• Browse All Articles
  • Newsletter Sign-Up

CustomerRelationshipManagement →

No results found in working knowledge.

  • Were any results found in one of the other content buckets on the left?
  • Try removing some search filters.
  • Use different search filters.

Sustainable customer relationship management

Marketing Intelligence & Planning

ISSN : 0263-4503

Article publication date: 15 December 2022

Issue publication date: 13 March 2023

Sustainable customer relationship management (SCRM) is a combination of business strategy, customer-oriented business processes and computer systems that seeks to integrate sustainability into customer relationship management. The purpose of this paper is to contribute to the body of knowledge of marketing, business management and computer systems research domains by classifying in research categories the current state of knowledge on SCRM, by analysing the major research streams and by identifying a future research agenda in each research category.

Design/methodology/approach

To identify, select, collect, synthesise, analyse and evaluate all research published on SCRM, providing a complete insight in this research area, the PRISMA methodology, content analysis and bibliometric tools are used.

In total, 139 papers were analysed to assess the trend of the number of papers published and the number of citations of these papers; to identify the top contributing countries, authors, institutions and sources; to reveal the findings of the major research streams; to develop a classification framework composed by seven research categories (CRM as a key factor for enterprise sustainability, SCRM frameworks, SCRM computer tools and methods, case studies, SCRM and sustainable supply chain management, sustainable marketing and knowledge management) in which academics could expand SCRM research; and to establish future research challenges.

Social implications

This paper have an important positive social and environmental impact for society because it will lead to an increase in the number of green and socially conscious customers with an ethical behavior, while also transforming business processes, products and services, making them more sustainable.

Originality/value

Customer relationship management in the age of sustainable development is an increasing research area. Nevertheless, to the authors' knowledge, there are no systematic literature reviews that identify the major research streams, develop a classification framework, analyse the evolution in this research field and propose a future research agenda.

  • Sustainability
  • Customer relations
  • Computer systems and software
  • Corporate social responsibility
  • Sustainable development

Ferrer-Estévez, M. and Chalmeta, R. (2023), "Sustainable customer relationship management", Marketing Intelligence & Planning , Vol. 41 No. 2, pp. 244-262. https://doi.org/10.1108/MIP-06-2022-0266

Emerald Publishing Limited

Copyright © 2022, Maria Ferrer-Estévez and Ricardo Chalmeta

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

Enterprises worldwide are being pushed to be more socially responsive and environmentally sustainable, while increasing company value and continue being customer-oriented ( Das and Hassan, 2021 ). This has forced to an evolution in customer relationship management (CRM) towards sustainable customer relationship management (SCRM). SCRM can be defined as taking into account social, economic and environmental impacts when creating long-term fruitful customer relations. Moreover, SCRM aims to engage sustainability-conscious customers and to increase consumer awareness of corporate sustainability issues ( Müller, 2014 ).

To implement SCRM, an enterprise has to transform its CRM business processes in new sustainability orientated processes ( Chalmeta and Barqueros-Muñoz, 2021 ). However, development of the research on SCRM is still limited, and there are few examples of SCRM applications to real cases ( Gil-Gomez et al ., 2020 ); and hence practitioners have problems integrating sustainability in Customer Relationship Management ( Gil-Gomez et al ., 2020 ). Existing SCRM literature usually consider sustainable as a synonym of a long-term business activity or analyse only one of the sustainability dimensions (economic, environmental, or social) with little research that provides an integrated perspective on how sustainability influence customer relationship management ( Jang and Lee, 2021 ). Therefore, sustainability in CRM is presented as a mutilated concept lacking of a holistic view. In addition, SCRM is commonly focused only on marketing, without taking into account other CRM areas such as sales or post-sales ( Ližbetinová et al ., 2019 ). Consequently, more research is needed in this field ( Liu and Chen, 2022 ).

Which are the most relevant institutions, sources, authors, countries and the most cited papers in the field of Sustainable Customer Relationship Management?

Is it feasible to classify SCRM research papers on the basis of relevant common points?

Which are the future challenges in the research area of Sustainable Customer Relationship Management?

The increasing number of papers in the SCRM research area need to be studied and analysed to identify research topics, main findings and gaps that can be approached in future research. Therefore, to answer the above research questions, this paper (1) carry out a systematic literature review on Sustainable Customer Relationship Management, since it has been validated as an effective research tool that enables an accurate evaluation of the findings to date ( Breslin and Gatrell, 2020 ); (2) gives a deep insight into the research area by using content analysis and bibliometric tools to analyse 139 papers and to identify the most relevant institutions, countries, sources, authors and research categories related to SCRM; (3) identifies and analyses the most cited papers; (4) proposes seven topics that would push academics to expand research on SCRM; and (5) identifies the future research challenges in every research topic. To perform the bibliographic analyses, the PRISMA approach was used ( Bandyopadhyay and Ray, 2020 ).

This paper is organized in the following way: Section 2 shows a review of the background related to SCRM. Section 3 describes the research methods and tools used to perform the systematic literature review and the categories identification. Section 4 presents the findings of the bibliographical and content analyses. Section 5 discusses the findings, and finally, section 6 shows the conclusions, with the future work and research limitations.

2. Background

Customer Relationship Management (CRM) is a change in the enterprise strategy that moves from a product-focused strategy to a customer-focused one ( Cierna and Sujova, 2022 ). Previously, business strategies were focused on the product or service, and the goal of marketing was to convince customers to buy them. This change, together with the development of new information and communication technologies, and new forms of business organization has converged in what it is currently known as CRM, which transforms the relationships between companies and clients ( Lokesh et al ., 2022 ). The aim is create value for customers, understand their needs and offer value-added services ( Meha, 2021 ).

CRM does not have a single definition. In the literature, it has been analysed from different academic disciplines such as Marketing, Business, Management, Information Technology ( Migdadi, 2020 ); and it has been conceptualised from five different viewpoints: (1) Process, (2) Strategy, (3) Philosophy, (4) Capability and/or (5) Technological tools ( Meena and Sahu, 2021 ). Therefore, CRM is not just technology. A suitable implementation of CRM requires an integrated and balanced approach to people, process and technology. Despite these different approaches, many definitions agree that the main business areas of CRM are marketing, sales and after-sales support ( Sun and Wang, 2022 ), and that the objective is to establish long-term relationships with customers in order to generate value between customers and company.

CRM allows enterprises (1) to have an integrated, single view of customers, by using analytical tools; (2) to manage customer relationships in a single way, regardless of the communication channel; and (3) to improve the effectiveness and efficiency of the processes involved in customer relationships ( Li and Xu, 2022 ). Therefore, CRM provides multiple benefits to the company and to customers, such as greater customer satisfaction, better service, better customer segmentation, personalized service, etc ( Chalmeta, 2006 ).

