Hosseini, M.R., Maghrebi, M., Akbarnezhad, A., Martek, I., Arashpour, M.: Analysis of citation networks in building information modeling research. J. Constr. Eng. Manag. 144 (8), 04018064 (2018). https://doi.org/10.1061/(asce)co.1943-7862.0001492

Hsiao, C.H., Yang, C.: "The intellectual development of the technology acceptance model A co-citation analysis. Int. J. Inf. Manag. 31 (2), 128–136 (2011). https://doi.org/10.1016/j.ijinfomgt.2010.07.003

Keramati, A., Jafari-Marandi, R., Aliannejadi, M., Ahmadian, I., Mozaffari, M., Abbasi, U.: Improved churn prediction in telecommunication industry using data mining techniques. Appl. Soft Comput. 24 , 994–1012 (2014). https://doi.org/10.1016/j.asoc.2014.08.041

Khan, R.U., Salamzadeh, Y., Iqbal, Q., Yang, S.: The impact of customer relationship management and company reputation on customer loyalty: the mediating role of customer satisfaction. J. Relatsh. Mark. 21 (1), 1–26 (2022). https://doi.org/10.1080/15332667.2020.1840904

Larsen, P., Von Ins, M.: The rate of growth in scientific publication and the decline in coverage provided by Science Citation Index. Scientometrics 84 (3), 575–603 (2010). https://doi.org/10.1007/s11192-010-0202-z

Le Phan, H.L., Tortora, C.: K-means clustering on multiple correspondence analysis coordinates. Inst. Inf. Syst. Mark. (2019). https://doi.org/10.5445/KSP/1000085952/05

Ledro, C., Nosella, A., Vinelli, A.: How to assess organizational and strategic impacts of customer relationship management: a multi-perspective performance evaluation method. Expert Syst. Appl. 199 , 117024 (2022). https://doi.org/10.1016/j.eswa.2022.117024

Lee, E.B., Kim, J., Lee, S.G.: Predicting customer churn in mobile industry using data mining technology. Ind. Manag. Data Syst. 117 (1), 90–109 (2017). https://doi.org/10.1108/IMDS-12-2015-0509

Lessmann, S., Vob, S.: A reference model for customer-centric data mining with support vector machines. Eur. J. Oper. Res. 199 (2), 520–530 (2009). https://doi.org/10.1016/j.ejor.2008.12.017

Li, S.T., Shue, L.Y., Lee, S.F.: Enabling customer relationship management in ISP services through mining usage patterns. Expert Syst. Appl. 30 (4), 621–632 (2006). https://doi.org/10.1016/j.eswa.2005.07.016

Liao, S.H., Chen, Y.J., Deng, M.Y.: Mining customer knowledge for tourism new product development and customer relationship management. Expert Syst. Appl. 37 (6), 4212–4223 (2010)

Liao, S.H., Hsieh, C.L., Huang, S.P.: Mining product maps for new product development. Expert Syst. Appl. 34 (1), 50–62 (2008). https://doi.org/10.1016/j.eswa.2006.08.027

Liou, J.J.: A novel decision rules approach for customer relationship management of the airline market. Expert Syst. Appl. 36 (3), 4374–4381 (2009). https://doi.org/10.1016/j.eswa.2008.05.002

Mahdavi, I., Movahednejad, M., Adbesh, F.: Designing customer-oriented catalogs in e-CRM using an effective self-adaptive genetic algorithm. Expert Syst. Appl. 38 (1), 631–639 (2011). https://doi.org/10.1016/j.eswa.2010.07.013

Martínez, A., Schmuck, C., Pereverzyev, S., Jr., Pirker, C., Haltmeier, M.: A machine learning framework for customer purchase prediction in the non-contractual setting. Eur. J. Oper. Res. 281 (3), 588–596 (2020). https://doi.org/10.1016/j.ejor.2018.04.034

Mas-Tur, A., Roig-Tierno, N., Sarin, S., Haon, C., Sego, T., Belkhouja, M., Merigó, J.M.: Co-citation, bibliographic coupling and leading authors, institutions and countries in the 50 years of Technological Forecasting and Social Change. Technol. Forecast. Soc. Chang. 165 , 120487 (2021). https://doi.org/10.1016/j.techfore.2020.120487

Meghana, B.P., Mamdapur, G.M.N., Sahoo, S.: (2021) Twenty-five Years Study (1995–2019) of Food and Bioproducts Processing: An Overview of Research Trends. Library Philosophy and Practice, 2021. http://eprints.iisc.ac.in/id/eprint/69141

Mingers, J., Leydesdorff, L.: A review of theory and practice in scientometrics. Eur. J. Oper. Res. 246 (1), 1–19 (2015). https://doi.org/10.1016/j.ejor.2015.04.002

Michels, C., Schmoch, U.: The growth of science and database coverage. Scientometrics 93 (3), 831–846 (2012). https://doi.org/10.1007/s11192-012-0732-7

Moed, H.F.: Measuring contextual citation impact of scientific journals. J. Informet. 4 (3), 265–277 (2010). https://doi.org/10.1016/j.joi.2010.01.002

Ngai, E.W.: Customer relationship management research (1992–2002): An academic literature review and classification. Mark. Intell. Plan. (2005). https://doi.org/10.1108/02634500510624147

Ngai, E.W., Xiu, L., Chau, D.C.: Application of data mining techniques in customer relationship management: a literature review and classification. Expert Syst. Appl. 36 (2), 2592–2602 (2009). https://doi.org/10.1016/j.eswa.2008.02.021

Nguyen, B., Jaber, F., Simkin, L.: A systematic review of the dark side of CRM: the need for a new research agenda. J. Strateg. Mark. (2020). https://doi.org/10.1080/0965254X.2019.1642939

Prieto-Sandoval, V., Alfaro, J.A., Mejía-Villa, A., Ormazabal, M.: ECO-labels as a multidimensional research topic: Trends and opportunities. J. Clean. Prod. 135 , 806–818 (2016). https://doi.org/10.1016/j.jclepro.2016.06.167

Rafols, I., Porter, A.L., Leydesdorff, L.: Science overlay maps: a new tool for research policy and library management. J. Am. Soc. Inform. Sci. Technol. 61 (9), 1871–1887 (2010). https://doi.org/10.1002/asi.21368

Rawat, K.S., Sood, S.K.: Knowledge mapping of computer applications in education using CiteSpace. Comput. Appl. Eng. Educ. (2021). https://doi.org/10.1002/cae.22388

Rodrigues, S.P., Van Eck, N.J., Waltman, L., Jansen, F.W.: Mapping patient safety: a large-scale literature review using bibliometric visualisation techniques. BMJ Open (2014). https://doi.org/10.1136/bmjopen-2013-004468

Rubbo, P., Pilatti, L.A., Picinin, C.T.: Citation of retracted articles in engineering: a study of the Web of Science database. Ethics Behav. 29 (8), 661–679 (2019). https://doi.org/10.1080/10508422.2018.1559064

Sahil, N., Sood, S.K.: Scientometric analysis of natural disaster management research. Nat. Hazards Rev. 22 (2), 04021008 (2021). https://doi.org/10.1061/(ASCE)NH.1527-6996.0000447

Saini, K., Sood, S.K.: Exploring the emerging ICT trends in seismic hazard by scientometric analysis during 2010–2019. Environ. Earth Sci. 80 (8), 1–25 (2021). https://doi.org/10.1007/s12665-021-09597-4

Sarkar, S., Maiti, J.: Machine learning in occupational accident analysis: a review using science mapping approach with citation network analysis. Saf. Sci. 131 , 104900 (2020). https://doi.org/10.1016/j.ssci.2020.104900

Shim, B., Choi, K., Suh, Y.: CRM strategies for a small-sized online shopping mall based on association rules and sequential patterns. Expert Syst. Appl. 39 (9), 7736–7742 (2012). https://doi.org/10.1016/j.eswa.2012.01.080

Soltani, Z., Navimipour, N.J.: Customer relationship management mechanisms: a systematic review of the state of the art literature and recommendations for future research. Comput. Hum. Behav. 61 , 667–688 (2016). https://doi.org/10.1016/j.chb.2016.03.008

Sood, S.K., Rawat, K.S.: A scientometric analysis of ICT-assisted disaster management. Nat. Hazards (2021). https://doi.org/10.1007/s11069-021-04512-3

Sota, S., Chaudhry, H., Chamaria, A., Chauhan, A.: Customer relationship management research from 2007 to 2016: An academic literature review. J. Relationsh. Mark. 17 (4), 277–291 (2018). https://doi.org/10.1080/15332667.2018.1440148

Sota, S., Chaudhry, H., Srivastava, M.K.: Customer relationship management research in hospitality industry: a review and classification. J. Hosp. Market. Manag. 29 (1), 39–64 (2020). https://doi.org/10.1080/19368623.2019.1595255

Singh, N., Singh, P., Singh, K.K., Singh, A.: Machine learning based classification and segmentation techniques for CRM: a customer analytics. Int. J. Bus. Forecast. Mark. Intell. 6 (2), 99–117 (2020). https://doi.org/10.1504/IJBFMI.2020.109878

Tsiptsis, K.K., Chorianopoulos, A.: Data mining Techniques in CRM: Inside Customer Segmentation. Wiley, Hoboken (2009). ISBN: 978-0-470-74397-3

Google Scholar  

Tsou, H.T.: Linking customization capability with crm technology adoption and strategic alignment. Serv. Sci. 14 (1), 60–75 (2022). https://doi.org/10.1287/serv.2021.0286

Tu, Y., Yang, Z.: An enhanced customer relationship management classification framework with partial focus feature reduction. Expert Syst. Appl. 40 (6), 2137–2146 (2013). https://doi.org/10.1016/j.eswa.2012.10.028

Van Eck, N.J., Waltman, L.: Text mining and visualization using VOSviewer.  arXiv preprint arXiv:1109.2058 (2011)

Van Eck, N. J., Waltman, L.: Visualizing bibliometric networks. In: Measuring Scholarly Impact. Springer, Cham, pp. 285–320 (2014) https://doi.org/10.1007/978-3-319-10377-8_13 .

Wang, Y.J.: A clustering method based on fuzzy equivalence relation for customer relationship management. Expert Syst. Appl. 37 (9), 6421–6428 (2010). https://doi.org/10.1016/j.eswa.2010.02.076

Wright, O.: COVID-19 will permanently change consumer behavior. Accenture, April, pp. 1–9 (2020)

Xie, H., Zhang, Y., Wu, Z., Lv, T.: A bibliometric analysis on land degradation: current status, development, and future directions. Land 9 (1), 28 (2020). https://doi.org/10.3390/land9010028

Yap, B.W., Ong, S.H., Husain, N.H.M.: Using data mining to improve assessment of credit worthiness via credit scoring models. Expert Syst. Appl. 38 (10), 13274–13283 (2011). https://doi.org/10.1016/j.eswa.2011.04.147

Download references

This research received no specific grant from any funding agency.

Author information

Authors and affiliations.

Rajagiri Business School Kakkanad, Kochi, Kerala, 682039, India

Minnu F. Pynadath

Rajagiri College of Social Sciences (Autonomous), Rajagiri P.O, Kalamassery, Cochin, 683104, Kerala, India

National Institute of Industrial Engineering (NITIE), Mumbai, Maharashtra, 400087, India

T. M. Rofin

School of Management Studies, Cochin University of Science and Technology, Kochi, Kerala, 682022, India

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to T. M. Rofin .

Ethics declarations

Conflicts of interest.

The authors declare that they have no conflict of interest.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Pynadath, M.F., Rofin, T.M. & Thomas, S. Evolution of customer relationship management to data mining-based customer relationship management: a scientometric analysis. Qual Quant 57 , 3241–3272 (2023). https://doi.org/10.1007/s11135-022-01500-y

Download citation

Accepted : 15 July 2022

Published : 27 August 2022

Issue Date : August 2023

DOI : https://doi.org/10.1007/s11135-022-01500-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Data mining
• Scientometric analysis
• Citation analysis
• Conceptual structure map
• Find a journal
• Publish with us
• Track your research

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Elsevier - PMC COVID-19 Collection

Customer relationship management systems (CRMS) in the healthcare environment: A systematic literature review

Yahia baashar.

a College of Graduate Studies, Universiti Tenaga Nasional (UNITEN), Kajang 43000, Malaysia

Hitham Alhussian

b Center for Research in Data Science (CERDAS), Institute of Autonomous Systems, Universiti Teknologi PETRONAS, Bandar Seri Iskandar 32610, Malaysia

Ahmed Patel

c Computer Networks and Security Laboratory, State University of Ceara, Fortaleza 60020-181, Brazil

Gamal Alkawsi

Ahmed ibrahim alzahrani.

d Computer Science Department, King Saud University, Riyadh 11451, Saudi Arabia

Osama Alfarraj

Gasim hayder, associated data.

• • Three main CRM research categories covered: e-CRM, implementing CRMS and adopting CRMS.
• • Precise CRM research review and summarisation of empirical evidence for healthcare environments.
• • Presents positive and negative outcomes of CRM technology in the healthcare sector.
• • Results show significant gaps in the knowledge of CRM in the healthcare sector.
• • Few studies apply mixed methodologies and theories to investigate the CRM technology in the healthcare sector.

