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A social CRM analytic framework for improving customer retention, acquisition, and conversion

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  • Lamrhari, Soumaya
  • Ghazi, Hamid El
  • Oubrich, Mourad
  • Faker, Abdellatif El

Abstract

Social Customer Relationship Management (social CRM) has become one of the central points for many companies seeking to improve their customer experience. It comprises a set of processes that allows decision-makers to analyze customer data, in order to launch an efficient, customer-centric, and cost-effective marketing strategy. Nonetheless, the inclusion of social media data in CRM introduces new challenges, as it requires advanced analytical approaches to extract actionable insight from such a huge amount of data. Thus, in this paper, we propose a social CRM analytic framework, which includes various analytical approaches, aiming at improving customer retention, acquisition, and conversion. This framework has been tested on various datasets and extensively evaluated based on several performance metrics. The obtained results suggest that the proposed framework can effectively extract relevant information and support decision-making processes. From an academics perspective, the study contributes to an understanding of customers’ experiences throughout their engagement on social media and focuses on long-term relationships with customers. From a managerial perspective, companies should leverage the insight generated through every customer engagement on social media to drive effective marketing strategies.

Suggested Citation

  • Lamrhari, Soumaya & Ghazi, Hamid El & Oubrich, Mourad & Faker, Abdellatif El, 2022. "A social CRM analytic framework for improving customer retention, acquisition, and conversion," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    • Handle: RePEc:eee:tefoso:v:174:y:2022:i:c:s0040162521007095
    DOI: 10.1016/j.techfore.2021.121275
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    as
    1. Harrigan, Paul & Soutar, Geoff & Choudhury, Musfiq Mannan & Lowe, Michelle, 2015. "Modelling CRM in a social media age," Australasian marketing journal, Elsevier, vol. 23(1), pages 27-37.
    2. Efraim Turban & Jon Outland & David King & Jae Kyu Lee & Ting-Peng Liang & Deborrah C. Turban, 2018. "Electronic Commerce 2018," Springer Texts in Business and Economics, Springer, edition 9, number 978-3-319-58715-8, August.
    3. Kumar, Anil & Luthra, Sunil & Khandelwal, Dinesh Kumar & Mehta, Rajneesh & Chaudhary, Nityanand & Bhatia, Sukhdev, 2017. "Measuring and improving customer retention at authorised automobile workshops after free services," Journal of Retailing and Consumer Services, Elsevier, vol. 39(C), pages 93-102.
    4. Babutsidze, Zakaria, 2018. "The rise of electronic social networks and implications for advertisers," Technological Forecasting and Social Change, Elsevier, vol. 137(C), pages 27-39.
    5. Bugshan, Hatem & Attar, Razaz Waheeb, 2020. "Social commerce information sharing and their impact on consumers," Technological Forecasting and Social Change, Elsevier, vol. 153(C).
    6. Naveen Gudigantala & Pelin Bicen & Mike (Tae-in) Eom, 2016. "An examination of antecedents of conversion rates of e-commerce retailers," Management Research Review, Emerald Group Publishing Limited, vol. 39(1), pages 82-114, January.
    7. Lin, Xiaolin & Kishore, Rajiv, 2021. "Social media-enabled healthcare: A conceptual model of social media affordances, online social support, and health behaviors and outcomes," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    8. Amin, Adnan & Al-Obeidat, Feras & Shah, Babar & Adnan, Awais & Loo, Jonathan & Anwar, Sajid, 2019. "Customer churn prediction in telecommunication industry using data certainty," Journal of Business Research, Elsevier, vol. 94(C), pages 290-301.
    9. Phadermrod, Boonyarat & Crowder, Richard M. & Wills, Gary B., 2019. "Importance-Performance Analysis based SWOT analysis," International Journal of Information Management, Elsevier, vol. 44(C), pages 194-203.
    10. V. Kumar & Werner Reinartz, 2018. "Customer Relationship Management," Springer Texts in Business and Economics, Springer, edition 3, number 978-3-662-55381-7, June.
    11. Engler, Tobias H. & Winter, Patrick & Schulz, Michael, 2015. "Understanding online product ratings: A customer satisfaction model," Journal of Retailing and Consumer Services, Elsevier, vol. 27(C), pages 113-120.
    12. Killian, Ginger & McManus, Kristy, 2015. "A marketing communications approach for the digital era: Managerial guidelines for social media integration," Business Horizons, Elsevier, vol. 58(5), pages 539-549.
    13. Trainor, Kevin J. & Andzulis, James (Mick) & Rapp, Adam & Agnihotri, Raj, 2014. "Social media technology usage and customer relationship performance: A capabilities-based examination of social CRM," Journal of Business Research, Elsevier, vol. 67(6), pages 1201-1208.
    14. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W. & Lessmann, Stefan, 2020. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1563-1578.
    15. Jang, Kyeong Kook & Bae, Joonheui & Kim, Kyung Hoon, 2021. "Servitization experience measurement and the effect of servitization experience on brand resonance and customer retention," Journal of Business Research, Elsevier, vol. 130(C), pages 384-397.