2.2 Sustainable customer relationship management

Sustainable has become a key strategic objective worldwide. It can be defined as satisfy humanity current needs without compromising future generations needs ( Brundtland et al ., 1987 ). However, there are other numerous definitions of the sustainability concept ( Langa et al ., 2021 ) which can be classified into 5 categories ( Lozano, 2008 ): (1) conventional economists' perspective; (2) non-environmental degradation perspective; (3) integrational perspective, i.e. encompassing the economic, environmental and social aspects; (4) inter-generational perspective; and (5) holistic perspective.

The awareness about sustainable development has generated a new paradigm in the enterprise values and policies and in the way of understanding business and has triggered new management models that take into account the economic, social and environmental impact of their decisions ( Luu et al ., 2019 ; Yu and Xu, 2022 ). The aim is to generate long-term shared value ( de Villiers et al ., 2022 ) between the enterprise and its internal and external stakeholders, combining economic and social value ( Blackburn et al ., 2018 ).

In this context, the Sustainable Customer Relationship Management arises as an evolution of the CRM ( Tian et al ., 2021 ). Müller (2014) states that SCRM means become aware of environmental, social and economic impact of customer-oriented business process, as well as to communicate corporate sustainability issues among their customers, which will increase corporate value among its sustainability-conscious customers.

SCRM is a consequence of (1) the sustainability awareness in companies ( Ceccarini et al ., 2022 ); (2) the information technologies evolution such as Digitalization, Big Data, etc. that allows the re-engineering of the CRM business processes, making them more sustainable ( Chalmeta and Barqueros-Muñoz, 2021 ); and (3) the increase of highly responsible consumers ( Papadopoulou et al ., 2022 ) who, aware of the negative impacts of the current model of consumption and production, seek more sustainable lifestyles ( Figure 1 ).

3. Research methodology

To answer the three research questions, RQ1 , RQ2 and RQ3 , a systematic literature review has been carried out. Systematic literature review is a research method that allows to identify, select, collect, synthesise and evaluate all research published on a particular research area. The results obtained are showed in Section 4 .

The research was conducted following the criteria of preferred reporting items for systematic reviews and meta-analysis (PRISMA) ( Liberati et al ., 2009 ). These include the following steps: (1) eligibility criteria; (2) information sources; (3) search terms; (4) study selection; and (5) data collection process and synthesis ( Figure 2 ).

3.1 Eligibility criteria

Studies were eligible for inclusion if they were: research papers, review papers and conference/congress papers since they are regarded true knowledge ( Ramos-Rodrígue and Ruíz-Navarro, 2004 ); directly relevant to sustainable customer relationship management; they were written in English; and published in peer-reviewed journals.

We excluded studies if they were: not written in English; books, thesis and conference proceedings; papers that focused on other sustainability domains such as supply chain, education sustainability; and papers that were not available in full text.

3.2 Information sources

We conducted an organised, systematic and comprehensive wide-ranging search of two online databases: Web of Science and Scopus. These two databases were selected because they combine both a rigorous selection process and wide interdisciplinary coverage. For this reason, they are the main sources of bibliographic citations used for bibliometric analyses ( Martínez-López et al ., 2018 ).

3.3 Search terms

The collection of papers was conducted by selecting those papers that had specific keywords related to our research aims and questions in the title, in the abstract or in the keywords section ( Table 1 ). These keywords were customer relationship management, CRM, sustainable and sustainability.

Logical operators were connected with different sets of keywords and designed as follows: (“customer relationship management” OR “CRM”) AND (“sustainable” OR “sustainability”)

3.4 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 the two co-authors of this study. Firstly, records are identified through different information sources (online databases) using the keywords. Secondly, once all records are obtained, records are excluded based on duplicates ( Linnenluecke et al ., 2020 ). Thirdly, once all duplicates are removed, records are screened based on “title, abstract and keywords”. Any studies that did not meet the eligibility criteria were excluded. Finally, a “full-text” screening of all studies was performed. A meeting was carried out to discuss and agree on the final studies that are included in this systematic review.

3.5 Data collection process and synthesis

The search concentrated on research papers and review papers published until June 9, 2022. In our initial search, we found 643 papers (364 papers from Web of Science and 279 papers from Scopus.). The number of duplicated papers removed was 181. Applying the eligibility criteria, we excluded papers by screening the title, the abstract and the keywords (for example papers whose CRM acronym is related with Coastal Resource Management; Copper Raw Materials; Coral Reef Management; Clinical Risk Management; Climate Risk Management, etc were excluded). Finally, we excluded papers based on the full text screening. Hence, the final sample consisted of 139 papers.

We used Microsoft Excel 2016 to collect basic publication data such as date, title, authors, publisher, DOI, URL, pages, volume, issues, keywords.

The analysis of the data collection allowed to identify the top contributing countries, authors, institutions and sources in the area of Sustainable CRM ( RQ1 ), to establish research categories ( RQ2 ) and to identify future research challenges ( RQ3 ).

For the research categories identification, the comparative method proposed by Collier (1998) was used. This method enables the identification of common points shared by the papers through a content analysis, so that the categories emerged. Content analysis is “an effective tool for analysing a sample of research documents in a systematic and rule-governed way” ( Seuring and Gold, 2012 ). It allows an objective identification of the content in a data set, such as selected articles ( Sandberg and Jafari, 2018 ). It overlaps with the concept of thematic analysis, which is mainly a qualitative method for uncovering different categories within a data set ( Fugard and Potts, 2015 ).

A first categories classification was done taking into account the aim of the paper and its contribution to the state of the art. Then, the capacity of the categories classification to arrange all the papers was checked paper by paper. If a paper did not fit into any research category, the classification was redesigned to integrate the incompatible paper. The categories classification was reconsidered several times until all the papers on the sample were properly distributed.

As a result, seven main SCRM research categories were identified: CRM as a key factor for enterprise sustainability, SCRM frameworks, SCRM computer tools and methods, Case studies, SCRM and sustainable supply chain management, Sustainable marketing and Knowledge management. We also created a qualitative and quantitative evidential narrative summary for each CRM research category.

Any disagreements between co-authors of this study were settled through consensus.

3.6 Finding analysis

Once the final sample of papers had been defined, the analysis tools provided by Scopus and Web of Science and were employed to determine the evolution in the number of papers published by year; to analyse the number of papers published by author, country, institution and journal; to analyse the indicators of relevance, impact and prestige of the ten journals with the most published articles on the list; to analyse the content of the ten most cited articles on the sample.

4. Findings

4.1 bibliometric analysis, 4.1.1 trend in the publication of papers.

The first article detected in the systematic literature review dates back to 2001. Since then to date, the number of papers has evolved greatly, and a growing trend is observed ( Figure 3 ). The number of papers published in 2022 corresponds to the period between January and June. Therefore, the number of publications at the end of 2022 should exceed the number of publications in previous years.