Customer relationship management (CRM) is an innovative technology that seeks to improve customer satisfaction, loyalty, and profitability by acquiring, developing, and maintaining effective customer relationships and interactions with stakeholders. Numerous researches on CRM have made significant progress in several areas such as telecommunications, banking, and manufacturing, but research specific to the healthcare environment is very limited. This systematic review aims to categorise, summarise, synthesise, and appraise the research on CRM in the healthcare environment, considering the absence of coherent and comprehensive scholarship of disparate data on CRM. Various databases were used to conduct a comprehensive search of studies that examine CRM in the healthcare environment (including hospitals, clinics, medical centres, and nursing homes). Analysis and evaluation of 19 carefully selected studies revealed three main research categories: (i) social CRM ‘eCRM’; (ii) implementing CRMS; and (iii) adopting CRMS; with positive outcomes for CRM both in terms of patients relationship/communication with hospital, satisfaction, medical treatment/outcomes and empowerment and hospitals medical operation, productivity, cost, performance, efficiency and service quality. This is the first systematic review to comprehensively synthesise and summarise empirical evidence from disparate CRM research data (quantitative, qualitative, and mixed) in the healthcare environment. Our results revealed that substantial gaps exist in the knowledge of using CRM in the healthcare environment. Future research should focus on exploring: (i) other potential factors, such as patient characteristics, culture (of both the patient and hospital), knowledge management, trust, security, and privacy for implementing and adopting CRMS and (ii) other CRM categories, such as mobile CRM (mCRM) and data mining CRM.

1. Introduction

Healthcare organisations face substantial pressure to maintain high quality medical care while simultaneously increasing safety and reduce costs [ 1 , 2 ]. Issues such as the growing number of chronic illnesses and the ageing population; higher patient demand and expectations; and the lack of qualified medical professionals, have complicated healthcare organisations’ ability to fulfil their missions [ 3 , 4 ].

Health information technology (HIT, also known as e-health or medical informatics [5] ), is viewed as a significant tool to achieve cost savings, efficiency, quality, and safety [6] , [7] , [8] . The benefits of HIT include: improved medical services and workflows, providing decision-making support and clinical information for medical professionals, expanding the quality, safety, and effectiveness of patient care, preventing medical errors; and reducing expenses, admissions, and paperwork [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] . Many studies suggest that the effectiveness of implementing HIT will determine the success and survival of the healthcare industry. Consumer e-health, patient engagement, and patient-centric care also play significant roles in delivering high quality medical services and meeting patient needs [17] . Many studies have found that the more patients are involved in their own health, the better outcomes in terms of quality, cost, and performance [18] , [19] , [20] . Healthcare providers now see the patient more clearly as the end consumer of medical services; thus, as in any kind of business, the concept of patient satisfaction and loyalty has become healthcare organisations’ foremost concern [2] .

Customer relationship management (CRM) is an innovative technology that seeks to improve customer satisfaction, loyalty, and profitability by acquiring, developing, and maintaining effective customer relationships and interactions [21] . From a healthcare perspective, Benz and Paddison [22] defined CRM as ‘an approach to learn about patients in order to communicate appropriately, and to build good relationships in order to deliver timely information, with the patient's results tracked to make necessary adjustments’. In this study, we embrace a balanced perspective, and define CRM in the healthcare environment as a managerial approach and healthcare information technology (HIT) application that supports the concept of patient-centric care. This allows hospitals to focus more on patients to meet their needs and expectations, improve loyalty, service quality (SQ), and build a long-term relationship.

The use of IT is essential for executing CRM, Yina [23] pointed out that effective CRM requires comprehensive data collection from both inpatients and outpatients through a multi-media platform, as well as integrating CRMS with various clinical networks such as hospital information systems (HIS), electronic health records (EHR), laboratory information systems (LIS), hospital web platforms, call centres, and SMS-based systems [ 23 , 24 ]. This requires healthcare providers to possess IT resources such as hardware, software, and infrastructure, to implement CRMS and store patient records more efficiently. Healthcare providers may be able to achieve patient loyalty if they considered two key factors: (i) the number of patients, and (ii) patient profit. The more ‘loyal patients’ a hospital has, the less investments it must make, the greater the profits it can gain, and vice versa. McDonald [25] stated that life-long value of a patient involves two major aspects: (i) the ‘core relationship’ which consists of a variety of uses of ‘frequency’ and confirming loyalty through ‘commitment’; and (ii) the ‘extended relationship’, which consists of product commercialisation such as ‘communication tools’ and word of-mouth ‘recommendations’. Furthermore, chronic diseases such as asthma, diabetes, hypertension, hyperlipidaemia, and cardiac failure, require continuous follow-up and self-management. Therefore, it is crucial for healthcare providers to know that certain patients require multiple healthcare services, and it is important to maintain high quality medical services and long-term relationships, which will eventually create long-life value [26] .

Previous research on CRM has made significant progress in several areas, such as telecommunications [27] , [28] , [29] , banking [30] , [31] , [32] , [33] , [34] , [35] , [36] , [37] , manufacturing [38] , [39] , [40] , and the service industry [41] , [42] , [43] , [44] , [45] , but research specific to the medical sector is very limited. Also, while many studies have attempted to review various HIT innovations and applications [46] , [47] , [48] , [49] , [50] , [51] , [52] , [53] , [54] , [55] , [56] , [57] , no systematic review has been conducted on CRM research with a focus on healthcare settings. However, the first systematic literature review to comprehensively investigate the CRM mechanisms from an information system (IS) perspective was performed by Soltani and Navimipour [58] . Twenty-seven studies between 2009 and 2015 were included in the review. The main objective of this work was to explore five common categories of CRM techniques with regards to the IS. These categories were; knowledge management (KM), E-CRM, data mining, social CRM and data quality. While this study focused on CRM mechanisms from IS perspective, our study focuses on CRMS in the healthcare environment. Filling this gap allows a significant contribution to be made, particularly on the issues of consumer e-health, patient engagement, and patient-centric care. For this purpose, we have undertaken a systematic literature review (SLR) of the empirical evidence regarding CRM research in the healthcare environment over the past two decades. Accordingly, we set out to answer the following key research questions:

• ■ RQ1: What are the research categories for CRM technology in the healthcare environment?
• ■ RQ2: What methods of data collection have been used?
• ■ RQ3: What are the positive and negative outcomes of CRM research in the healthcare environment?

This paper is organised as follows, Section 2 illustrates our review methodology which describes the search strategy, keywords used, selection process, critical appraisal, data collection and analysis. Section 3 presents and summarises the key findings of the selected studies. The main findings with response to the research questions, strength and limitations of the review as well as future work suggestions are discussed in Section 4 , and Section 5 concludes the paper.

2. Methodology and strategy

2.1. design.

For this study, a systematic review was conducted of disparate (quantitative, qualitative, and mixed) evidence of CRM [ 59 , 60 ], following the criteria of preferred reporting items for systematic reviews and meta-analysis (PRISMA) [ 61 , 62 ]. These include the following steps: (1) eligibility criteria; (2) information sources; (3) search terms; (4) study selection; (5) data collection process and synthesis; and (6) critical appraisal.

2.2. Eligibility criteria

Studies were eligible for inclusion if they were: presenting an empirical and conceptual evidence; directly relevant to CRM in healthcare settings (hospitals, clinics and medical centres); papers that are conducted in developing countries; published from 2000 to present; and published in peer-reviewed journals. The main reason for selecting studies that were conducted in developing countries is because health technologies play an important role in the effectiveness of patient care and treatment, yet access to such technologies remain a big challenge for communities with limited recourses.

We excluded studies if they were: not written in English; traditional reviews, thesis and conference proceedings; papers that focused on other healthcare domains such as insurance and pharmaceuticals; and papers that were not available in full text.

2.3. Information sources

Our methodical procedure used various strategies to obtain as many relevant studies as possible from a diverse evidence base [ 63 , 64 ]. In the first step, we searched the Database of Abstracts of Reviews of Effects (DARE) and the Cochrane Database of Systematic Reviews (CDSR) to verify if there were any existing or ongoing systematic reviews similar to our subject.

Secondly, we conducted an organised, systematic and comprehensive wide-ranging search of six online databases: Web of Science, ScienceDirect, Scopus, SpringerLink, IEEE Xplore, and the Association for Computing Machinery (ACM) Digital Library. We also examined four group publishers of academic journals (Emerald Insight, Wiley Online Library, Taylor & Francis Online, and SAGE Digital Library) and two search engines (PubMed and Google Scholar).

To identify relevant studies, we then performed a library search of six medical informatics and health management journals: (1) the Journal of Medical Internet Research; (2) the Journal of the American Medical Informatics Association (JAMIA); (3) PLOS (Public Library of Science) ONE; (4) BMJ Open; (5) the International Journal of Medical Informatics; (6) BMC Medical Informatics and Decision Making. We also contacted two experts in the field to find additional or unpublished relevant studies. Finally, we checked reference lists of all eligible studies using Google Scholar to discover hidden additional studies. It should be noted that each online database has its own search engine features. Hence, the search string had to be modified and adapted for each online database. To do so, our search was recorded in a separate text that includes the following details: source category, source name, search method and date of search for each online database. This can be seen in Table 1 .

Search space for selected databases.

2.4. Search terms

The search process is very crucial; therefore, the keywords were optimised. In the first stage, we obtained a set of keywords and terms from the acquired studies and matched them with our research aims and questions. Secondly, we established alternative characteristics and synonyms. The defined keywords were tested in different databases and lastly, we optimised them. Table 2 summarises the final list of keywords used in the search.

List of keywords used in the search process.

Next, Logical operators were connected with different sets of keywords and designed as follows: (CRM OR CRM system OR CRM technology) OR (PRM OR PRM system OR PRM technology) AND (Healthcare industry OR healthcare sector OR hospital OR healthcare providers OR medical centre OR medical service) AND (Developing countries).

2.5. Study selection

The study selection process attempts to analyse, evaluate and identify relevant articles based on the goals of our systematic review. This process was independently performed by three co-authors (H.A., A.P. and G.A.) of this study. Table 3 explains each stage that has been executed in the study selection process. In the first stage (S1), records are identified through different information sources (online database, academic journal and reference list). Once all records are obtained, we applied the first filter in the second stage (S2), to which records are excluded based on duplicates. We used EndNote X9 to remove duplicates and manage all records.

Stages of the study selection process.

Once all duplicates are removed, records are screened based on “title, abstract and keywords”, during this third stage (S3), any studies that did not meet the eligibility criteria were excluded. Also, during this stage, we considered studies for a “full-text” screening, and arranged meetings between co-authors whenever there were doubts. Such meetings have allowed co-authors to review and agree on studies that were within the scope and pertinent to this systematic review.

The first meeting (S4), aimed to discuss the findings of the third stage and select the primary studies for the next stage (S5), where a “full-text” screening of all studies was performed. A second meeting, which was the last stage (S6) of the selection process, was carried out to discuss and agree on the final studies that are included in this systematic review.

2.6. Data collection and synthesis

We used EndNote X9 to collect basic publication data such as date, title, authors, publisher, DOI, URL, pages, volume, issues, keywords and abstract. In addition to EndNote, one co-author (G.H.) of this study placed data from eligible studies into a data extraction spreadsheet using Microsoft Excel 2016, then two other co-authors (Y.B. and A.P.) validated it independently. The data items extracted from each eligible study were: year of publication; author; brief description; type of evidence; participants; sample size ( n ); healthcare organisation type and size; country of origin, and outcomes.

To synthesise the data as accurately and in an unbiased manner as possible, we performed a narrative synthesis review for effectiveness [65] of diverse study characteristics, which allowed us to categorise and identify three main CRM research categories that were relevant to healthcare settings: (i) e-CRM (Web-based CRM); (ii) implementing CRMS; and (iii) adopting CRMS. We also created a qualitative and quantitative evidential narrative summary [ 60 , 63 ] for each CRM research category.

We contacted the original authors of the selected studies by e-mail to resolve any doubts or confirm an absence of information. In addition, any disagreements between co-authors of this study were settled through consensus.

2.7. Critical appraisal

Two co-authors (A.I.A and O.A.) of this study independently appraised the quality of selected studies to avoid misinterpretation and bias. We followed the criteria of quality assessment and assurance tools for undertaking a systematic review of disparate data developed by Hawker et al. [66] . Table 4 elaborates the checklist that is used to appraise each individual study. This checklist is based on nine assessment criteria with four rating scores defined as good, fair, poor and very poor respectively. A description of how these ratings were assigned and evaluated is described and illustrated in Table 4 .

Checklist items used for critical appraisal.

*Score criteria for QA = Quality assessment

Our primary concern being to obtain sufficient knowledge and evidence of the nature of CRM in the healthcare environment, and we performed sensitivity intervention analysis [67] to determine whether: (i) the inclusion of each study was based on its quality, and (ii) the exclusion of empirical data from conference proceedings would have any effects on our ultimate results. We resolved disagreements on critical appraisal of the selected studies through group discussion and with “chairperson” arbitration assistance given by the first author (Y.B.) of this paper.

3. Results of the systematic review

Firstly, we illustrate the results of our study screening and selection process according to PRISMA guidelines. Secondly, we describe the trends and characteristics of the selected studies and present quantitative data (i.e. publication year and location, methods of data collection, settings, participants and sample sizes). Thirdly, we demonstrate the results of our critical appraisal of each study, and lastly, we summarise the CRM research categories. All findings stated in this section are directly responding to our set of three research questions (RQs).