    16. de Vries, Lisette & Gensler, Sonja & Leeflang, Peter S.H., 2012. "Popularity of Brand Posts on Brand Fan Pages: An Investigation of the Effects of Social Media Marketing," Journal of Interactive Marketing, Elsevier, vol. 26(2), pages 83-91.
    17. Arno de Caigny & Kristof Coussement & Koen W. de Bock, 2018. "A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees," Post-Print hal-01741661, HAL.
    18. Hussein A. Al-Homery & Azizah Ahmad & Hasbullah AsharaiS, 2019. "The Core Components and Types of CRM," Pakistan Journal of Humanities and Social Sciences, International Research Alliance for Sustainable Development (iRASD), vol. 7(1), pages :121-145, March.
    19. Lee, DonHee, 2018. "Strategies for technology-driven service encounters for patient experience satisfaction in hospitals," Technological Forecasting and Social Change, Elsevier, vol. 137(C), pages 118-127.
    20. Agnihotri, Raj & Trainor, Kevin J. & Itani, Omar S. & Rodriguez, Michael, 2017. "Examining the role of sales-based CRM technology and social media use on post-sale service behaviors in India," Journal of Business Research, Elsevier, vol. 81(C), pages 144-154.
    21. Sanz-Blas, Silvia & Buzova, Daniela & Pérez-Ruiz, Pilar, 2021. "Building relational worth in an online social community through virtual structural embeddedness and relational embeddedness," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    22. Stan Lipovetsky, 2010. "Double logistic curve in regression modeling," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(11), pages 1785-1793.
    23. Zhan, Yuanzhu & Han, Runyue & Tse, Mike & Ali, Mohd Helmi & Hu, Jiayao, 2021. "A social media analytic framework for improving operations and service management: A study of the retail pharmacy industry," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    24. Geum, Youngjung & Park, Yongtae, 2016. "How to generate creative ideas for innovation: a hybrid approach of WordNet and morphological analysis," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 176-187.
    25. Chen, Yan-Cheng & Su, Chao-Ton, 2016. "Distance-based margin support vector machine for classification," Applied Mathematics and Computation, Elsevier, vol. 283(C), pages 141-152.
    26. V. Kumar & Werner Reinartz, 2018. "Future of CRM," Springer Texts in Business and Economics, in: Customer Relationship Management, edition 3, chapter 18, pages 385-404, Springer.
    27. Chang, Chun-Wei & Zhang, Jonathan Z., 2016. "The Effects of Channel Experiences and Direct Marketing on Customer Retention in Multichannel Settings," Journal of Interactive Marketing, Elsevier, vol. 36(C), pages 77-90.
    28. Rainer Alt & Olaf Reinhold, 2012. "Social Customer Relationship Management (Social CRM)," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 4(5), pages 287-291, October.
    29. Suh, Yongyoon & Woo, Chulwan & Koh, Jinhwan & Jeon, Jeonghwan, 2019. "Analysing the satisfaction of university–industry cooperation efforts based on the Kano model: A Korean case," Technological Forecasting and Social Change, Elsevier, vol. 148(C).
    30. Chandra Hendriyani & Sam'un Jaja Raharja, 2018. "Analysis building customer engagement through e-CRM in the era of digital banking in Indonesia," International Journal of Economic Policy in Emerging Economies, Inderscience Enterprises Ltd, vol. 11(5), pages 479-486.
    31. Malthouse, Edward C. & Haenlein, Michael & Skiera, Bernd & Wege, Egbert & Zhang, Michael, 2013. "Managing Customer Relationships in the Social Media Era: Introducing the Social CRM House," Journal of Interactive Marketing, Elsevier, vol. 27(4), pages 270-280.
    32. Foltean, Florin Sabin & Trif, Simona Mihaela & Tuleu, Daniela Liliana, 2019. "Customer relationship management capabilities and social media technology use: Consequences on firm performance," Journal of Business Research, Elsevier, vol. 104(C), pages 563-575.
    33. Martínez-Torres, M.R. & Arenas-Marquez, F.J. & Olmedilla, M. & Toral, S.L., 2018. "Identifying the features of reputable users in eWOM communities by using Particle Swarm Optimization," Technological Forecasting and Social Change, Elsevier, vol. 133(C), pages 220-228.
    34. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W., 2018. "A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees," European Journal of Operational Research, Elsevier, vol. 269(2), pages 760-772.
    35. Mona Jami Pour & Mahnaz Hosseinzadeh, 2021. "An integrated framework of change management for social CRM implementation," Information Systems and e-Business Management, Springer, vol. 19(1), pages 43-75, March.
    36. Hyun Gon Kim & Zhan Wang, 2019. "Defining and measuring social customer-relationship management (CRM) capabilities," Journal of Marketing Analytics, Palgrave Macmillan, vol. 7(1), pages 40-50, March.
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    2. Su, Jingqin & Zhang, Yajie & Wu, Xianyun, 2023. "How market pressures and organizational readiness drive digital marketing adoption strategies' evolution in small and medium enterprises," Technological Forecasting and Social Change, Elsevier, vol. 193(C).

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