4.1.2 Most influence authors

In the analysis of the authors, there is no one that highlights significantly regarding the number of publications. This can be due to there is limited number of specialists in the area. There are only eight authors with three articles. The rest of the authors have two papers or less (see Table 2 ).

4.1.3 Most influence countries

Regarding the countries, there is a leadership of India, followed by the United Stated and China. These three countries with higher number of publications account for approximately one-third of the total number of publications related to the study area (see Table 3 ).

4.1.4 Top contributing institutions

The analysis of the institutions reveals that none highlights significantly regarding the number of publications. Therefore, there is no institution with a high degree of expertise in this research area ( Table 4 ).

4.1.5 Most cited papers

Regarding papers citation analysis, the most cited paper has 760 citations, which stands out significantly from the rest of the citations. Table 5 shows the ten most cited papers.

4.1.6 Sources analysis

Regarding publishers, the journal that has most publications is Sustainability with nine papers. The following journals are International Journal of Productivity and Performance Management and Business Process Management Journal, with four and three publications. These three journals have published roughly the 11% of the papers of the sample, so it can be concluded that there is no journal specialized in this research area.

To assess the impact and relevance of the sources, three impact indicators have been used: CiteScore, Source Normalized Impact per Paper (SNIP) and SCImago Journal Rank (SJR). The impact factors correspond to the year 2021 and were collected from Scopus. Table 6 shows the results of the journals evaluation according to these three indicators.

4.2 Research categories

A content analysis of the 139 articles was carried out to identify (1) a classification framework composed by seven research categories that organize papers according to common issues and (2) future research challenges in Sustainable CRM.

The research categories obtained are shown in Table 7 . The number of papers in each category is showed in brackets in the first column. The description of the state of existing knowledge in every category has been carried out analysing the five most cited papers in every category.

Table 8 shows a summary of the future research challenges on SCRM, made by a content analysis of the papers in each topic. It is interesting to highlight that the most cited papers in the research area, shown in Table 5 , are mainly in the category SCRM computer tools and methods (five of the ten articles). The other five papers belong to category Case studies (two articles), category sustainable supply chain management (two articles) and category SCRM frameworks (one article).

5. Discussion

This study has applied the PRISMA systematic literature review approach, which not only categorised and organised the existing literature in a systematic and valid manner, but also identified the main potential areas for future research. The PRISMA approach guarantees a replicable, scientific and transparent process to minimise bias and provides an audit trail of the reviewer's decisions, procedures and conclusions, which is a necessary requirement in systematic reviews ( Tranfield et al ., 2003 ).

5.1 Contributions to theory

According to Paul and Criado (2020) and Paul et al . (2021) , a systematic literature review should be written when there is a substantial body of work in the domain (at least 40 articles for review), and no systematic literature review has been conducted in the field in recent years (within the last 5 years). Therefore, this paper covers a gap in the domain of Sustainable Customer Relationship Management, as it updates the only systematic literature review carried out to date ( Müller, 2014 ), expanding the period of analysis up to June 2022. In addition, it analyses papers from the Scopus and Web of Science databases, which follow a more rigorous selection process than the online databases Proquest and ScienceDirect used by Müller (2014) .

According to Linnenluecke et al . (2020) , descriptive statistics (e.g. frequency tables) should be used to summarise the basic information on the topic gathered over time in systematic reviews. This paper uses bibliometric statistical analysis techniques to show significant information in the SCRM domain such as the top contributing countries, authors, institutions and sources ( RQ1 answer).

According to Mukherjee et al . (2022) , Linnenluecke (2017) , Post et al . (2020) , to make a theoretical contribution it is not enough to merely report on previous literature. Systematic literature reviews should focus on identifying new frameworks, promoting the objective discovery of knowledge clusters or identifying major research streams. Through a content analysis, this paper proposes a classification framework composed of seven research categories that shows different ways of contributing to the current state of knowledge on the topic: CRM as a key factor for enterprise sustainability, SCRM frameworks, SCRM computer tools and methods, Case studies, SCRM and sustainable supply chain management, Sustainable marketing and Knowledge management ( RQ2 answer). In addition, instead of an arbitrary selection of evidence for category description, the five most cited papers have been selected to describe the major research streams in the research categories, which contributes to the replicability of the process and the quality of the findings.

According to Post et al . (2020) , to make a theoretical contribution, systematic literature reviews can focus on identifying a research agenda. However, this research agenda should follow and accompany another form of synthesis, such as a taxonomy or framework. This paper synthesises the future research challenges in each research category of the proposed classification framework (answer to RQ3 ).

5.2 Contributions to managerial practice

This study offer consulting firms and managers of enterprises different lines of thought that will allow them to carry out SCRM in enterprises. Moreover, the literature classification in seven categories, enable practitioners to: (1) understand the current state of the art in SCRM, in terms of conceptualisation, frameworks, models, methods, tools, influence and business practices; (2) know the future challenges in the seven research topics to make appropriate investment decisions about improving current tools/methods; (3) analyse the consequences of SCRM implementation.

5.3 Contributions to society

This paper will make significant progress towards Sustainability-oriented CRM research and implementation. This will have an important positive social and environmental impact for society because it will lead to an increase in the number of green and socially conscious customers with an ethical behavior ( Roberts, 2003 ), while also transforming business processes, products and services, making them more sustainable. This will imply, among other benefits, fairer treatment of customers and employees, the hiring and training of local people, more community consultation processes, reduction of carbon emissions or water consumption, etc.

6. Conclusion

The growing interests of consumers and companies in sustainability has transformed CRM into SCRM, with the aim of offering more socially and environmentally sustainable products and services while attracting and retaining sustainability-conscious customers.

To advance in the state of the art in SCRM, in this paper a systematic literature review on SCRM has been carried out. A sample of 139 papers were analysed to assess the trend of the number of papers published and the number of citations of these papers; to identify the top contributing countries, authors, institutions and sources; to reveal the findings of the ten most cited papers; and to establish research categories and future research challenges in the area.

This study therefore addresses a critical research gap, namely, the lack of extensive systematic reviews of the current research on sustainable CRM, which could constrain its influence. As a consequence of the study, some conclusions can be drawn: First, the number of papers is still low although the tendency is clearly on the increase, in terms of both the number of citations and the number of publications per year. Second, analyses of the influence of authors and institutions do not reveal any particular tendency or pattern. Third, regarding productivity, Gholami, H., Saman, M.Z.M., Sharif, S., Zakuan, N., Gomez, J.M., vom Berg, B.W., Lee, Y.I. and Trim, P.R.J are the top contributing authors; and India, the United States, China and theUnited Kingdom are the countries with the most publications in this field. This shows that the more productive regions are Asia and English-speaking countries. Fourth, the main sources are: Sustainability, International Journal of Productivity and Performance Management Business Process Management Journal and Journal of Cleaner Production. Sustainability is the journal with the most papers published. Fifth, a classification by categories has been developed through a comparative analysis of the content, so as to bring some order to the research effort that is being made, which includes all the papers in the sample. This classification into seven research categories supports the future work of academics in this research area because it establishes common shared elements and patterns in every research category and reveals those aspects that have been studied to a lesser extent and are in need of future research. The categorisation revealed that, regardless of the category and despite the volume of research in the area, few studies address a comprehensive vision of the concept of sustainability – conclusions that are in line with those of Müller (2014) . In some cases, the concept refers to the economic or long-term sustainability of the company. In others, sustainability is approached only from social and/or environmental perspectives.