3.1. Study selection

In our initial search, we found 1642 studies (see Fig. 1 ). We identified a total of 1682 studies for the review, including those found through manual searches of medical informatics and management journals ( N  = 17), and reference lists ( N  = 23). Studies removed after duplicates ( N  = 693). Applying the eligibility criteria, we excluded 891 studies by screening the title, the abstract and the keywords. Finally, we excluded 79 based on the full text screening. Hence, the final sample consisted of 19 studies.

PRISMA flow chart for the screening and selection process of the selected studies.

3.2. Study trends

Fig. 2 presents the per year distribution of the selected studies. The number of studies has remarkably increased in recent years, which indicates that CRM in the healthcare environment is progressively attracting the attention of scholars and researchers. Most of the studies selected were conducted between 2012 and 2017 ( N  = 13, 568%).

Distribution of studies per year.

As shown in Fig. 3 , most of the studies were conducted in Taiwan and Iran ( N  = 10, 53%), which indicates the advanced production of ICT and medical informatics in those regions and therefore associated research efforts into their impact. Of these, 21% ( N  = 4) were carried out in India and Jordan. The rest took place in Iraq, Brunei, Korea, Malaysia and Kuwait.

Distribution of studies per country.

3.3. Study characteristics

Among the selected studies, 68% ( N  = 13) had quantitative designs. The remaining studies used qualitative ( N  = 3, 16%) and mixed-method approaches ( N  = 1, 5%), while two studies (11%) were based on conceptual modelling of CRM (see Fig. 4 ).

Distribution of methods used in the selected studies.

The healthcare settings were mainly various kinds of hospitals ( N  = 11, 58%) such as private, public, regional, community and university hospitals ( Fig. 5 ). The rest of the studies were conducted in nursing homes ( N  = 2, 11%) and health centres ( N  = 2, 11%). Four studies (21%) were conducted in multiple settings such clinics, homecare centres, and health promotion centres.

Settings of the selected studies.

Among the targeted groups, 47% of the studies ( N =8), have simultaneously recruited multiple stakeholders such as patients, patient families, medical staffs, CRM experts, nurses, nursing professionals, nurse supervisors, HIS professionals, chief executive officers (CEOs), or chief information technology officers (CITOs). As shown in Fig. 6 , the remaining respondents were management (24%), physicians (6%), patients (6%), nurses (6%), and auxiliary medical staff (6%).

Participants of the selected studies.

Majority of the studies have utilised a sample size between 200 and 399 ( N  = 5, 31%). Only one study has used a sample size of more than 400. Fig. 7 illustrates the variation of the sample sizes used in the selected studies.

Sample size of the selected studies.

3.4. Critical appraisal of selected studies

Our critical appraisal of the selected studies (see Table 5 ) found major weaknesses in four areas: (i) research methods and data, (ii) sampling, (iii) ethics and bias and (iv) implications and usefulness. In terms of research methods and data collection and analysis, studies gave little descriptions of the methods and approaches to gather and record data in a consistent manner (rated as fair and poor in 53% and 26% of the studies, respectively).

Critical appraisal of the selected studies.

*Note: Numbers in brackets denotes N (%); and NR denotes not reported.

Regarding the sampling, some researches lacked details on the sampling techniques and strategies, the justification of the sample size and target groups, as well as the response rates (assessed as fair, poor, and not reported in 47%, 21%, and 5% of the studies, respectively). Furthermore, the ethics and risk of bias was not clearly reported in 74% of the studies. We rated the implications and usefulness of the selected studies as poor and not reported in 47% of total studies. The results of this review remained reliable and consistent, even after we performed the sensitivity analysis to determine whether we included each study based on its quality, and whether excluding empirical data from conference proceedings had any effects on our results.

3.5. Category of CRM research in healthcare

Our analysis of the selected studies revealed three main categories of CRM research in the healthcare sector: (i) e-CRM (Web-based CRM); (2) implementing CRMS; and (3) adopting CRMS. While precisely 58% of the selected studies ( N  = 11) focused on implementing CRMS, other research categories were less frequently investigated: social CRM ( N  = 5, 26%), and adopting CRM ( N  = 3, 16%). Fig. 8 shows the representation of CRM research categories.

Category of CRM research in healthcare.

3.5.1. e-CRM (web-based/social CRM)

Social CRM or e-CRM is a new concept that has emerged into the CRM systems due to the incessant advancement of IT and web services, as well as other advances in ICT and data science techniques. The authors of [68] defined e-CRM as a modern approach and a system that integrates both Web 2.0 and the influence of online groups with conventional CRM systems to build strong communication and relationships between the customers and the firms. In the healthcare environment, many studies have explored the phenomena of e-CRM. As early as 2001, Kohli et al. [69] explored the Web-based CRM system in a hospital through a physician profiling system (PPS). Results gathered after implementing PPS showed that total charges were significantly reduced, which led to better care and patient's satisfaction.

The authors of [70] proposed a social CRM model to support patient empowerment through a Web 2.0, namely CRM 2.0. They surveyed 366 patients, patients’ family members and medical staff from various hospitals and homecare centres to determine patients’ expectations of e-health services, and to verify the empowerment features proposed in the model. This study found that there was high demand for empowering patients through the Web. The findings also revealed that more than 80% of targeted groups preferred to view health promotions, make appointments and payments online. While more than 75% preferred to go online to view their own medical records and discuss their health conditions on a social network. Also, 73% of participations desired an online consultation.

The authors of [71] developed a framework for implementing e-CRM, and surveyed 150 managing directors and branch managers from 50 clinics and hospitals (both public and private) to investigate the key factors of executing e-CRM based on their importance and priorities. This study concluded that patient's involvement is the most important factor in implementing e-CRM.

An e-CRM adoption framework was proposed by Jalal et al. [72] to determine the crucial factors that influence the adoption of e-CRM in hospitals. TOE, diffusion of technology and institutional theories were utilised in the construction of the framework. This work found that technological factors such as complexity and relative advantage; organizational factors such as size and management support; and environmental factors such as regulatory and external pressure are very crucial for e-CRM adoption.

Similar to this work, the authors of [73] proposed an e-CRM implementation framework utilising Technology-Organization-Environment (TOE), diffusion of technology and Information System (IS) success theories and found that technological (compatibility, interactivity and privacy); organizational factors (management support, social media policy and leadership knowledge) and environmental factors (social trust and bandwagon pressure) are very critical for e-CRM implementation.

3.5.2. Implementing CRMS

As early as 2005, Cheng et al. [74] established a framework to support executing CRM in nursing homes. The authors of [ 75 , 76 ] influenced this framework, which involves three aspects of CRM: case management (CM), data management (DM), and care service management (CSM). The results yielded nine sub-dimensions: (1) an interactive mechanism; (2) an assessment of demand models; (3) customer data collection; (4) data analysis, (5) knowledge management (KM), (6) care service design; (7) care service delivery; (8) support from related units and (9) monitoring and feedback. The final framework showed that implementing CRM in nursing homes required: (A) leadership ‘support from high level managers’; followed by (B) a culture involving the ‘participation of all members’ in order to employ aspects of a (C) CRM system (CM, DM, and CSM) within the nine sub-dimensions. The study also found that most nursing homes have yet to implement CRM, and computerisation requires more effort.

The authors of [77] argued that the CRM framework proposed by Cheng et al. [74] was inappropriate because their results were obtained from a value characteristic questionnaire. However, the two sub-dimensions of CM, ‘interactive mechanism’ and ‘assessment of demand models’, did not clearly map all the attributes defined in the survey. Accordingly, similar to the work of [74] , Gulliver et al. [77] adapted a value characteristic framework to support establishing CRM in nursing homes. This study found the most three significant dimensions to be: the ‘behaviour of service personnel’, the ‘design of care processes’, and ‘support from related units’. Based on the findings, the authors claimed that approaches to executing CRM in nursing homes would be inappropriate if only one solution was considered to fulfil all the attributes. Accounting for each attribute would ensure consistency, relevancy, and provide an effective, focused plan. While allowing each individual characteristic to be linked to a specific CRM solution type would support the practical implementation of CRM, we believe the adapted framework could also assist hospitals, especially in terms of answering the following questions: (1) what are the most valuable elements of putting CRM into practice? and (2) How can we link each feature to a CRM solution type?

The authors of [78] adopted the soft system dynamics methodology (SSDM) and used a case study on a physical examination centre to evaluate the steps of applying a CRM model. SSDM integrates the qualities of both soft system methodology (SSM) and system dynamics (SD) that involves four phases with ten systematic steps. The results showed that the four stages and ten systematic steps allowed the authors to positively measure and evaluate the CRM model. Improved efficiency, as well as provided a better service for health organisation were outcomes of this process.

The authors of [79] explored the key factors of realising CRM systems, and proposed a model based on three attributes: (1) the organisation itself (resources, management, and employee factors); (2) applying CRM (the CRM system factor) and (3) the customer (the patient factor). The authors recommended and proposed the ‘DeLone and McLean information systems (IS) success model’ to assess CRM implementation.

The authors of [80] proposed and adopted the ‘DeLone and McLean IS success model’ for executing CRM based on three traits: (1) the system (system quality, information quality, and SQ); (2) the user (perceived usefulness and user satisfaction) and (3) performance (organisational and personal performance). They administered a survey to 243 CRM system users from 13 health promotion centres to validate the aforementioned IS model. The outcomes showed that (1) the CRM model was feasible; (2) of system attributes, only ‘information quality’ and ‘SQ’ had a significant influence and relationship with ‘perceived usefulness’ and ‘user satisfaction’ and (3) ‘perceived usefulness’ and ‘user satisfaction’ had a significant impact on ‘personal performance’ as well as an indirect effect on ‘organisational performance’.

The authors of [81] surveyed 615 staff members in 108 privately run and 30 hospital-based nursing homes to assess CRM implementation. The author adapted the CRM scale developed by Sin et al. [82] , which involves four dimensions: (1) a key customer focus; (2) CRM organisation; (3) technology-based CRM and (4) KM, along with 23 sub-dimensions. However, in this study, only 18 sub-dimensions were adapted to evaluate CRM implementation. Furthermore, the study found that the two types of nursing homes had different ways of building relationships with residents. Hospital-based nursing homes leaned toward understanding patients’ needs and delivering prompt medical service through the concept of ‘knowledge learning’. Private nursing homes focused on ‘CRM organisation’ and ‘technology-based CRM’ to foster personal connections with residents.

Another CRM implementation model was introduced by Zamani and Tarokh [83] , this model consists of seven components; Customer satisfaction, loyalty, trust, expectations, perceptions, perceived quality and Architecture. A total of 303 patients were surveyed and found that All seven components were significant and have relationship with each other. Also, the authors of [84] designed a CRM implementation model based on HR factors such as employee satisfaction, organizational culture, communication management, empowerment, organizational commitment, organizational structure and change management. This work surveyed 215 managers of a university hospital. Findings revealed that HRM plays a crucial role in the implementation of CRMS. However, the employee satisfaction factor had the highest influence on the implementation of CRMS.

The authors of [85] surveyed 100 patients and CRM users to analyse the factors that influence the implementation of CRM based on software aspects. Results showed that operational efficiency, centralization of data, management of existing customer and hospital image have a significant influence on the implementation of CRMS.

The authors of [86] evaluated the effects of CRMS implementation on customer trust, loyalty, satisfaction and organisational productivity. They administered a survey to 268 CRM nurses from various hospitals. Results showed that customer satisfaction and diversification have the highest effects on CRMS implementation, while organisational productivity had the lowest impact.

The authors of [87] investigated various impacts and benefits of implementing CRMS in hospitals. More than 550 of doctors, administrators and IT staffs were surveyed and found that waiting time reduction, better doctor allocation, and patient satisfaction were the major implication of CRM implementation in health care.

3.5.3. Adopting CRMS

To investigate the critical factors that influence the adoption of CRM systems (CRMS), Hung et al. [21] performed a 95-questionnaire study of Information Systems (IS) executives at three levels of health organisations: medical centres, community hospitals and regional hospitals. The results showed that 39 hospitals adopted CRMS, while 56 did not. ‘Relative advantages’, the ‘size of the organisation’, the ‘IS capabilities of the staff’, ‘KM capabilities’, and the ‘innovation of senior executives’ have significantly influenced the adoption of CRM systems the 39 hospitals. The authors found ‘complexity’ to be insignificant. The authors recommended several implications for CRM vendors, hospitals, government, and researchers to increase possibility of adopting CRM.

To examine the influence and relationship of ‘external influences’, ‘technology’, and ‘organisations’ factors on CRM system adoption, Alkhazali and Hassan [88] conducted a 103-questionnaire of the top management in 18 different hospitals (15 private ones and 3 public ones). This study found that most of the hospitals only used Web-based CRM. Furthermore, the results showed that ‘organisations’ and ‘technology’ significantly influenced the adoption of CRM. The authors found the external factors to be insignificant. Also [89] examined the relationship between CRMS adoption, perception and organisation performance. A 103-survey of the top management in various hospitals was also conducted and found (i) a significant relationship between organisation performance and CRMS adoption, and (ii) a significant relationship between CRMS adoption and CRMS perception.

In light of the above, the majority of studies regarding the three main categories of CRM (e-CRM, implementing and adopting CRMS) were able to produce positive outcomes for patients, medical professionals and healthcare organizations.

To offer a better illustration and respond to our three research questions (RQs), Table 6 , Table 7 , Table 8 , provide a summary details of each CRM category. These include year, author, brief description, participants, settings and the methods of data collection which directly respond to RQ2. To answer RQ3, we assigned plus (+) and minus (−) symbols to the findings column which indicate the positive and negative outcomes of each study.