The systematic literature review has proved that an analysis of current research could support academics future research in SCRM as well as and practitioners work. The main conclusion is that this research area requires more research and a higher number of annual publications. The majority of papers have been published in four categories: SCRM computer tools and methods, case studies, SCRM and sustainable supply chain management and CRM as a key factor for enterprise sustainability; therefore, more research is needed in the other three. On the other hand, it is necessary more research studies that consider jointly the economic, environmental and social sustainability dimensions, because the majority of the SCRM literature studies focus only in one of these sustainability dimensions.

Finally, it is important to highlight the limitations of this work: (1) only two bibliographical databases have been studied, Scopus and Web of Science. Other databases could be analysed to extent and contrast the findings; (2) there is a language bias, due to the search was carry out only in English; (3) other keywords could have been used and might have produced other findings; (3) the comparative method proposed by Collier (1998) was used. Other methods, such as network analysis or latent Dirichlet allocation (LDA), might be used for research categories identification and may result in other classifications.

research paper on customer relationship management

Evolution of the CRM concept to Sustainable CRM

research paper on customer relationship management

Research methodology steps

research paper on customer relationship management

Trend in the publication of papers

Search strategy

Top contributing authors

Top 12 contributing countries

Top contributing institutions

Ten most cited papers

Sources analysis

Research categories

Future research challenges

Ahuja , J. , Panda , T.K. , Luthra , S. , Kumar , A. , Choudhary , S. and Garza-Reyes , J.A. ( 2019 ), “ Do human critical success factors matter in adoption of sustainable manufacturing practices? An influential mapping analysis of multi-company perspective ”, Journal of Cleaner Production , Vol.  239 , doi: 10.1016/j.jclepro.2019.117981 .

Azad , N. and Ahmadi , F. ( 2015 ), “ The customer relationship management process: its measurement and impact on performance ”, Uncertain Supply Chain Management , Vol.  3 No.  1 , doi: 10.5267/j.uscm.2014.9.002 .

Bahri-Ammari , N. and Soliman , K.S. ( 2016 ), “ The effect of CRM implementation on pharmaceutical industry’s profitability: the case of Tunisia ”, Management Research Review , Vol.  39 No.  8 , pp. 854 - 878 , doi: 10.1108/MRR-11-2014-0258 .

Bandyopadhyay , C. and Ray , S. ( 2020 ), “ Social enterprise marketing: review of literature and future research agenda ”, Marketing Intelligence and Planning , Vol.  38 No.  1 , pp.  121 - 135 , doi: 10.1108/MIP-02-2019-0079 .

Bhat , S.A. and Darzi , M.A. ( 2018 ), “ Service, people and customer orientation: a capability view to CRM and sustainable competitive advantage ”, Vision , Vol.  22 No.  2 , pp.  163 - 173 , doi: 10.1177/0972262918766132 .

Blackburn , N. , Hooper , V. , Abratt , R. and Brown , J. ( 2018 ), “ Stakeholder engagement in corporate reporting: towards building a strong reputation ”, Marketing Intelligence and Planning , Vol.  36 No.  4 , pp.  484 - 497 .

Breslin , D. and Gatrell , C. ( 2020 ), “ Theorizing through literature reviews: the miner-prospector continuum ”, Organizational Research Methods , doi: 10.1177/1094428120943288 .

Brundtland , G.H. , Khalid , M. , Agnelli , S. , Al-Athel , S. and Chidzero , B.J.N.Y. ( 1987 ), Our Common Future: Brundtland Report , ONU , Ada, OH .

Ceccarini , C. , Bogucka , E.P. , Sen , I. , Constantinides , M. , Prandi , C. and Quercia , D. ( 2022 ), “ Visualizing internal sustainability efforts in big companies ”, IEEE Computer Graphics and Applications , Vol.  42 No.  3 , pp. 87 - 98 , doi: 10.1109/MCG.2022.3163063 .

Chalmeta , R. ( 2006 ), “ Methodology for customer relationship management ”, Journal of Systems and Software , Vol.  79 No.  7 , doi: 10.1016/j.jss.2005.10.018 .

Chalmeta , R. and Barqueros-Muñoz , J.E. ( 2021 ), “ Using Big data for sustainability in supply chain management ”, Sustainability , Vol.  13 No.  13 , p. 7004 , doi: 10.3390/su13137004 .

Cierna , H. and Sujova , E. ( 2022 ), “ Differentiated customer relationship management - a tool for increasing enterprise competitiveness ”, Management Systems in Production Engineering , Vol.  30 No.  2 , pp.  163 - 171 , doi: 10.2478/mspe-2022-0020 .

Collier , D. ( 1998 ), “ Comparative method in the 1990s ”, CP Newsletter of the Comparative Politics Organized Section of the American Political Science Association . doi: 10.2139/ssrn.1757219 .

Das , S. and Hassan , H.M.K. ( 2021 ), “ Impact of sustainable supply chain management and customer relationship management on organizational performance ”, International Journal of Productivity and Performance Management , Vol.  71 No.  6 , pp. 2140 - 2160 , doi: 10.1108/IJPPM-08-2020-0441 .

de Villiers , C. , Hsiao , P.C.K. , Zambon , S. and Elisabetta , M. ( 2022 ), “ Sustainability, non-financial, integrated, and value reporting (extended external reporting): a conceptual framework and an agenda for future research ”, Meditari Accountancy Research . doi: 10.1108/MEDAR-04-2022-1640 , forthcoming, The University of Auckland Business School Research Paper, SSRN, available at: https://ssrn.com/abstract=4099866

Dey , P.K. and Cheffi , W. ( 2013 ), “ Green supply chain performance measurement using the analytic hierarchy process: a comparative analysis of manufacturing organisations ”, Production Planning and Control , Vol.  24 Nos 8–9 , pp.  702 - 720 , doi: 10.1080/09537287.2012.666859 .

Dubey , R. , Gunasekaran , A. , Papadopoulos , T. and Childe , S.J. ( 2015 ), “ Green supply chain management enablers: mixed methods research ”, Sustainable Production and Consumption , Vol.  4 , doi: 10.1016/j.spc.2015.07.001 .