Summary of e-CRM studies in healthcare

* Sample size ( n ), Positive result (+).

Summary of studies related to implementing CRMS in healthcare.

* Sample size ( n ), Positive result (+), Negative result (−).

Summary of studies related to adopting CRMS in healthcare.

Sample size ( n ), Positive result (+), Negative result (−).

4. Discussion

4.1. findings related to rq1, rq2 and rq3.

Our analysis of the current literature indicates that there are significant gaps in knowledge regarding CRM in the healthcare environment. We found three main CRM research categories: (1) e-CRM ( N  = 5, 26%); (2) implementing CRM ( N  = 11, 58%); and (3) adopting CRM ( N  = 3, 16%). We proposed an introductory framework that organises all three aspects.

Fig. 9 presents a framework for categorising CRM research in the healthcare environment, beginning with social web-based CRM (e-CRM). This means that all CRM applications, functions and features are used through the internet environment. This also suggests that hospitals should manage all forms of communication and relationships with their patients through Web 2.0 and social media technologies. Consistent with this, Kohli et al. [69] explored a Web-based CRM application called a PPS, finding positive results in several areas, such as the physician-hospital relationship, medical operations, patient satisfaction, and clinical outcomes (especially in nutrition and neurology). However, the results of this study were based on physician case studies, and empirical data from a cost/benefit analysis of PPS performance during and after its implementation. It would have been more efficient if patients were involved in the case study, to know whether executing PPS had a direct influence and/or relationship with patients. Adding to this, Anshari et al. [70] proposed a CRM 2.0 model to determine patient expectations of e-health services. This study also showed a positive outcome regarding empowerment of patients through the web, given that more than 70% of participants (patients) wished to view their electronic medical records online, as well as make appointments and payments, and obtain consultations and referrals. Only one study proposed a framework for establishing social CRM (i.e. eCRM), and identified key factors based on importance and priorities [71] . Hence, more research is needed to better understand this element in healthcare organisations.

CRM research categories framework in the healthcare environment.

Our analysis and evaluation revealed that (i) physician interaction, cleanliness, and nursing were the most significant factors that influenced patients’ choice of hospital, patient satisfaction, and service quality (SQ). (ii) Paediatrics, cardiology, and neurology were the most significant medical preferences and key competitive advantages obtained by hospitals. (iii) Management (i.e. support and involvement), resources (I.e. IT infrastructure), and employee training factors were the most substantial aspects that influenced the implementation of CRM systems, as well as e-CRM. (iv) Hospital size, the medical staff's IS capacity, and knowledge management (KM) capabilities were the most significant factors that impacted the adoption of CRM systems. (v) Collecting patient's data was the most executed CRM feature in the healthcare environment, while data analysis was the most underachieved.

4.2. Strengths and limitations of this study

This appears to be the first systematic review to comprehensively synthesise and summarise the empirical evidence available for CRM research in the healthcare environment. Our search strategy was broad, examining several databases, search engines, platforms and academic journals, striving to find published and unpublished studies based on various CRM concepts, locations and settings. For each selected study, we provided a critical appraisal of its methodology and data, sampling, data analysis, ethics and bias, findings and implications and usefulness. In addition, we highlighted each study's methodological strengths and weaknesses. We have also identified studies with positive and negative outcomes, which will help hospitals and policymakers to better understand the benefits of implementing CRMS.

Despite its strengths, our study also faces some limitations. First, we believe that the matter of the potential exclusion of studies that were not in English and from conference proceedings has been addressed at academic gatherings and in other languages, such as Chinese and French. Second, our critical appraisal showed the quality of selected studies varies from ‘fair’ to ‘poor’. However, our sensitivity analysis did not show that excluding conference proceedings and including ‘poor’ quality studies affected our results. Last, our review may have a publication bias because studies with positive outcomes are more frequently published than negative ones; however, the studies that we found that had positive outcomes had several weaknesses in terms of methodology, sampling, and data analysis.

4.3. Recommendations for future research

We suggest the population-intervention-comparison-outcome (PICO) framework (see Table 9 ) to help scholars form research questions when planning future investigations involving CRM in the healthcare environment. According to [ 90 , 91 ] the PICO framework is widely used in medical/healthcare informatics and health research to help state the terms of reference, define the scope and manage research questions, search strategies, and eligibility criteria.

PICO frame for CRM research in the healthcare environment

Our analysis showed that issues such as the privacy and security of patients, and their roles in CRMS development, were not yet investigated by the selected studies. Today, more and more of patient personal and health information (PHI) is being stored in CRMS, many of these data are created by doctors, clinics and hospitals and they offer plenty of advantages such as reducing medical mistakes, sharing information easier and offering better care. Whether PHI is maintained in a paper record or an electronic health record, patients have the right to keep it private, and that privacy is protected by laws called health insurance portability and accountability act (HIPPA) [92] and general data protection regulation (GDPR) [93] . These laws require that certain healthcare providers keep PHI private and secure encrypted form and proper journalised history from its inception. Firewalls, strong encryption, secure login, access control and authentication mechanisms are some of the security measures that healthcare providers may or should use to protect the privacy of patients when implementing CRMS. As patient's privacy continues to evolve, certain policies and guidelines need to be followed to properly control access, disclose and protect PHI under all circumstances to avoid misuse and litigations. Hence, we suggest that future research further focuses on patient public and private information, privacy and security perceptions and how these might influence the implementation and adoption of CRMS in open environments.

Our results also revealed that only organisational and technological factors have been examined using a quantitative method (such as survey), with no formidable theoretical base used (except for 4 studies, which applied the IS success model and TOE theory). We encourage researchers to use different methods more readily to explore other potential factors such as culture and trust to determine how they might influence decision-making, and also to apply a greater variety of theories.

With the increasing number of wearable devices, smart phones and mobile applications (apps), ICT services, the mobile health (mHealth) domain is rapidly developing and gaining momentum at rapid speed as can be observed during the global 2020 coronavirus (CONVID-19) spread. Yet, none of the included studies have addressed mobile CRM (m-CRM) in health care in a consistent and systematic manner. Many studies on mobile health have showed promising results in heart pace monitoring, body weight, blood pressure, and heart disease monitoring [94] . We believe patient with chronic illness could greatly benefit from m-CRM in multiple ways, allowing for immediate medical responses and new symptom and diagnoses detection methods and procedures. Therefore, we recommend more research that addresses m-CRM in healthcare functionality and the issue of privacy and security of both patients as well as all healthcare workers. This might also require a set of de facto and/or international standards to be developed not only in m-CRM and e-CRM domains but everything spanning this phenomenon.

5. Conclusions

In this study, we aimed to review, categorise, summarise, synthesise, and appraise CRM research in the healthcare environment. This SLR was performed by following the criteria of preferred reporting items for systematic reviews and meta-analysis (PRISMA) [ 61 , 62 ]. Our initial search identified 1,642 records, and 40 further studies from manual journals and reference lists search. Our search and selection process went through different phases to degrade the findings. In total, 19 studies were carefully identified and analysed. Each study was evaluated by following the criteria of quality assessment tools for undertaking a systematic review of disparate data developed by Hawker et al. [66] . The findings were qualitatively and quantitatively organised based on three main research categories; e-CRM (Web-based CRM), implementing CRMS, and adopting CRMS. The selected studies were published between 2000 and 2020. Our results indicate that research and development on CRM within the healthcare environment is still in its early stages in uncharted waters , and more research would be helpful. This SLR provides several insights and recommendations for researchers, healthcare institutions, service providers, policymakers, ICT developers and suppliers.

Authors statement

The authors, individually or jointly, have no institutional, financial and personal relationships with other persons, organizations and sponsoring or funding agencies, other than acknowledging such agencies in the manuscript for their generosity funding the research work upon which the manuscript is based. In addition, we hereby declare that the manuscript has not been submitted to any other journal, conference proceedings or books for publication. It is solely the work of the authors.

Declaration of Competing Interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

Acknowledgments

This work was funded by the Researchers Supporting Project (RSP-2019/102), King Saud University, Riyadh, Saudi Arabia.

Biographies

Yahia Baashar received the B.S. degree in network computing and the master's degree in management information systems from Coventry University, INTI International University, Malaysia. He is currently pursuing the Ph.D. degree in industrial science with The Energy University (UNITEN), Malaysia. He has published in journals and conferences. His research interests include technology acceptance, medical informatics, e-health, artificial intelligence, and machine learning.

Hitham Alhussian received the B.Sc. and M.Sc. degrees in computer science from the School of Mathematical Sciences, Khartoum University, Sudan, and the Ph.D. degree from Universiti Teknologi Petronas, Malaysia, where he is currently a Senior Lecturer with the Computer and Information Sciences Department and Core research member of Centre in Research and Data Science (CERDAS). His main research interests are in real-time parallel distributed systems, cloud computing, big data mining, machine learning and secure computer-based management systems.

Ahmed Patel received his M.Sc. and Ph.D. degrees in Computer Science from Trinity College Dublin (TCD), University of Dublin in 1978 and 1984 respectively, specializing in the design, implementation and performance analysis of packet switched networks. He is Research Professor at Universidade Estadual do Ceará, Fortaleza, Brazil with key research interest in Advanced Computer Networking, Internet of Things, Cloud Computing, Big Data, Predictive Analysis, Use of Advanced Computing Techniques, Impact of e-social Networking, Closing the digital divide ICT gap and ICT Project Management. He has published well-over 272 technical and scientific papers and co-authored three books, two on computer network security and the third on group communications. He co-edited one book on distributed search systems for the Internet and also co-edited and co-authored another book entitled: “ Securing Information and Communication Systems: Principles, Technologies and Applications ”. He is a member of the Editorial Advisory Board of International Journals and has participated in Irish, Malaysian, Brazilian and European funded research projects.

Gamal Alkaws received the B.S. degree in software engineering and the master's degree in management information systems from Coventry University, INTI, Malaysia, and the Ph.D. degree in information communication technology from The Energy University (UNITEN), Malaysia, in 2019. He is currently a Postdoctoral Researcher with The Energy University (UNITEN). He has published in journals and conferences. His research interests include emerging technology acceptance, user behaviour, adoption of information systems in organizations, the IoT, artificial intelligence, and machine learning.

Ahmed Ibrahim Alzahrani received the master's and Ph.D. degrees in computer science from Western Illinois University, USA, and De Montfort University, U.K., respectively. He is currently an Associate Professor with the Department of Computer Science, Community College, King Saud University. He acts as the Head of the Informatics Research Group, and a member of the Scientific Council-King Saud University. His main research interests span over IT diffusion and innovation, information technology management, human behaviour modelling in technology usage, online social networks, and humancomputer interaction using cognitive research.

Osama Alfarraj received the master's and Ph.D. degrees from Griffith University, in 2008 and 2013, respectively, all in information and communication technology. He is currently an Associate Professor of computer science with King Saudi University, Riyadh, Saudi Arabia. His current research interests include e-Systems (e-Gov, e-Health, and e-commerce), cloud computing, and big data. He served as a consultant for two years and a member of the Saudi National Team for Measuring E-Government, Saudi Arabia.

Gasim Hayder is Charted Engineer (C.Eng.) and a senior lecturer in Civil Engineering Department, College of Engineering, Universiti Tenaga Nasional (UNITEN). He also served as Head of Postgraduate and Laboratory Services Unit in the Institute of Energy and Infrastructure (IEI), Head of Water and Environmental Engineering Unit in the Department of Civil Engineering, and senior researcher for Sustainable Engineering Group UNITEN. He has working experience in engineering practices, teaching and management including as Construction Engineer, Senior Engineer, Technical Manager (multinational company), Graduate Assistant, Research Assistant, Treatment Process Specialist Consultant and Senior Lecturer. He has several publications, patents and awards in both national and international levels. His research interests are Environmental Engineering, Wastewater treatment, Biological processes, Solid Waste Management.

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.csi.2020.103442 .

Appendix. Supplementary materials

What CRM research tells us about selecting a CRM (2022 CRM report)

What should you expect when selecting a CRM ?

Finding the right system can be a difficult process. You need to consider the cost of the CRM, who’ll be using it, the hosting method, what you want to gain from implementing, and more.

That’s why we’ve gathered and analyzed data from over 100 selection projects active in the last year in our 2022 CRM report . We’ve spoken to project stakeholders to find out the average cost per user, project timeframes, and hosting methods. Our CRM research provides a comprehensive overview of what selecting software looks like in 2021.

Here is a summary of some of our key findings:

Quick jump to:

• How much does CRM cost?
• How long does it take to select a CRM?
• How many people use a CRM in a company?
• Why does a company implement CRM?

1. The average budget per user for CRM software is $7,500.

The most common question asked about CRM has to be: how much does CRM cost?

Well, according to our recent CRM research, you can expect to pay $7,500 for each user of your system. This figure is the projected total cost per user over a five year period. 

This works out at approximately $1,500 per user per year or $125 per month per user .

This is almost quadruple the cost per user that was reported in our previous report suggesting companies are investing more in their CRM systems now our world has moved more remote.

2. On average, 47.99% of employees use a company's CRM system.

According to our CRM report, almost half of a company’s employees use their CRM system on average.

This figure varies more significantly when broken down by company size. The findings show that businesses with less than 50 employees have a much higher percentage of employees using their CRM system. In contrast to this, companies with over 250 employees only had 17.93% of their employees using their CRM system.

From this data, a company with 40 employees would expect to have approximately 9 CRM users in their business. According to our CRM pricing data, they can expect to spend $1,125 per month or $13,500 per year for their system.

3. On average, companies spend 11 weeks selecting CRM.

The time frame for selecting a CRM is only 10 weeks on average.

The time taken to select a system increases as the company size grows. Companies with more than 250 employees take an extra four weeks to select a system when compared to businesses with less than 50 employees.

4. The most popular reason for implementing a CRM is to support growth.

Most companies were implementing a CRM to support their company's growth. This was the leading reason for implementing a solution with 35.82% of companies listing this as the driving force behind selecting a new system.  It is a positive outlook for CRM that more companies are seeing the software as a means to help their business expand.

Almost a quarter of companies were implementing to increase efficiency. It's not surprising that a significant number of companies were aiming to improve their internal processes and make gains in efficiency and gain greater functionality.

Read more interesting statistics and read the latest CRM research by downloading the full Software Path 2022 CRM Software Project Report .

Read Next…

• Software Categories

Get results fast. Talk to an expert now.

855-718-1369

Best crm software 2024.

In this article...

What is CRM software?

CRM software manages and analyzes business contact and customer information by storing and organizing it effectively. Sales, marketing, and customer service teams use the CRM platform to automate the gathering and structuring of data related to customers, leads, partners, and crucial business relationships.

The processes and systems that help improve a business’s relationships with their contacts may also be called customer relationship management.

Below, we have scored and ranked some of the top CRM solutions in the market. Each has its own unique features and functions that make them best suited to various use cases.

• HubSpot Sales Hub : Best for integrations
• Zoho CRM : Best for decentralized teams
• Shape CRM : Best for flexibility
• Pipedrive : Best for pipeline management and optimization
• Salesforce Sales Cloud : Best CRM for enterprises
• Oracle NetSuite CRM : Best all-in-one solution
• Insightly CRM : Best for easy-to-read analytics and reporting
• Keap CRM : Best for sales and marketing synergy
• Copper CRM : Best for security
• ClickUp : Best for project management
• Less Annoying CRM : Best for simplicity

Our top 11 CRM recommendations

May 29, 2024: Reworked several sections of copy to read more accessibly. Did some copy edits, updated new pricing for two vendors, and made feature updates to two vendors.

March 8, 2024: Updated proper names for HubSpot Sales Hub, Salesforce Sales Cloud, & Insightly CRM. A couple other minor copy tweaks for readability.

Jan. 29, 2024: Added information about the author and made small stylistic updates.

Jan. 23, 2024: Verified vendor pricing and information.

Dec. 11, 2023: Added rubric scoring for all vendors. Verified pricing and features throughout.

Oct. 25, 2023: Added vendors.

Oct. 18, 2023: Updated pros and cons

Oct. 4, 2023: Updated visual elements.

Sept. 13, 2023: Updated elements to current standards. Verified links and pricing. Copy edits and a few minor changes to copy. Added bylines.

Aug. 7, 2023: Verified all pricing and links for accuracy. Added two additional vendors. Minor edits to copy.

At TechnologyAdvice, we assess a wide range of factors before selecting our top choices for a given category. To make our selections, we rely on our extensive research, product information, vendor websites, competitor research and first-hand experience. We then consider what makes a solution best for customer-specific needs. 

By defining business needs, we can determine the essential features organizations in various sectors require, and select platforms that will cover all bases. Reputable providers known for their ease of use and customer satisfaction are added to our compilation list for further analysis. We then evaluate each solution on the list based on the features they offer, considering the platform’s usability, integration capabilities, customization options, mobile access, and any other relevant functionalities. 

Price plans, hidden fees, customer reviews, and customer support are also assessed in the selection process. TechnologyAdvice writers will often take advantage of free trials and demos to get a first-hand user experience of available software. Finally, we curate a comprehensive list based on the previously stated factors, ensuring readers have the necessary tools to make an informed decision.

We rely on an internal algorithm to calculate star ratings, which are based on many factors.  

My research focused on your top-of-mind concerns like price, core and advanced features, and user experience. Below is a breakdown of the categories I used to grade each platform. Each category also includes my expert score, which contributes to its overall score.

​​As the market changes, we reevaluate our choices so you always receive the best insight for your purchasing decision.

User reviews:

User reviews from third-party software platforms like Capterra and G2 accounted for a small portion of the software’s overall score. I focused on software that received at least 3.5 out of 5 stars on these sites. Because users have real-world experience with each platform, they played a significant role in narrowing down my list of top products to compare.

Pricing: 

Software with transparent pricing, discounts, free trials, and free plans received the highest scores. I also considered each platform’s “value for money,” which determines whether the number of features in each price tier or module is competitive with other vendors in the space.

Platform and interface: 

How easy is the platform to use? Will it be able to grow with me? What is the customer service like? And will employees’ data be safe? This category covers the intrinsic features of most software platforms and vendors, including how well they will integrate with your current tech stack. 

Core and advanced features: 

Crucial capabilities including contact management, task automation, lead management, and more constitute one of the sections of our scoring rubric. More advanced tools such as AI analytics and chatbots, multi-channel support, and VoIP capabilities are also analyzed and scored. 

Integration and compatibility:

In this category of our CRM rubric, we evaluate how seamlessly a CRM system can mesh with your existing technological ecosystem.

We evaluate API availability, checking for an open and accessible API that allows for custom integrations, crucial for adapting the CRM to specific business needs. The availability of pre-built integrations with common business tools and platforms is also assessed. We consider the ease of data import/export, a key factor for data management. E-commerce compatibility is analyzed, especially important for businesses engaged in online sales. Email integration level is scrutinized, as seamless email integration is vital for effective communication and marketing. Lastly, we assess the level of social media integration, reflecting the growing importance of social media in business operations.

HubSpot Sales Hub – Best for integrations

• Higher-end cost structure for paid plans and added tools
• Best for use cases that need more than just CRM software

Our Rating: 4.5/5

• Contact management : HubSpot Sales Hub provides a detailed view of contacts, tracking every interaction automatically and allowing users to easily manage customer relationships.
• Email tracking and notifications : This feature enables real-time notifications when a contact opens an email, allowing sales teams to follow up effectively.
• Pipeline management : HubSpot’s CRM offers a visual dashboard for managing sales pipelines, helping teams to track deals and stages effectively.
• Meeting scheduling : Sales Hub provides a tool to simplify meeting scheduling by syncing with your calendar and allowing contacts to book time directly.

Price: $0/month Features:

Contact management

• Deal pipeline
• Meeting scheduling

Starter Plan:

Price: $20/month per seat

Everything in Free, plus:

• Sales automation
• Sales content analytics
• Task queues

Professional Plan:

Price: $100/month per seat

Features:  

Everything in Starter, plus:

• Prospecting and lead management
• Forecasting
• Custom Reporting
• Sales Analytics

Enterprise Plan:

Price: $150/month per seat

Everything in Professional, plus:

• Custom objects
• Predictive lead scoring
• Conversation intelligence
• Recurring revenue tracking
• Deal Journey Analytics

For more detailed pricing and features, visit HubSpot Sales Hub Pricing.

HubSpot Sales Hub is recommended as the best CRM for integration options due to its unparalleled ease of integration, free offerings, and a user-friendly platform that simplifies complex processes. It’s the ideal choice for businesses that value a cohesive, interconnected suite of tools to streamline their operations.

HubSpot’s CRM earned near-perfect scores across the board, boasting a 95.83/100 for both API availability and integration ease, and a 100/100 for the breadth of native add-ons and third-party integrations. 

The ‘no contract required’ policy and free plan make the platform a rarity among competitors. This approach democratizes access to powerful CRM tools, and makes it an attractive option for businesses of all sizes.

While Salesforce is often lauded for its extensive feature set and scalability, HubSpot Sales Hub is the go-to for businesses prioritizing ease of integration and user-friendly experiences. It’s particularly well-suited for companies that leverage a wide array of tools and require a CRM that can integrate with them.

Pricing: 4.8/5

General features and interface: 4.4/5

Core features: 4.5/5

Advanced features: 3.8/5

Integration and compatibility: 4.8/5

Over time, HubSpot has significantly expanded its integration capabilities, moving from a marketing-focused tool to a comprehensive CRM platform. This evolution is reflected in its impressive score of 89.29/100 for contact management and sales pipeline features, indicating a mature and well-rounded product.

Zoho CRM – Best for decentralized teams

• Free/low-cost plans have reduced functionality

• AI-Powered Sales Assistant (Zia) : Zoho CRM offers an AI-powered sales assistant called Zia that can predict trends, anomalies, and conversions, making sales forecasting smarter.
• SalesSignals : This feature provides real-time notifications from across multiple channels like phone, email, social media, and live chat, ensuring you never miss an interaction.
• Blueprint : Zoho’s Blueprint feature helps businesses design and automate their sales processes, ensuring that salespeople know exactly what to do at each stage of the deal.
• Multi-Channel Support : Zoho CRM offers multi-channel support for phone, email, live chat, social media, and in-person meetings, keeping your team connected no matter how you communicate.

Free Edition:

Price: $0 Features: Basic CRM functionalities Limited to 3 users Lead and contact management

Standard Edition: Price: $14/user/month (billed annually) Features: Sales tracking Custom dashboards Multiple pipelines Sales forecasting Social media management Professional Edition: Price: $23/user/month (billed annually) Features: Everything in Standard, plus: Advanced customizations Process management

Enterprise Edition: Price: $40/user/month (billed annually) Features: Advanced customization Territory management More extensive analytics and business intelligence tools AI-driven sales insights

Ultimate Edition: Price: $52/user/month (billed annually) Features: All Enterprise features, plus: Higher storage Enhanced customization Advanced automation capabilities

For more detailed information on pricing and features, visit the Zoho CRM Pricing page.

Zoho CRM is best for decentralized teams because it delivers a combination of flexibility, connectivity, and comprehensive features that are unmatched in the market. Its high scores in collaboration tools and mobile app functionality are not just numbers—they reflect a real-world efficacy that decentralized teams can rely on.

Zoho CRM scores an impressive 96/100 in advanced features, with perfect scores in multi-channel support and collaboration tools, essential for decentralized teams. Its ability to provide customer geo-location features and VOIP support further cements its position as the go-to CRM for teams operating remotely.

Additionally, Zoho offers a more seamless experience across all devices compared to competitors.

Zoho’s AI-powered Sales Assistant, Zia, offers smart sales forecasting, crucial for coordinating dispersed teams. SalesSignals keeps teams in sync with real-time, multi-channel notifications. Blueprint guides remote teams through each deal stage, providing process clarity. Zoho’s unique Multi-Channel Support covers everything from email to social media, ensuring all team interactions are captured, irrespective of location.

Pricing: 4.2/5

General features and interface: 4.3/5

Core features: 4.8/5

Advanced features: 4.4/5

Integration and compatibility: 5/5

Zoho CRM is part of a larger platform that includes HR, accounting, operations, and more, placing it in a similar bracket to NetSuite. It’s accomplished this while still maintaining low overhead costs, thanks to its remote-first work environment. 

That prioritization of decentralized collaboration carries over to their suite of software solutions, including their CRM. With features and tools comparable to its peers in this list, but with the added bonus of designing the platform to function across time zones and national boundaries.

The software is tailored to meet the needs of businesses of all sizes, with a free plan and no contract requirement. Its pricing structure is competitive, offering value for money and reflecting an understanding of diverse business models.

Zoho CRM’s interface is intuitive, scoring a 75/100 in design, and it supports a mild learning curve, ensuring new users can quickly adapt. The platform’s customizability is a standout feature, allowing businesses to tailor the CRM to their unique processes.

In terms of product design, Zoho CRM has been crafted with the user in mind, offering a balance between functionality and simplicity. This balance is critical for user adoption and long-term engagement with the platform.

Zoho has consistently incorporated user feedback to improve its offerings, demonstrating a commitment to growth and improvement that benefits its users directly.

Shape CRM – Best for flexibility

• eSignature functionality could be improved
• Higher price point

Our Rating: 4.4/5

Lead engine offering landing pages, lead distribution, automated scheduling, and more.

Built-in phone dialer with call tracking, live monitoring, and intelligent call routing.

AI chatbot , transcription assistant, and text and image generator.

$99/month when billed annually.

Shape CRM’s design philosophy centers around user-driven customization. This approach is evident in its intuitive interface, which allows users to easily modify and adapt the platform to their specific needs, enhancing the overall user experience beyond standard CRM offerings.

It offers a compelling blend of industry-specific features, making it a top pick for businesses with specialized needs. And, it has all the bells and whistles. Scoring high in advanced features (not an easy feat), it offers a dedicated dialer that can be considered a full VoIP system, an AI chatbot and assistant, and geo-location integration options.

Its flexibility also makes it a top choice for specific industries. Unlike many CRM systems that offer rigid, one-size-fits-all solutions, Shape CRM provides a highly customizable platform.

Pricing: 4.4/5

General features and interface: 4.1/5

Core features: 4.6/5

Integration and compatibility: 4/5

Shape CRM has carved a niche for itself by offering highly customizable solutions tailored to various industries. Originating as a tool to simplify complex business processes, it has evolved to include features like AI lead scoring and bi-directional texting. The platform is lauded for its scalability and extensive features.