Duque-Uribe , V. , Sarache , W. and Gutiérrez , E.V. ( 2019 ), “ Sustainable supply chain management practices and sustainable performance in hospitals: a systematic review and integrative framework ”, Sustainability (Switzerland) , Vol.  11 No.  21 , doi: 10.3390/su11215949 .

Evangelista , P. and Durst , S. ( 2015 ), “ Knowledge management in environmental sustainability practices of third-party logistics service providers ”, VINE , Vol.  45 No.  4 , pp.  509 - 529 , doi: 10.1108/VINE-02-2015-0012 .

Fairchild , A. ( 2011 ), “ A view on knowledge management ”, Strategies for Information Technology Governance . doi: 10.4018/9781591401407.ch007 .

Fugard , A.J.B. and Potts , H.W.W. ( 2015 ), “ Supporting thinking on sample sizes for thematic analyses: a quantitative tool ”, International Journal of Social Research Methodology , Vol.  18 No.  6 , pp.  669 - 684 .

Gholami , H. , Saman , M.Z.M. , Sharif , S. and Zakuan , N. ( 2015 ), “ A CRM strategic leadership towards sustainable development in student relationship management: SD in higher education ”, Procedia Manufacturing , Vol.  2 , pp.  51 - 60 , doi: 10.1016/j.promfg.2015.07.010 .

Gholami , H. , Zameri Mat Saman , M. , Mardani , A. , Streimikiene , D. , Sharif , S. and Zakuan , N. ( 2018 ), “ Proposed analytic framework for student relationship management based on a systematic review of CRM systems literature ”, Sustainability , Vol.  10 No.  4 , p. 1237 , doi: 10.3390/su10041237 .

Gholami , H. , Zakuan , N. , Saman , M.Z.M. , Sharif , S. and Kohar , U.H.A. ( 2020 ), “ Conceptualizing and operationalizing the student relationship management strategy: towards a more sustainable-based platform ”, Journal of Cleaner Production , Vol.  244 , 118707 , doi: 10.1016/j.jclepro.2019.118707 .

Gil-Gomez , H. , Guerola-Navarro , V. , Oltra-Badenes , R. and Lozano-Quilis , J.A. ( 2020 ), “ Customer relationship management: digital transformation and sustainable business model innovation ”, Economic Research-Ekonomska Istrazivanja , Vol.  33 No.  1 , pp. 2733 - 2750 , doi: 10.1080/1331677X.2019.1676283 .

Güçdemir , H. and Selim , H. ( 2017 ), “ Customer centric production planning and control in job shops: a simulation optimization approach ”, Journal of Manufacturing Systems , Vol.  43 , pp.  100 - 116 , doi: 10.1016/j.jmsy.2017.02.004 .

Guerola-Navarro , V. , Oltra-Badenes , R. , Gil-Gomez , H. and Gil-Gomez , J.A. ( 2021 ), “ Research model for measuring the impact of customer relationship management (CRM) on performance indicators ”, Economic Research-Ekonomska Istraživanja , Vol.  34 No.  1 , pp. 2669 - 2691 , doi: 10.1080/1331677X.2020.1836992 .

Hasani , T. , Bojei , J. and Dehghantanha , A. ( 2017 ), “ Investigating the antecedents to the adoption of SCRM technologies by start-up companies ”, Telematics and Informatics , Vol.  34 No.  5 , pp. 655 - 675 , doi: 10.1016/j.tele.2016.12.004 .

Hazen , B.T. , Russo , I. , Confente , I. and Pellathy , D. ( 2020 ), “ Supply chain management for circular economy: conceptual framework and research agenda ”, International Journal of Logistics Management , Vol.  32 No.  2 , pp. 510 - 537 , doi: 10.1108/IJLM-12-2019-0332 .

Hitka , M. , Pajtinkova-Bartakova , G. , Lorincova , S. , Palus , H. , Pinak , A. , Lipoldova , M. , Krahulcova , M. , Slastanova , N. , Gubiniova , K. and Klaric , K. ( 2019 ), “ Sustainability in marketing through customer relationship management in a telecommunication company ”, Marketing and Management of Innovations , Vol.  4 , doi: 10.21272/mmi.2019.4-16 .

Jang , H.-W. and Lee , S.-B. ( 2021 ), “ The relationship between contact-free services, social and personal norms, and customers’ behavior for the sustainable management of the restaurant industry ”, Sustainability , Vol.  13 No.  17 , p. 9870 , doi: 10.3390/su13179870 .

Khasawneh , R. and Alazzam , A. ( 2014 ), “ Towards customer knowledge management (CKM): where knowledge and customer meet ”, Marketing and Consumer Behavior: Concepts, Methodologies, Tools, and Applications , Vol.  1 No.  4 , doi: 10.4018/978-1-4666-7357-1.ch002 .

Kunz , W. , Aksoy , L. , Bart , Y. , Heinonen , K. , Kabadayi , S. , Ordenes , F.V. , Sigala , M. , Diaz , D. and Theodoulidis , B. ( 2017 ), “ Customer engagement in a Big data world ”, Journal of Services Marketing , Vol.  31 No.  2 , doi: 10.1108/JSM-10-2016-0352 .

Langa , E.S. , Agostinho , F. , Liu , G. , Almeida , C.M. and Giannetti , B.F. ( 2021 ), “ How the global initiative report’s indicators are related to the strong sustainability concept? - a paraconsistent approach ”, Journal of Environmental Accounting and Management , Vol.  9 , pp. 299 - 318 , doi: 10.5890/JEAM.2021.09.007 .

Lee , Y.-I. and Trim , P.R.J. ( 2006 ), “ Retail marketing strategy: the role of marketing intelligence, relationship marketing and trust ”, Marketing Intelligence and Planning , Vol.  24 No.  7 , pp.  730 - 745 , doi: 10.1108/02634500610711888 .

Li , F.Y. and Xu , G.H. ( 2022 ), “ AI-driven customer relationship management for sustainable enterprise performance ”, Sustainable Energy Technologies and Assessments , Vol.  52 , doi: 10.1016/j.seta.2022.102103 .

Liberati , A. , Altman , D.G. , Tetzlaff , J. , Mulrow , C. , Gøtzsche , P.C. , Ioannidis , J.P.A. , Clarke , M. , Devereaux , P.J. , Kleijnen , J. and Moher , D. ( 2009 ), “ The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration ”, BMJ , Vol.  339 , p. b2700 , doi: 10.1136/bmj.b2700 .

Linnenluecke , M.K. ( 2017 ), “ Resilience in business and management research: a review of influential publications and a research agenda ”, International Journal of Management Reviews , Vol.  19 , pp.  4 - 30 .