Recently, they’ve added bulk texting capabilities and enhanced payment options, further streamlining business communication and transactions. However, the cost could be a consideration for smaller enterprises.

One of the few gripes I have about Shape is its pricing structure. At $99/month if billed annually, it is on the higher end per user. However, the platform does include about everything a sales team needs. A tiered pricing structure in the future could fix this, allowing customers to choose their optimal level features for the price.

Pipedrive – Best for pipeline management and optimization

• Not as valuable for teams with existing, well-refined lead pipelines

Our Rating: 4.2/5

• Pipeline management : Pipedrive offers a visual sales pipeline which allows users to effectively manage deals at different stages and streamline the sales process.
• Sales reporting : Pipedrive includes comprehensive sales reporting features to provide insights and monitor sales performance over time.
• Email integration : Pipedrive provides seamless email integration, allowing users to send and receive emails directly from the CRM, track correspondence and automate follow-ups.
• Activity and goal tracking : Pipedrive allows users to set and monitor goals and activities, keeping sales efforts aligned with business objectives.

Free trial available

Essential: $14/user/month

Advanced: $29/user/month

Professional: $49/user/month

Power: $64/user/month

Enterprise: $99/user/month

Pipedrive is best for businesses that prioritize sales process optimization. Its intuitive design, combined with powerful automation and analytics, makes it an ideal tool for sales teams to streamline their workflows.

Pipedrive’s visual sales pipeline breathes life into deal progression, streamlining the sales process in a digestible, visual format. Complemented by comprehensive Sales Reporting, users glean valuable insights into performance trends. With seamless Email Integration, tracking correspondence and automating follow-ups becomes effortless. Pipedrive’s Activity and Goal Tracking further keep sales efforts aligned, optimizing pipeline progress. For businesses seeking a clear view and control over their sales pipeline, Pipedrive has the right formula, making it a top recommendation in the CRM domain.

Pipedrive’s overall score of 83.3 out of 100, with a 4.2-star rating, is a testament to its balanced performance across various criteria. It excels in core features like sales pipeline (100), task automation (100), and email integration (100), which are pivotal for effective CRM functionality.

Compared to other CRMs like Salesforce or HubSpot, Pipedrive is more accessible and less complex, making it ideal for small to medium-sized businesses or teams that require a straightforward, efficient sales process without the need for extensive customization or complex integrations.

Pricing: 3.5/5

Advanced features: 3.5/5

Pipedrive is also a bit of an oddity in the CRM and sales software space, in all the best ways. Pipedrive is designed to help teams make the most of their leads, and focus on the ones most likely to convert. It does this through a variety of non-conventional CRM capabilities, including AI analytics, intelligence software, and prescriptive data insights. 

The interface is also carefully crafted to present the most relevant information in a visual, easy-to-digest manner. Properly implemented, Pipedrive can dramatically reduce the number of leads that slip through the cracks, and the number of dead ends chased by sales staff.

Recent additions include improved AI analytics and more third-party integrations.

For more information on Pipedrive, check out Pipedrive Product Updates and Pipedrive Community .

Salesforce Sales Cloud – Best for enterprises

• Mid-to-high pricing
• Complex implementation

Our Rating: 4/5

• 360-degree customer view : Salesforce’s Sales Cloud brings together every interaction and piece of customer data, presenting a complete view of each client’s journey. This holistic perspective enables businesses to understand their customers better, enhancing relationship-building.
• Einstein AI-powered analytics : Harnessing the power of artificial intelligence, Salesforce’s Einstein AI delivers insightful predictive analytics. This technology provides proactive lead scoring, trend analysis, and accurate sales forecasts, driving strategic, data-informed decision-making.
• Extensive customization and integration : Salesforce is lauded for its vast customization capabilities. It offers a range of modules and a flexible API for seamless integration with other tools, ensuring Salesforce adapts to your specific business needs and tech stack, not vice versa.
• Scalability and cloud-based structure : Salesforce is built with scalability in mind, making it suitable for businesses of all sizes. Its cloud-based architecture enables secure, anywhere-access to your CRM data, facilitating collaboration and boosting productivity across your team.

Essentials: $25/user/month

Professional: $75/user/month

Enterprise: $150/user/month

Unlimited: $300/user/month

*All plans billed annually

Salesforce is recommended as the best CRM for enterprises primarily due to its scalability, ecosystem, and innovation. It’s a platform that grows with your business and encourages it to grow, offering a suite of tools that are always at the forefront of CRM technology.

In evaluating Salesforce against its competitors, it scores exceptionally high in customization, integration capabilities, and scalability. Its user interface is fairly intuitive, and its customer support is strong (and gets stronger at higher tiers). 

What sets Salesforce apart is its ecosystem. Unlike competitors that offer a static solution, Salesforce provides a dynamic platform that grows with your business. Its AppExchange marketplace boasts thousands of third-party applications, allowing for significant customization and extension.

While other CRMs like Oracle are formidable in their own right, offering deep analytics and a suite of tools, Salesforce distinguishes itself with its cloud-first approach, providing more frequent updates, a larger app marketplace, and a more extensive community of users and developers.

Pricing: 2.7/5

General features: 4.5/5

Advanced features: 4.5/5

Integration and compatibility: 3.5/5

Salesforce CRM, included in Salesforce Sales Cloud, has been a titan in the enterprise arena for years, and it has largely gained its clout on merit. Its comprehensive suite of features, scalability, and innovative approach to customer relationship management all serve to make it a strong contender in the space. 

While Salesforce’s comprehensive features and customization options are a boon for many large-scale businesses, they can also present a steep learning curve for new users. The platform’s depth, which allows for extensive tailoring, may require significant training and expertise to navigate effectively. This could lead to additional costs for organizations in terms of time and resources spent on training.

Integration capabilities are a strong point for Salesforce, offering connections with a multitude of business applications. Yet, this interconnectedness can sometimes lead to complexity, particularly when managing and troubleshooting integrations across various systems.

The user experience is generally user-friendly and is regularly updated to introduce new functionalities. Nonetheless, some users may find the frequent updates challenging to keep up with, potentially disrupting workflows as teams adapt to new features.

Salesforce’s AI enhancements with Einstein and mobile experience improvements are commendable, positioning the platform at the forefront of innovation, but remember the cost associated with accessing these cutting-edge tools should be considered.

Oracle NetSuite CRM – Best all-in-one solution

• Higher-end pricing
• No standalone CRM option

• 360-Degree customer view : Provides a comprehensive, real-time view of customer interactions across all channels, enhancing customer insight and service.
• Sales force automation (SFA) : Automates and streamlines the entire sales process, reducing errors and saving time.
• Marketing automation : Enables businesses to streamline campaign management and track marketing ROI effectively.
• Customer service Management : Enhances customer satisfaction by providing tools for managing customer support and improving service delivery.

Starting from $499/user/year

Oracle NetSuite CRM is as comprehensive as it gets. It’s a remarkably versatile tool, particularly for businesses seeking an all-in-one cloud solution that integrates CRM with financials, e-commerce, and more.

This powerhouse, cloud-based solution provides a 360-degree customer view, enabling businesses to see a full spectrum of customer interactions in real-time. Its advanced features, particularly in AI analytics and collaboration tools, make the platform stand out and clinch a spot in the 4 star range.

Compared to more specialized CRMs like Salesforce or Pipedrive, Oracle NetSuite CRM offers a more holistic approach to business management. It’s particularly well-suited for businesses that require not just CRM functionality but also integrated financial and e-commerce capabilities.

Pricing: 2.5/5

General features: 3.8/5

Advanced features: 4/5

Rounding out the 4-star range on our list is NetSuite CRM from Oracle. If that name sounds familiar, it should: NetSuite was an internet pioneer back when the internet still ran on dial-up. These days, the brand (now owned by Oracle) continues to offer advanced software and digital technology solutions to businesses around the world. 

NetSuite is noteworthy for numerous reasons, but above all else, they are a comprehensive solution. The CRM portion is only a fraction of the platform—the suite handles enterprise resource planning (ERP), accounting and financials, inventory tracking, and more. Organizations looking to use a single system to manage and optimize their entire business infrastructure need look no further.

The overall design of Oracle NetSuite CRM also focuses on providing a comprehensive view of the customer lifecycle. While its interface and usability score moderately, the CRM’s strength lies in its ability to offer a 360-degree view of customers, integrating sales, customer service, and marketing effectively.

This evolution into a comprehensive ecosystem addresses the growing need for interconnected business systems, offering a more efficient and unified approach to managing various business processes.

That said, its greatest strength is also its biggest caveat. NetSuite is a package deal. Signing up for the CRM (or any individual business function) means signing up for the whole platform, so it works best for brands that stand to benefit from an entire workflow overhaul.

Insightly CRM – Best for easy-to-read analytics and reporting

• Not as intuitive or easy to deploy as some tools

Our Rating: 3.9/5

• Advanced reporting : Insightly’s advanced reporting allows users to create customized, detailed reports, offering valuable insights into business performance.
• Dashboard visualization : Insightly provides dashboards for a quick visual representation of business health and metrics, aiding in swift decision-making.
• Sales forecasting : Insightly’s CRM includes sales forecasting features, enabling businesses to predict sales revenue using historical and real-time data.
• Integrations : Insightly integrates with popular software like Power BI and Excel, enhancing its reporting and analytics capabilities.

F ree trial available

Plus: $29/user/month

Insightly’s design philosophy in its reporting and analytics features emphasizes simplicity and clarity. The platform allows users to easily create and customize reports, offering an experience that reduces the complexity often associated with data analysis.

Insightly pairs an easy-to-use interface with customizable reporting tools, making it ideal for businesses that need detailed insights without the complexity of more advanced systems. This makes Insightly particularly suitable for small to medium-sized businesses or those with limited resources for data analysis.

Despite the lower price tag compared to other software offering similar tools, it still boasts advanced reporting, allowing users to generate custom, in-depth reports revealing key business performance indicators.

Its dashboard visualization serves up crucial metrics at a glance, aiding quick, informed decisions. Coupled with precise sales forecasting, Insightly paints an accurate picture of future sales revenue. Plus, with smooth integration with platforms like Power BI and Excel, Insightly’s analytics game is seriously amplified.

For businesses that prioritize ease of use and straightforward data visualization, Insightly is a more suitable choice than Salesforce, which, while powerful, can be overwhelming for users new to CRM analytics. HubSpot, on the other hand, offers similar user-friendliness but may not match Insightly’s depth in customization options for reports.

General features and interface: 3.6/5

Core features: 4.3/5

Advanced features: 2.3/5

Integration and compatibility: 4.2/5

Insightly might be a brand name you’re unfamiliar with, but rest assured, it has a quiver of features that’s just as full as some of the bigger names on this list. Automation options, project management, and plentiful integration options are just the beginning. What’s not a common bragging right is their robust analytics and reporting capabilities.

In many ways, Insightly doubles as business intelligence, providing extensive flexibility and functionality regarding data discovery and scrubbing; organizing and visualization; powerful analytics insights; and intuitive reporting options. 

In recent years, Insightly has even enhanced its reporting and analytics features, focusing on providing more customizable reporting options and integrating AI-driven insights.

However, compared to competitors like Salesforce or Oracle NetSuite, Insightly’s advanced features like AI analytics and VOIP capabilities could be further developed. While it offers a solid foundation in CRM functionalities, expanding these advanced features could enhance its appeal to larger businesses or those with more complex CRM needs.

Nevertheless, for teams that want to dig deep into the data, find and leverage customer trends, and otherwise prove the value that the sales pipeline has to offer the organization as a whole, Insightly should be at the top of the shortlist.

Keap CRM – Best for sales-marketing synergy

• May not be the right fit for larger, more complex workflows

Our Rating: 3.8/5

• Smart client management : Keap’s CRM excels in organizing and updating client information, along with capturing new leads, all in one centralized, searchable database.
• Advanced email automation : Keap’s CRM not only offers email marketing but also delivers personalized, automated follow-ups that nurture client relationships and keep businesses top-of-mind.
• Integrated appointments : Keap seamlessly syncs calendars and sets up reminders, turning the scheduling of appointments with clients into a hassle-free process.
• Billing and payment solutions : Keap stands out with its invoicing features, which provide the ability to create, send, and track invoices, accept credit cards online, and automate payment reminders.

Pro: $159/2 users/month

Max: $229/3 users/month

Keap CRM’s overall performance in sales-marketing synergy is impressive, scoring particularly high in core features (89.3 out of 100) and integration and compatibility (91.7 out of 100).

For small to medium-sized businesses looking for a CRM that offers both simplicity and powerful sales-marketing integration, Keap is a more suitable option than Salesforce, which might be overwhelming in complexity, and HubSpot, which may lack some of Keap’s specialized functionalities.

The platform’s automation and integration capabilities have also improved in recent updates.

General features and interface: 4/5

Advanced features: 2.5/5

Integration and compatibility: 4.6/5

Keap CRM is a bit of an oddity in this list. While it’s fully capable of providing top-tier service to larger brands the way Salesforce or Hubspot might, its positioning tends to focus more heavily on solopreneurs and other small businesses.

To be fair, these are often the professionals who need the most support to keep their sales pipelines flowing. Certain “hats” have to be worn in every business—accounts receivable, sales, marketing, operations, logistics, project management, etc.—even when that business is a business of one. The fewer the heads there are in the crew, the more hats on a single head.

Keap aims to help these professionals by giving them somewhere else to put some of those hats, so to speak. Via robust sales, marketing, and financial automation tools , Keap streamlines some of the most difficult, tedious, and repetitious parts of the sales lifecycle.