Linnenluecke , M.K. , Marrone , M. and Singh , A. ( 2020 ), “ Conducting systematic literature reviews and bibliometric analyses ”, Australian Journal of Management , Vol.  45 No.  2 , pp. 175 - 194 , doi: 10.1177/0312896219877678 .

Liu , Y.S. and Chen , Z. ( 2022 ), “ A new model to evaluate the success of electronic customer relationship management systems in industrial marketing: the mediating role of customer feedback management ”, Total Quality Management and Business Excellence , doi: 10.1080/14783363.2022.2071694 (In press) .

Ližbetinová , L. , Štarchoň , P. , Lorincová , S. , Weberová , D. and Pruša , P. ( 2019 ), “ Application of cluster analysis in marketing communications in small and medium-sized enterprises: an empirical study in the Slovak Republic ”, Sustainability (Switzerland) , Vol.  11 No.  8 , doi: 10.3390/su11082302 .

Lokesh , S. , Menaga , A. and Vasantha , S. ( 2022 ), “ Influence of customer relationship management towards customer loyalty with mediating factor customer satisfaction in insurances sector ”, Quality-Access to Success , Vol.  23 No.  187 , pp.  169 - 173 .

Lozano , R. ( 2008 ), “ Envisioning sustainability three-dimensionally ”, Journal of Cleaner Production , Vol.  16 No.  17 , pp. 1838 - 1846 , doi: 10.1016/j.jclepro.2008.02.008 .

Luu , T. , Viet , L. , Masli , E. and Rajendran , D. ( 2019 ), “ Corporate social responsibility, ambidextrous leadership, and service excellence ”, Marketing Intelligence and Planning , Vol.  37 No.  5 , pp.  580 - 594 , doi: 10.1108/MIP-05-2018-0157 .

Martínez-López , F.J. , Merigó , J.M. , Valenzuela-Fernández , L. and Nicolás , C. ( 2018 ), “ Fifty years of the European Journal of Marketing: a bibliometric analysis ”, European Journal of Marketing , doi: 10.1108/EJM-11-2017-0853 .

Meena , P. and Sahu , P. ( 2021 ), “ Customer relationship management research from 2000 to 2020: an academic literature review and classification ”, Vision-The Journal of Business Perspective , Vol.  25 No.  2 , pp.  136 - 158 , doi: 10.1177/0972262920984550 .

Meha , A. ( 2021 ), “ Customer relationship management ”, Quality-Access to Success , Vol.  22 No.  183 , pp.  42 - 47 .

Memari , A. , Vom Berg , B.W. and Gomez , J.M. ( 2011 ), “ Sustainable CRM for mobility services based on SOA architecture ”, Environmental Science and Engineering (Subseries: Environmental Science) . doi: 10.1007/978-3-642-19536-5_19 .

Migdadi , M.M. ( 2020 ), “ Knowledge management, customer relationship management and innovation capabilities ”, Journal of Business and Industrial Marketing , Vol.  36 No.  1 , pp.  111 - 124 , doi: 10.1108/JBIM-12-2019-0504 .

Mishra , S. and Prasad , S. ( 2014 ), “ Exploring linkages between socio-demographic factors and customer loyalty in India ”, Management and Marketing , Vol.  9 No.  1 , pp.  13 - 26 .

Mohanty , M. ( 2018 ), “ Assessing sustainable supply chain enablers using total interpretive structural modeling approach and fuzzy-MICMAC analysis ”, Management of Environmental Quality: An International Journal , Vol.  29 No.  2 , pp.  216 - 239 , doi: 10.1108/MEQ-03-2017-0027 .

Müller , A.-L. ( 2014 ), “ Sustainability and customer relationship management: current state of research and future research opportunities ”, Management Review Quarterly , Vol.  64 No.  4 , pp.  201 - 224 , doi: 10.1007/s11301-014-0104-x .

Mukherjee , D. , Marc , W. , Kumar , S. and Donthu , N. ( 2022 ), “ Guidelines for advancing theory and practice through bibliometric research ”, Journal of Business Research , Elsevier , Vol.  148 , pp.  101 - 115 .

Osarenkhoe , A. and Bennani , A. ( 2007 ), “ An exploratory study of implementation of customer relationship management strategy ”, Business Process Management Journal , Vol.  13 No.  1 , pp.  139 - 164 , doi: 10.1108/14637150710721177 .

Pan , S.L. and Lee , J.-N. ( 2003 ), “ Using e-CRM for a unified view of the customer ”, Communications of the ACM , Vol.  46 No.  4 , pp.  95 - 99 , doi: 10.1145/641205.641212 .

Pan , S.-L. , Tan , C.-W. and Lim , E.T.K. ( 2006 ), “ Customer relationship management (CRM) in e-government: a relational perspective ”, Decision Support Systems , Vol.  42 No.  1 , pp.  237 - 250 , doi: 10.1016/j.dss.2004.12.001 .

Papadopoulou , M. , Papasolomou , I. and Thrassou , A. ( 2022 ), “ Exploring the level of sustainability awareness among consumers within the fast-fashion clothing industry: a dual business and consumer perspective ”, Competitiveness Review , Vol.  32 No.  3 , pp.  350 - 375 , doi: 10.1108/CR-04-2021-0061 .

Paul , J. and Criado , A.R. ( 2020 ), “ The art of writing literature review: what do we know and what do we need to know? ”, International Business Review , Vol.  29 No.  4 , 101717 , doi: 10.1016/j.ibusrev.2020.101717 .

Paul , J. , Lim , W.M. , O'Cass , A. , Hao , A.W. and Bresciani , S. ( 2021 ), “ Scientific procedures and rationales for systematic literature reviews (SPAR-4-SLR) ”, International Journal of Consumer Studies , April 2022 , doi: 10.1111/ijcs.12695 .

Pohludka , M. and Štverková , H. ( 2019 ), “ The best practice of CRM implementation for small- and medium-sized enterprises ”, Administrative Sciences , Vol. 9 No. 1 , 22 . doi: 10.3390/admsci9010022 .

Post , C. , Sarala , R. , Gatrell , C. and Prescott , J.E. ( 2020 ), “ Advancing theory with review articles ”, Journal of Management Studies , Vol.  57 No.  2 , pp.  351 - 376 .

Racherla , P. and Hu , C. ( 2008 ), “ e-CRM system adoption by hospitality organizations: a technology-organization-environment (toe) framework ”, Journal of Hospitality and Leisure Marketing , Vol.  17 Nos 1-2 , pp.  30 - 58 , doi: 10.1080/10507050801978372 .

Ramos-Rodrígue , A.R. and Ruíz-Navarro , J. ( 2004 ), “ Changes in the intellectual structure of strategic management research: a bibliometric study of the Strategic Management Journal, 1980-2000 ”, Strategic Management Journal , Vol.  25 No.  10 , pp. 981 - 1004 , doi: 10.1002/smj.397 .

Raut , R. , Cheikhrouhou , N. and Kharat , M. ( 2017 ), “ Sustainability in the banking industry: a strategic multi-criterion analysis ”, Business Strategy and the Environment , Vol.  26 No.  4 , pp.  550 - 568 , doi: 10.1002/bse.1946 .

Reinartz , W. , Krafft , M. and Hoyer , W.D. ( 2004 ), “ The customer relationship management process: its measurement and impact on performance ”, Journal of Marketing Research , Vol.  41 No.  3 , doi: 10.1509/jmkr.41.3.293.35991 .

Roberts , S. ( 2003 ), “ Supply chain specific? Understanding the patchy success of ethical source initiatives ”, Journal of Business Ethics , Vol.  44 Nos 2–3 , pp.  159 - 170 .

Sandberg , E. and Jafari , H. ( 2018 ), “ Retail supply chain responsiveness. Towards a retail-specific framework and a future research agenda ”, International Journal of Productivity and Performance Management , Vol.  67 No.  9 , pp.  1977 - 1993 .

Seuring , S. and Gold , S. ( 2012 ), “ Conducting content analysis based literature reviews in supply chain management ”, Supply Chain Management: An International Journal , Vol.  17 No.  5 , pp.  544 - 555 .

Shukla , M.K. and Pattnaik , P.N. ( 2019 ), “ Managing customer relations in a modern business environment: towards an ecosystem-based sustainable CRM model ”, Journal of Relationship Marketing , Vol.  18 No.  1 , pp.  17 - 33 , doi: 10.1080/15332667.2018.1534057 .

Siu , N.Y.M. , Zhang , T.J.F. , Dong , P. and Kwan , H.-Y. ( 2013 ), “ New service bonds and customer value in customer relationship management: the case of museum visitors ”, Tourism Management , Vol.  36 , pp.  293 - 303 , doi: 10.1016/j.tourman.2012.12.001 .

Sun , Y.L. and Wang , P. ( 2022 ), “ The E-commerce investment and enterprise performance based on customer relationship management ”, Journal of Global Information Management , Vol.  30 No.  3 , pp. 1 - 15 , doi: 10.4018/JGIM.20220701.oa9 .

Tian , G. , Pekyi , G.D. , Chen , H. , Sun , H. and Wang , X. ( 2021 ), “ Sustainability-conscious stakeholders and CSR: evidence from IJVs of Ghana ”, Sustainability , Vol.  13 No.  2 , p. 639 , doi: 10.3390/su13020639 .

Tranfield , D. , Denyer , D. and Smart , P. ( 2003 ), “ Towards a methodology for developing evidence-informed management knowledge by means of systematic review ”, British Journal of Management , Vol.  14 , pp.  207 - 222 .

Trim , P.R.J. and Lee , Y. ( 2004 ), “ Enhancing customer service and organizational learning through qualitative research ”, Qualitative Market Research , Vol.  7 No.  4 , pp.  284 - 292 , doi: 10.1108/13522750410557094 .

Trim , P.R.J. and Lee , Y.-I. ( 2008 ), “ A strategic approach to sustainable partnership development ”, European Business Review , Vol.  20 No.  3 , pp.  222 - 239 , doi: 10.1108/09555340810871428 .

Vesal , M. , Siahtiri , V. and O'Cass , A. ( 2021 ), “ Strengthening B2B brands by signalling environmental sustainability and managing customer relationships ”, Industrial Marketing Management , Vol.  92 , pp.  321 - 331 , doi: 10.1016/j.indmarman.2020.02.024 .

vom Berg , B.W. , Gómez , J.M. and Sandau , A. ( 2017 ), “ ICT-platform to transform car dealerships to regional providers of sustainable mobility services ”, Interdisciplinary Journal of Information, Knowledge, and Management , Vol.  12 , pp.  037 - 051 , doi: 10.28945/3652 .

vom Berg , B.W. , Valdés , A.R. , Memari , A. , Barakat , N. and Gómez , J.M. ( 2014 ), “ Customer segmentation based on compensatory fuzzy logic within a sustainability CRM for intermodal mobility ”, in Espin , R. , Pérez , R. , Cobo , A. , Marx , J. and Valdés , A. (Eds), Soft Computing for Business Intelligence. Studies in Computational Intelligence , Springer , Berlin, Heidelberg , Vol.  537 , doi: 10.1007/978-3-642-53737-0_27 .

Wang , J.W. , Gao , F. and Ip , W.H. ( 2010 ), “ Measurement of resilience and its application to enterprise information systems ”, Enterprise Information Systems , Vol.  4 No.  2 , pp.  215 - 223 , doi: 10.1080/17517571003754561 .

Wei , J.-T. , Lee , M.-C. , Chen , H.-K. and Wu , H.-H. ( 2013 ), “ Customer relationship management in the hairdressing industry: an application of data mining techniques ”, Expert Systems with Applications , Vol.  40 No.  18 , pp.  7513 - 7518 , doi: 10.1016/j.eswa.2013.07.053 .

Yu , Y.B. and Xu , Q. ( 2022 ), “ Influencing factors of enterprise R&D investment: post-subsidy, sustainability, and heterogeneity ”, Sustainability , Vol.  14 No.  10 , doi: 10.3390/su14105759 .

Corresponding author

Related articles, we’re listening — tell us what you think, something didn’t work….

Report bugs here

All feedback is valuable

Please share your general feedback

Join us on our journey

Platform update page.

Visit emeraldpublishing.com/platformupdate to discover the latest news and updates

Questions & More Information

Answers to the most commonly asked questions here

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  

5546 Accesses

3 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

research paper on customer relationship management

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

research paper on customer relationship management

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

Wei Liu, Zongshui Wang & Hong Zhao

research paper on customer relationship management

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.

figure 1

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.

figure 2

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 .

figure 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 .

figure 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 .

figure 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 .

figure 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.

figure 7

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.

figure 8

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.

figure 9

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 .

figure 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 .

figure 11

Important topics in CRM

figure 12

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.

figure 13

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.

figure 14

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.

figure 15

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 ).

figure 16

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.

figure 17

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.

figure 18

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

IMAGES

  1. (PDF) THE IMPACT OF CUSTOMER RELATIONSHIP MANAGEMENT ON CUSTOMER

    research paper on customer relationship management

  2. (PDF) Customer relationship management in banking industry: Modern approach

    research paper on customer relationship management

  3. (PDF) A Literature Review on Customer Relationship Management

    research paper on customer relationship management

  4. (PDF) “CUSTOMER RELATIONSHIP MANAGEMENT IS THE NEED OF TODAY”

    research paper on customer relationship management

  5. (PDF) COMPETITIVE ADVANTAGE THROUGH CUSTOMER RELATIONSHIP MANAGEMENT

    research paper on customer relationship management

  6. (DOC) A Dissertation On Customer Relationship Management And Importance

    research paper on customer relationship management

VIDEO

  1. Financial Management Regular Question Paper Solution l Semester 3 l Jan 24 Exams l B Com l

  2. Customer relationship management

  3. Qualitative market research /Customer behavior week2 #marketresearch

  4. Customer Relationship Management

  5. Surprising Truth About Referring Clients

  6. Assignment 3

COMMENTS

  1. Customer relationship management: digital transformation and

    Introduction. This paper proposes a research model to analyse how customer relationship management (CRM) brings small and medium enterprises (SMEs) a dual benefit, in terms of both customer knowledge management (CKM) and innovation.