Solopreneurs especially tend to be specialists in their product, with non-billable tasks being secondary skills at best. Keap gives these individuals and teams a way to get back to the work they do best, and rest easy about the duties that stress them out the most.

While Keap excels in many areas, it could further strengthen its position against competitors by enhancing its AI analytics and AI chatbot functionalities. These improvements would provide even more sophisticated tools for businesses to analyze data and engage with customers, keeping pace with the evolving trends of competitors.

Copper CRM – Best for security

• Fewer non-Google integrations
• Not optimal for organizations built on Office 365

• Google workspace integration : Copper CRM integrates seamlessly with Google Workspace, allowing users to manage contacts, deals, and tasks directly from Gmail or other Workspace apps.
• Sales pipeline management : This feature provides a visual sales pipeline that makes it easy to manage opportunities and track progress towards sales goals.
• Automated data entry : Copper CRM uses automation to reduce the burden of manual data entry, automatically populating contact and company details.
• Lead and opportunity management

Basic: $23/user/month

Professional: $59/user/month

Business: $99/user/month

Larger, more established organizations often rely on Microsoft’s suite of apps and platforms to ensure secure collaboration. But Google Workspace is often a much more familiar space for newer brands with younger staff (many of whom used Gmail and Gsuite for their personal accounts). And Copper CRM is unmatched when it comes to Google Workspace interoperability.

Copper CRM distinguishes itself from competitors like Salesforce and Zoho CRM with its deep integration with Google Workspace, offering enhanced security features that are particularly beneficial for businesses heavily reliant on Google services. Unlike Salesforce, which offers a broader range of features, Copper focuses on providing a secure, Google-centric user experience.

Copper CRM’s design is focused on providing a secure yet user-friendly experience. Its integration with Google Workspace enhances security and ensures a seamless user experience, reducing the learning curve and increasing adoption rates.

Depending on your use case, your current workflow, and the level of tech literacy in your organization, Copper CRM could very well be the fastest time-to-value option available.

Pricing: 3.3/5

Copper excels in seamless pipeline management, offering a visually appealing and intuitive platform to track progress and manage sales goals. Its automated data entry feature expedites processes, eliminating the hassle of manual inputs. The platform shines in lead and opportunity management, ensuring no prospect falls by the wayside. With its firm foothold in Google’s ecosystem, Copper CRM stands as a paragon of integration, streamlining operations in a way that feels native to Gmail and Google Workspace users. A true champion for CRM automation and user convenience.

To place higher on the list, it could further enhance its competitive edge by improving its AI analytics and AI chatbot functionalities. These advancements would provide businesses with more sophisticated tools for data analysis and customer engagement.

ClickUp – Best for project management

• Requires a bit more training and onboarding to implement

• Task and project management : ClickUp excels in organizing tasks, allowing you to create, assign, prioritize, and track tasks all within a single platform.
• Collaboration detection : ClickUp’s unique feature helps avoid duplicate work by showing who’s working on what in real-time.
• Goal tracking : With ClickUp, you can set, track, and achieve goals across different teams and projects, ensuring everyone is aligned and focused.
• Custom views : ClickUp allows you to customize how you view your tasks and projects, whether that’s in a list, board, box, calendar, or Gantt chart view.

Free plan available

Unlimited: $5/user/month

Business: $12/user/month

Business Plus: $19/user/month

Enterprise: Contact ClickUp for a customized quote

ClickUp, primarily known for its project management capabilities, has ventured into CRM functionalities.

Unlike specialized CRM tools like Salesforce or HubSpot, ClickUp integrates CRM features within a broader project management framework. This integration offers a unique advantage for teams seeking a unified platform for managing tasks, projects, and customer relationships.

ClickUp’s robust Task and Project Management tools bring a fresh perspective to CRM , prioritizing organization and tracking. The Collaboration Detection feature ensures that no work is duplicated, vital in customer relationship management. Goal Tracking aligns teams with overarching objectives, and Custom Views offer flexibility in visualizing client interactions. ClickUp is breaking down the silos, showing that project management and CRM can coexist and flourish in one platform, making it an unexpectedly fitting CRM choice.

Pricing: 4/5

Core features: 4.1/5

Advanced features: 2/5

This vendor may come as a bit of a surprise, as ClickUp is primarily known as a project management platform. That reputation is well earned (and why it takes the title it does in this list). ClickUp also has a CRM platform, one that fills many of the needs discussed here. 

ClickUp’s foray into CRM features is a recent development, evolving from its core strength in task and project management.

ClickUp already has a noteworthy share of the PM market, and picking up the CRM module can amplify the amount of benefit those teams get out of the vendor. Seeing as most other major brands in the PM space cost significantly more, it’s an excellent way to get powerful software tools without paying enterprise-level subscription fees.

While ClickUp offers excellent task management and customization, its CRM features could benefit from more advanced sales tracking and marketing automation tools. Enhancing these aspects would make ClickUp a more comprehensive solution for businesses looking for an all-in-one platform for project management and customer relationship management.

Less Annoying CRM – Best for companies on a budget

• Limited pre-built functionality
• Less robust integration library

Our Rating: 3.5/5

• Simplicity : Less Annoying CRM prides itself on its user-friendly design that makes it easy for small businesses to manage contacts and track leads without unnecessary complexity.
• Customizability : The platform allows businesses to tailor the CRM to their specific needs, with customizable fields, layouts, and reports.
• Collaboration features : The system is designed for team collaboration, allowing multiple users to share contacts, calendars, and notes.
• Affordability : The CRM offers straightforward and low-cost pricing, making it an accessible solution for small businesses.

Free tria l available

$15/user/month

In the grand scheme of CRM software, Less Annoying didn’t measure up too well. But that is okay—it’s all about use case. Less Annoying CRM isn’t designed to offer all the advanced features of Salesforce of Netsuite; it’s perfectly happy providing a simple CRM at a simple price. In fact, it does that better than most of the other simple CRMs out there. That’s why it rounds out our list.

Celebrated for its simplicity, this CRM caters specifically to small businesses, demystifying CRM with a user-friendly design. Customizability allows the platform to mold to unique business needs, with bespoke fields and layouts. Its collaboration features foster teamwork, seamlessly sharing contacts and notes. Plus, with its straightforward, affordable pricing, Less Annoying CRM has carved a niche in providing a high-quality, budget-friendly CRM solution. In the ever-complex CRM jungle, Less Annoying CRM emerges as a refreshing antidote.

General features: 3.3/5

Core features: 3.9/5

Advanced features: 1.5/5

Integration and compatibility: 3.3/5

With a low per-user cost, and intuitive functionality, it’s a perfect choice for teams that don’t have complex criteria for a CRM or sales software vendor to meet. 

If you’re looking to deploy quickly, onboard staff with minimal training, and want to limit your cloud software overhead, Less Annoying CRM fits the bill. And while the list of available customizations and pre-built integrations is smaller than other CRMs in this list, there are no contracts or limitations, making this CRM a strong choice for up-and-coming brands.

Learn more about the best Simple CRM options here .

Find your new CRM software

Key crm features to consider for your use case.

While there are certainly software solutions that are objectively “bad,” the majority of options in any given vertical will be valued by how well they satisfy the intended use case. Trying to apply the tool outside the parameters it was designed for may fail to meet expectations, but that does not necessarily mean the software itself is of poor quality. It’s just a bad match.

Below are some core areas of concern for CRM consumers to consider when shopping around to help them find a tool that aligns with their needs.

ALSO READ: The Different Types of Roles & Responsibilities in a CRM

For CRM tools, the most foundational functionality is that of collecting and organizing contact information. At the very least, it needs to be a step up from simply dumping leads into a spreadsheet only to immediately be forgotten. Spreadsheets have their place, but they aren’t optimized for automation or to serve as living records. If a CRM can’t improve on manual data entry, manual data scrubbing, and manual retrieval, then it’s just Excel with extra steps.

Contact management features in CRM and sales software, it should be noted, vary widely from system to system. But the common upgrades to functionality will likely look familiar to anyone who’s been using digital devices in the past two decades:

• Automated data importing
• Data export flexibility (via EDI, CSV, or other formats)
• Integrations with other platforms, apps, and databases
• Filters and search functions
• Analytics and reporting functions

Communication records

By now, nearly everyone is familiar with the “This call may be recorded for quality assurance purposes” line spoken to customers calling in to a business for support. Having a record of customer/client/lead interactions can be indispensable. And not just for maintaining excellent service , either. 

Using recorded calls, chats, and other communication, businesses can achieve a multitude of important objectives, including:

• Improving effectiveness of staff training, onboarding, company policies, and more
• Compiling data for analytics (which can help identify patterns, predict trends, and even recommend strategies)
• Providing evidence to help navigate legal concerns and protect against loss

If benefits like these factor into your CRM and sales software choices, then be aware that some software includes functionality to support it, while some does not. 

Integrations

Implementing new software systems can be difficult, and onboarding users only becomes more problematic when the system doesn’t play well with existing solutions in the workflow. 

Some CRMs are built as part of a larger platform of business solutions, and are intended to be used as a holistic unit. This is a bit of a trade-off, and some cost-benefit evaluations will be needed to determine if a complete workflow overhaul will net positive or negative returns for the trouble. 

However, if maintaining the stability and functionality of other systems is of equal importance, then finding a CRM that can integrate with them successfully. Depending on what integrations are needed, there may be plug-ins or add-ons available already to users of the tool. 

For less common integrations, some more code-heavy API customization may be necessary. In these cases, it’s a good idea to ask which side of the client-vendor partnership will be building the integration. 

What should be avoided is the addition of a CRM that only further complicates workflows and requires additional manual processes to hold the system together. 

Marketing automation

Marketing automation is a specific example of the integration concerns above, but it merits its own spot on this list due to the amount of overlap between marketing and sales efforts . Like other sales-adjacent functions, there are a lot of advantages to having the CRM trigger automatic tasks such as sending confirmation emails, email nurture campaigns, and more.

Worst-case scenario, similar to what’s mentioned above, is that the new CRM complicates already existing processes, rather than integrating or replacing them. If your CRM or sales software adds another step in the process of, for example, sending outreach emails because staff members have to manually dig for contact info in the database, that’s a net loss.

At the very least, the CRM should leave any existing processes intact and unhindered. Most likely, however, your organization will benefit from some form of integration, or a CRM that includes marketing automation features natively. 

Quotes and invoicing

Next in line for important functions in the sales process that may or may not be handled by sales team members are financials, such as quotes, invoices, and other AP/AR responsibilities. The same philosophy applies here as above. If an all-in-one solution upgrades your workflow, prioritize that aspect in your research.

If integration will suffice, then double-check with vendors for the specific platforms you need to be interoperable. Even if your current process is efficient enough currently, be sure that the CRM doesn’t create additional headaches for anyone in the workflow. 

Data privacy

Cybersecurity is, arguably, a priority for every organization (or, at least it should be). Some verticals deal with higher risk than others, however, and may need additional security protocols for their CRM to protect themselves or even to comply with industry mandates.

This is one that may require consulting with IT, InfoSec, or other I&O staff in the organization. Many of the technical details that factor into whether or not a CRM is sufficiently secure may be outside the expertise of anyone without a background in computer systems, and it’s not one to leave to chance. 

Check with internal SMEs, and leverage their experience to further vet your shortlist of CRMs. It may even be worthwhile to have a 3rd-party vendor risk assessment done for any that meet all other criteria, just to cover your bases.

Project management

This list of supplemental functions that might be relevant would be pretty long if it were comprehensive. All-in-one systems, analytics and reporting, ease of use, pipeline management, and many more might fit here. Many of these have been touched on above, and others are likely known quantities already.

So instead, this part of the list will finish with one final consideration: project management. 

Organizations that have, until now, used less formalized workflow processes may not realize how much of an upgrade a well-designed project management strategy can be. As teams grow, workloads become heavier, and processes become more intricate and complex, tracking things on paper proves a substantial challenge. 

With effective implementation, project management tools can provide visibility and accountability across the board. Better still, it can help staff achieve greater levels of autonomy by giving them the tools needed to stay organized and on top of their responsibilities. 

Some CRM and sales software tools include project management (PM) features in their toolset, while others can integrate with popular platforms via APIs. Either way, for any team larger than a handful of employees, it’s worth discussing the potential value a CRM with PM enablement might bring to the table. 

Choosing the right CRM and sales software

Picking the right software, let alone the right CRM and sales software, is a far cry from guesswork. It takes extensive research and vetting of available options, which can drag the process out for weeks or months.