  2. Customer Relationship Management: Articles, Research, & Case Studies on

    New research on customer relationship management from Harvard Business School faculty on issues including ways to increase loyalty, determining and using customer lifetime value calculations, and the effect of using Groupon-type vouchers to promote customer growth. ... A new working paper, "To Groupon or Not to Groupon," sets out to help small ...

  3. Customer Relationship Management Research from 2000 to 2020: An

    Praveen Sahu ([email protected]) is a Professor in Central University of Rajasthan, Ajmer, India.Professor Sahu is also the Head of Department of Commerce and Dean of School of Commerce and Management. He has a rich experience of more than 18 years in teaching and research in the arena of marketing, general management and human resource management.

  4. Customer relationship management and its impact on innovation: A

    This paper constitutes, together with the bibliometric studies on CRM, one of the most contemporaneous analyses on the relevance of CRM among the most-used business solutions with the greatest projection today. ... Customer relationship management research (1992-2002) An academic literature review and classification. Marketing Intelligence ...

  5. Customer Relationship Management Research from 2000 to 2020: An

    In review papers, the maximum times CRM and electronic customer relationship management (E-CRM) were taken as a basis for reviewing. The further article has classifications within primary studies ...

  6. Customer relationship management and its impact on ...

    To consider the most relevant research papers for a comprehensive literature review, an important field in WoS is Document Type (Vallaster et al., 2019). ... Customer relationship management research (1992-2002) An academic literature review and classification. Marketing Intelligence & Planning, 23(6), 582-605. Article Google Scholar

  7. Comparative study of customer relationship management research from

    Customer relationship management (CRM) has become a critical research topic since its emergence as a research field in the 1990s (Payne and Frow 2005; Srinivasan and Moorman 2005; Verhoef et al. 2010).Firms shed light on CRM and invest substantial capital in CRM systems, since a large number of studies show that implementing CRM can boost customer satisfaction (Mithas et al. 2005), increase ...

  8. PDF Comparative study of customer relationship management research from

    RESEARCH PAPER Comparative study of customer relationship management research from East Asia, North America and Europe: A bibliometric overview Wei Liu1 & Zongshui Wang2,3 & Hong Zhao1 Abstract Customer relationship management (CRM) has become a critical research topic for scholars and practitioners, yet most existing

  9. Impact of customer relationship management (CRM) on customer

    Impact of customer relationship management (CRM) on customer satisfaction and loyalty: A systematic review March 2017 Research Journal of Business Management 6(1):86-107

  10. Effect of Customer Relationship Management on Customer ...

    A good CRM (customer relationship management) program that helps company in satisfying the customer, the research study would explore different methods and techniques for establishing effective CRM to satisfy the customers. The purpose of the study was to check the effectiveness of customer relationship management (CRM) in retaining and ...

  11. Customer Relationship Management Research from 2000 to 2020: An

    Soltani and Navimipour (2016, pp. 667-688) have reviewed CRM articles published from June 2009 to June 2015, classified the papers within the categories of electronic customer relationship management (E-CRM), knowledge management (KM) and CRM, data mining and CRM, data quality and CRM and social CRM. They mentioned in their article that the ...

  12. The impact of the customer relationship management on the organization

    Relationship Marketing (RM) principles, a developing area of modern day marketing, is the base of Customer Relationship Management (CRM) (Rahimi & Kozak, 2017). CRM concept is prevailed in the 1990s, in the domain of business. As a scholarly inquiry, it is highly paid attention and has stimulated research community and global business interest.

  13. Artificial intelligence in customer relationship management: literature

    Due to the recent development of Big Data and artificial intelligence (AI) technology solutions in customer relationship management (CRM), this paper provides a systematic overview of the field, thus unveiling gaps and providing promising paths for future research.,A total of 212 peer-reviewed articles published between 1989 and 2020 were ...

  14. Customer Relationship Management: The Evolving Role of ...

    Abstract. The purpose of the present study is twofold. The authors first present a review of the customer relationship management (CRM) literature between 2003 and 2011. Second, this study introduces an analytical framework of the evolving nature of CRM research and offers novel insights into re-thinking the role and utilisation of customer ...

  15. A Literature Review on Customer Relationship Management

    Abstract. The study examines the literature on Customer Relationship Management (CRM), with a particular emphasis on CRM's effect on client satisfaction and customer loyalty. CRM is a set of ...

  16. PDF The Impact of Effective Customer Relationship Management (Crm) on

    Key words: Customer relationship management, customer loyalty, hospitality industry, Ghana, repurchase. INTRODUCTION Customer satisfaction is a business philosophy which tends to the creation of value for customers, anticipating and managing their expectations, and demonstrating ability and responsibility to satisfy their needs, (Dominici

  17. Sustainable customer relationship management

    Sustainable customer relationship management (SCRM) is a combination of business strategy, customer-oriented business processes and computer systems that seeks to integrate sustainability into customer relationship management. ... and to establish future research challenges.,This paper have an important positive social and environmental impact ...

  18. PDF Customer relationship management and its impact on ...

    impactful tools, Customer relationship management (CRM) is one of the lead-ing business strategies and business management tools (Al-Omoush et al., 2021), and it has been shown to be crucial in developing sales, marketing, and produc-tion planning strategies. This key role of CRM is a consequence of the customer-

  19. Does Customer Relationship Management and Customer ...

    This research is motivated by the development of competition in the business world, and especially companies engaged in the service sector. It requires companies to always survive in managing their business by maintaining good relationships with customers and increasing customer satisfaction. Therefore, companies carry out marketing strategies often referred to as CRM (Customer Relationship ...

  20. Customer Relationship Management Research: An Assessment of Research

    Customer Relationship Management was first introduced by Peter Drucker and Theodore Levitt in 1960s (Cunningham, 2002). Communication means and marketing were used for better understanding the ...

  21. Evolution of customer relationship management to data mining-based

    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 ...

  22. An empirical study of customer relationship management in the

    The present paper strives to address the need for maintaining this feedback loop by offering an empirical study that articulates the key aspects of customer service. The results of the research shed light on the customer sentiments, which helps the managers at the different management levels of the company to make informed decisions.