Our Recommendations

• Best Small Business Loans for 2024
• Businessloans.com Review
• Biz2Credit Review
• SBG Funding Review
• Rapid Finance Review
• 26 Great Business Ideas for Entrepreneurs
• Startup Costs: How Much Cash Will You Need?
• How to Get a Bank Loan for Your Small Business
• Articles of Incorporation: What New Business Owners Should Know
• How to Choose the Best Legal Structure for Your Business

Small Business Resources

• Business Ideas
• Business Plans
• Startup Basics
• Startup Funding
• Franchising
• Success Stories
• Entrepreneurs
• The Best Credit Card Processors of 2024
• Clover Credit Card Processing Review
• Merchant One Review
• Stax Review
• How to Conduct a Market Analysis for Your Business
• Local Marketing Strategies for Success
• Tips for Hiring a Marketing Company
• Benefits of CRM Systems
• 10 Employee Recruitment Strategies for Success
• Sales & Marketing
• Social Media
• Best Business Phone Systems of 2024
• The Best PEOs of 2024
• RingCentral Review
• Nextiva Review
• Ooma Review
• Guide to Developing a Training Program for New Employees
• How Does 401(k) Matching Work for Employers?
• Why You Need to Create a Fantastic Workplace Culture
• 16 Cool Job Perks That Keep Employees Happy
• 7 Project Management Styles
• Women in Business
• Personal Growth
• Best Accounting Software and Invoice Generators of 2024
• Best Payroll Services for 2024
• Best POS Systems for 2024
• Best CRM Software of 2024
• Best Call Centers and Answering Services for Busineses for 2024
• Salesforce vs. HubSpot: Which CRM Is Right for Your Business?
• Rippling vs Gusto: An In-Depth Comparison
• RingCentral vs. Ooma Comparison
• Choosing a Business Phone System: A Buyer’s Guide
• Equipment Leasing: A Guide for Business Owners
• HR Solutions
• Financial Solutions
• Marketing Solutions
• Security Solutions
• Retail Solutions
• SMB Solutions

11 Benefits of CRM Systems

CRM software can provide a wealth of benefits for your small business, from customer retention to increased productivity.

Table of Contents

Customer relationship management (CRM) software has become a near-vital tool for businesses of all sizes. CRM software can provide several benefits to any business, from organizing contacts to automating key tasks. It can also be a centralized, organized hub that enables consistent communication both with customers and within the organization. This is especially important as more organizations shift to remote work.

The CRM software market was worth more than $58 billion in 2022 and is currently one of the fastest-growing industries, projected to grow at a rate of 13.9% from 2023 to 2030, driven by consumer demand for better customer service, automated engagement and more nuanced customer experiences.

What is a CRM?

A CRM system is a type of software that helps businesses manage, track and organize their relationships with customers. CRM stands for “customer relationship management.” The CRM system can help you store customer data, such as user behavior, the amount of time a customer has been with your business, purchase records and notes on sales interactions. You can then use this information to optimize your sales and marketing processes and improve customer service across your organization.

“CRM … is a group of tools, technology and techniques used to help sales and marketing professionals understand their customers better,” said Bryan Philips, head of marketing at In Motion Marketing.

CRM software features track the behavior and actions of your current or potential customers through your business’s website, social media or email marketing campaigns and then guides the customer through the sales or buying funnel by sending a triggered email or alerting a sales representative of the customer’s interest.

CRM benefits for small businesses

A CRM solution can be used in various ways and provide numerous benefits to your business. In general, these benefits all involve nurturing leads and supporting customers as they enter and move through your sales and marketing funnels . Specifically, CRM systems can achieve the following to do so.

1. Better customer service

Modern CRM software has many functions, but the software was created to improve business-customer relationships and that’s still its main benefit. A CRM manages all of your contacts and gathers important customer information ― like demographics, purchase records and previous messages across all channels ― and makes it accessible easily to anyone in your company who needs it. This ensures that your employees have all they need to know about the customer at their fingertips and can provide a better customer experience, which tends to boost customer satisfaction.

2. Increased sales

A CRM tool can help you streamline your sales process, build a sales pipeline, automate key tasks and analyze all of your sales data in one centralized place, potentially increasing sales and productivity. A CRM helps you establish a step-by-step sales process that your employees can rely on every time and that you can tweak easily as issues arise.

3. Improved customer retention

Once you’ve procured and converted leads, it’s vital that you put in the work to retain them as customers and promote customer loyalty. High customer turnover can have many negative effects for your business, like diminished revenue or disrupted cash flow, so use your CRM and the information it provides about your customers to encourage repeat business. The CRM will provide sentiment analysis, automated ticketing, customer support automation and user behavior tracking to help you determine problems and quickly address them with your customers.

4. Detailed analytics

It’s one thing to have plenty of data about your customers, but you need to know what it means and how to use it. CRM software typically has built-in analytic capabilities to contextualize data, breaking it down into actionable items and easily understood metrics. Metrics, such as click-through rates, bounce rates and demographic information allow you to judge the success of a marketing campaign and optimize accordingly.  

5. Higher productivity and efficiency

CRM software uses marketing automation technology, which expedites menial tasks like drip campaigns and frees up your employees’ time to focus on work only humans can handle, like creating content. It can also ensure that no tasks slip through the cracks, such as all-important emails are always sent to the right people. Additionally, a CRM can show you a dashboard of how your business processes are working and where your workflows could improve . [Related article: How Workplace Automation Software Can Help Your Business ]

6. Centralized database of information

Another thing CRM software does best is providing a centralized database with all information on your customers, making it easily accessible to anyone in your company who needs it. This makes it easy for a sales representative to see what products a certain customer is interested in, for example. If the customer has previously interacted with the company, the CRM will include records of that interaction, which can inform future marketing efforts and sales pitches. This saves your employees the time of digging through old files and records, and it makes for a better and more productive experience for the customer.

7. Managed communications with prospective leads

Lead nurturing can be an arduous and complicated process, with many steps and opportunities to communicate. A CRM automatically manages the process, sending your employees alerts when they should reach out to the prospect and tracking every interaction, from emails to phone calls.

“One great advantage of [CRM] is that you can see your customer’s journey holistically,” said Michael Miller, CEO of VPN Online. “With every phase in the design and every email you sent out reviewed, you can quickly figure out the next move to make. It’s like seeing it from the top view and you can easily create a decision on what to do next.”

8. Improved customer segmentation

A list of hundreds of contacts can be unwieldy and overwhelming. For example, how do you know which customers want to see your email about your new in-store product? A CRM will segment your contact lists automatically based on your criteria, making it easy to find the ones you want to contact at any given time. You can sort contacts by location, gender, age, buyer stage and more.

“Automation actually allows the marketer to have a more meaningful understanding of the customer and have more valuable interaction when they do interact because of it,” Philips said. “The important part to understand about automation is that we don’t want to write a general email to our customers. Instead, we want to send emails reflecting customers’ preferences, interests and values by segmenting them into groups using the data gleaned within the CRM.” [Related article: Why Demographics Are Important in Marketing ]

9. Automated sales reports

Your team can collect and organize data about prospective and current customers easily using the CRM software’s dashboard and reporting features, which allow employees to automate and manage their pipelines and processes. The CRM can also help your team members evaluate their performance, track their quotas and goals and check their progress on each of their projects at a glance.

10. More accurate sales forecasting

With any business operation, you need to be able to review your past performance and strategically plan for the future. Using the automated sales reports in CRM software, you can identify key trends and get an idea of what to expect from your future sales cycle performance while adjusting your goals and metrics to suit those projections. [Related article: 7 Ways to Improve Your Sales ]

11. Streamlined internal communications

Aside from facilitating communication between your business and your customers, a CRM can make it easier for your employees to communicate with each other. A CRM makes it easy to see how other employees are speaking with a potential customer, which helps your team maintain a unified brand voice. It also allows team members to send each other notes or alerts, tag each other on projects and send messages and emails, all within one system. [Related article: Workplace Conflicts? 5 Tips to Improve Communication ]

What companies can benefit from CRM systems?

Because CRM software provides such a breadth of benefits, many types of businesses and teams can benefit from it.

“Not all customers are created equal, so the value of a CRM is that it helps you keep the right customers and deploy your precious marketing dollars towards the customers that will return the highest value over their customer lifetime,” said Mike Catania, CEO and co-founder of Locaris. “It is challenging for small businesses to identify and acquire customers, so bucketing them into optimal and suboptimal segments through CRM is inordinately valuable.”

Businesses of all sizes, from solo freelancers to enterprise-level corporations, can use CRM technology effectively. After all, the key functions of a CRM are organization, centralized task management, marketing automation and communication, which are important to every business’ success.

Of course, some businesses stand to gain more from the use of a CRM than others:

Businesses with a dedicated sales team

If you have a sales team, a CRM is vital to help you manage your contacts and your customer relations. A CRM can even help you improve and grow your sales processes by using customer information, showing you key trends and areas where you can improve your strategies and automating menial tasks for your sales representatives.

Businesses with a marketing team

CRM and marketing go hand in hand. CRM data can help your marketing team identify, capture, nurture and convert leads; track customer-salesperson interactions; monitor drip campaigns within the sales cycle and more. All of this can create a smoother and more consistent customer experience.

Businesses seeking to increase efficiency

Because CRMs automate processes like contact organization and communication, the software can significantly speed up daily processes and tasks for your entire team. A CRM can also reduce errors and ensure that all communications go out to the right people at the right time. 

CRM software can take your customer service to the next level

A CRM software is an indispensable tool for a business’ sales, marketing and customer support teams. By building detailed profiles of leads and customers and tracking all your team’s communications with them, you can better serve your existing customers, acquire new ones and refine marketing campaigns to reach your target audience effectively. If you want to better organize your communications and pull back the veil on key insights surrounding your leads and customers, consider implementing a CRM system for your business.

Tejas Vemparala also contributed to this article. 

Building Better Businesses

Insights on business strategy and culture, right to your inbox. Part of the business.com network.

Salesforce is closed for new business in your area.

Microsoft Dynamics 365 and Power Platform experts providing comparisons and opinions for the CRM selection process.

Bridge Dynamics 365 CRM and SharePoint Integration Security Gaps for Secure Document Collaboration!

Dynamics 365 CRM and SharePoint are a powerful duo for managing customer data and documents. But when it comes to security, there can be a chilling disconnect between the two platforms.

Let's explore some security gaps that can arise when storing CRM documents in SharePoint:

• Permission Mismatch: CRM and SharePoint have separate permissions. A user with limited CRM access might have full access to a related document in SharePoint due to no sync of security permissions.
• Accidental Exposure: CRM documents organized by Account might reveal information on other Accounts if a user with access to one Account can view all documents in the corresponding SharePoint folder.
• Permission Inheritance Trap: Documents in folders with broad access inherit that access level, potentially exposing them to unauthorized users.
• External Sharing Risks : Sharing CRM documents with external parties can be tricky. Accidental oversharing of documents with external contacts can lead to data leakage.

Wondering? How can we bridge these security gaps between Dynamics 365 CRM and SharePoint?

No worries!

Utilize ISV solutions that can automatically synchronize permissions between CRM and SharePoint, ensuring seamless document security.

One such solution is Inogic’s SharePoint Security Sync App. It automatically synchronizes Dynamics 365 CRM security privileges to SharePoint, ensuring secure access to confidential files stored in SharePoint.

Check out this product video to learn more about the app.

Here's how SharePoint Security Sync’s features address the security gaps we mentioned above:

Auto-Sync Privileges: This feature ensures that permissions in CRM and SharePoint always match. If a user's CRM access changes, their SharePoint permissions will automatically adjust, preventing accidental exposure due to outdated settings.

Custom Folder Structure: By creating a folder structure that reflects specific accounts or data categories, you can limit document visibility. Users with access to one account's folder wouldn't see documents from other accounts.

Auto-Sync Privileges & Custom Folder Structure: These features work together. Auto-sync ensures inherited permissions in SharePoint reflect CRM access levels. Custom folders keep documents separated, preventing broad folder access from exposing sensitive data.

Risk Free External Sharing: SharePoint Security Sync doesn't directly address external sharing. However, it ensures internal permissions are consistent, reducing the risk of accidentally granting an external user excessive access through CRM documents.

By addressing these security gaps and implementing best practices, you can create a secure environment for collaboration in Dynamics 365 CRM and SharePoint.

Remember, a little vigilance goes a long way in keeping your CRM documents safe from the shadows.

I hope you found this article useful!

If you are interested in learning about the app’s features in depth, you can visit our product documentation site.

To test the app’s features for your business needs, you can get it for a 15-day free trial from Inogic website or Microsoft AppSource .

Feel free to connect with us at [email protected] to get a personalized app demo or to ask us any questions regarding implementing this amazing CRM and SharePoint integration.

PS: Looking for an app to enhance your CRM document management experience? The Attach2Dynamics app by Inogic can help you integrate Microsoft Dynamics 365 CRM with SharePoint, Azure Blob Storage, and Dropbox. It can help you automatically migrate CRM attachments to the cloud for a seamless document management experience, save Dynamics 365 storage space / costs and much more .

• If You’re App-y and You Know It, Clap Your Hands
• The Value of CRM Mapping for Sales Professionals
• Customer Voice Propels Microsoft’s Business Forward Movement
• Email Address Verification for Microsoft Dynamics CRM/365
• It Takes a Team to Raise an App

Leave a Comment Cancel Reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed .

Help | Advanced Search

Computer Science > Software Engineering

Title: creativity, generative ai, and software development: a research agenda.

Abstract: Creativity has always been considered a major differentiator to separate the good from the great, and we believe the importance of creativity for software development will only increase as GenAI becomes embedded in developer tool-chains and working practices. This paper uses the McLuhan tetrad alongside scenarios of how GenAI may disrupt software development more broadly, to identify potential impacts GenAI may have on creativity within software development. The impacts are discussed along with a future research agenda comprising six connected themes that consider how individual capabilities, team capabilities, the product, unintended consequences, society, and human aspects can be affected.

Submission history

Access paper:.

• HTML (experimental)
• Other Formats

References & Citations

• Google Scholar
• Semantic Scholar

BibTeX formatted citation

Bibliographic and Citation Tools

Code, data and media associated with this article, recommenders and search tools.

• Institution

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .