Customer Profitability and Digitalization in the B2B Market: Systematic Literature Review
Authors
Universitas Pendidikan Indonesia (Indonesia)
Universitas Pendidikan Indonesia (Indonesia)
Universitas Pendidikan Indonesia (Indonesia)
Article Information
DOI: 10.47772/IJRISS.2025.91100234
Subject Category: Accounting
Volume/Issue: 9/11 | Page No: 2963-2980
Publication Timeline
Submitted: 2025-11-10
Accepted: 2025-11-20
Published: 2025-12-06
Abstract
This study presents a Systematic Literature Review (SLR) to synthesise and analyse the roles, contributions, challenges, and gaps in the literature at the intersection of Customer Profitability Analysis (CPA), digitalisation, and the Business-to-Business (B2B) market. Using an SLR approach, this study identifies and evaluates 30 scholarly articles that focus on CPA in B2B markets within the context of digitalization. The analysis is conducted thematically to identify models, non-risk challenges, and integration gaps. Digitalization has transformed B2B CPA from a purely historical, backward-looking model into a predictive, risk adjusted approach. The use of Machine Learning (ML) and data mining techniques (such as Boosting and Random Forest) has proven accurate in estimating customer churn probabilities and risk levels, which are then integrated into Customer Lifetime Value (CLV) calculations based on Risk Adjusted Revenue (RAR). The main non risk challenges include failures in integrating legacy systems (ERP, CRM, SCM), which hinder accurate tracing of cost to serve, as well as the need for managerial cultural change and the adoption of new hybrid technology roles.The key gap in the literature is the lack of empirical and technical studies that explicitly explain how digital technology platforms (hybrid actors such as chatbots or e marketplaces) automatically capture and allocate service costs to B2B customer accounts within traditional cost accounting models, particularly Activity Based Costing (ABC).
Keywords
Customer Profitability Analysis, Digitalization, B2B Market
Downloads
References
1. Alnofeli, K. K., Akter, S., Yanamandram, V., & Hani, U. (2026). AI-powered CRM capability model: Advancing marketing ambidexterity, profitability and competitive performance. International Journal of Information Management, 86, 102981. https://doi.org/10.1016/j.ijinfomgt.2025.102981 [Google Scholar] [Crossref]
2. Anan, L. (2023, October). Fintechs: A new paradigm of growth . Mckinsey & Company. [Google Scholar] [Crossref]
3. Bao, C., Li, M., & Pei, Y. (2026). Customer flow spillovers in retailers’ short- and long-term decisions: Profitability and dynamic mechanisms. Journal of Retailing and Consumer Services, 88, 104450. https://doi.org/10.1016/j.jretconser.2025.104450 [Google Scholar] [Crossref]
4. Bonney, L., Beeler, L. L., Johnson, R. W., & Hochstein, B. (2022). The salesperson as a knowledge broker: The effect of sales influence tactics on customer learning, purchase decision, and profitability. Industrial Marketing Management, 104, 352–365. https://doi.org/10.1016/j.indmarman.2022.05.001 [Google Scholar] [Crossref]
5. Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company. [Google Scholar] [Crossref]
6. Clemente-Almendros, J. A., Pelaez, A. D., Quispe, G. C., & Velarde Molina, J. F. (2025). Emerging countries could be different for MSMES: Digitalization and the mediation effects of innovation confronted to environmental practices. International Journal of Innovation Studies, 9(4), 354–365. https://doi.org/10.1016/j.ijis.2025.08.001 [Google Scholar] [Crossref]
7. Coussement, K., & De Bock, K. W. (2013). Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning. Journal of Business Research, 66(9), 1629–1636. https://doi.org/10.1016/j.jbusres.2012.12.008 [Google Scholar] [Crossref]
8. Dawson, B., Young, L., Murray, J. M., & Wilkinson, I. (2017). Drivers of supplier-customer relationship profitability in China: Assessing International Joint Ventures versus State Owned Enterprises. Industrial Marketing Management, 66, 29–41. https://doi.org/10.1016/j.indmarman.2017.06.009 [Google Scholar] [Crossref]
9. Erensal, Y., Oncan, T., & Demircan, M. (2006). Determining key capabilities in technology management using fuzzy analytic hierarchy process: A case study of Turkey . Information Sciences, 18(176), 2755–2770. [Google Scholar] [Crossref]
10. Firmansyah, E. B., Machado, M. R., & Moreira, J. L. R. (2024). How can Artificial Intelligence (AI) be used to manage Customer Lifetime Value (CLV)—A systematic literature review. International Journal of Information Management Data Insights, 4(2), 100279. https://doi.org/10.1016/j.jjimei.2024.100279 [Google Scholar] [Crossref]
11. Gao, J., Opute, A. P., Jawad, C., & Zhan, M. (2025). The influence of artificial intelligence chatbot problem solving on customers’ continued usage intention in e-commerce platforms: an expectation-confirmation model approach. Journal of Business Research, 200, 115661. https://doi.org/10.1016/j.jbusres.2025.115661 [Google Scholar] [Crossref]
12. Homburg, C., Workman, J. P., Jensen, O., Johann, C., Loewner, J., Model, A., & Prauschke, C. (2002). A Configurational Perspective on Key Account Management. , . Journal of Marketing, 66, 38–60. [Google Scholar] [Crossref]
13. Huang, T.-L. (2025). Digital transformation and business performance in China. Asia Pacific Management Review, 100400. https://doi.org/10.1016/j.apmrv.2025.100400 [Google Scholar] [Crossref]
14. Huang, W., Bai, Y., & Luo, H. (2024). Customer identity concealing and insider selling profitability: Evidence from China. Journal of Corporate Finance, 85, 102566. https://doi.org/10.1016/j.jcorpfin.2024.102566 [Google Scholar] [Crossref]
15. Jarvinen, J., & Vaataaja, K. (2018). Customer Profitability Analysis Using Time-Driven Activity-Based Costing. Three Interventionist Case Studies. The Nordic Journal of Business, Forthcoming. [Google Scholar] [Crossref]
16. Kitchenham, B. (2004). Procedures for Performing Systematic Reviews (Vol. 33). Keele University. [Google Scholar] [Crossref]
17. Kyrdoda, Y., Marzi, G., & Vianelli, D. (2025). Digital transformation in the B2B context: A review, theorisation and future perspectives. Industrial Marketing Management, 129, 182–196. https://doi.org/10.1016/j.indmarman.2025.05.008 [Google Scholar] [Crossref]
18. Lau, H., Nakandala, D., Samaranayake, P., & Shum, P. (2016). A hybrid multi-criteria decision model for supporting customer-focused profitability analysis. Industrial Management & Data Systems, 116(6), 1105–1130. https://doi.org/10.1108/IMDS-10-2015-0410 [Google Scholar] [Crossref]
19. Lemmens, A., & Gupta, S. (2013). Managing churn to maximize profits. [Google Scholar] [Crossref]
20. Lin, C., & Bowman, D. (2022). The impact of introducing a customer loyalty program on category sales and profitability. Journal of Retailing and Consumer Services, 64, 102769. https://doi.org/10.1016/j.jretconser.2021.102769 [Google Scholar] [Crossref]
21. Liu, Y. (David), Sun, J., Zhang, Z. (Justin), Wu, M., Sima, H., & Ooi, Y. M. (2024). How AI Impacts Companies’ Dynamic Capabilities. Research-Technology Management, 67(3), 64–76. https://doi.org/10.1080/08956308.2024.2324407 [Google Scholar] [Crossref]
22. Lueg, R., & Ilieva, D. (2024). Customer Profitability Analysis in decision-making–The roles of customer characteristics, cost structures, and strategizing. PLOS ONE, 19(5), e0296974. https://doi.org/10.1371/journal.pone.0296974 [Google Scholar] [Crossref]
23. Lundin, L., & Kindström, D. (2023). Digitalizing customer journeys in B2B markets. Journal of Business Research, 157, 113639. https://doi.org/10.1016/j.jbusres.2022.113639 [Google Scholar] [Crossref]
24. Machado, M. R., & Karray, S. (2022). Integrating Customer Portfolio Theory and the Multiple Sources of Risk Approaches to Model Risk-Adjusted Revenue. IFAC-PapersOnLine, 55(16), 356–363. https://doi.org/10.1016/j.ifacol.2022.09.050 [Google Scholar] [Crossref]
25. Mahlamäki, T., Storbacka, K., Pylkkönen, S., & Ojala, M. (2020). Adoption of digital sales force automation tools in supply chain: Customers’ acceptance of sales configurators. Industrial Marketing Management, 91, 162–173. https://doi.org/10.1016/j.indmarman.2020.08.024 [Google Scholar] [Crossref]
26. Martínez-López, F. J., & Casillas, J. (2013). Artificial intelligence-based systems applied in industrial marketing: An historical overview, current and future insights. Industrial Marketing Management, 42(4), 489–495. https://doi.org/10.1016/j.indmarman.2013.03.001 [Google Scholar] [Crossref]
27. Meyer, A., Glock, K., & Radaschewski, F. (2021). Planning profitable tours for field sales forces: A unified view on sales analytics and mathematical optimization. Omega, 105, 102518. https://doi.org/10.1016/j.omega.2021.102518 [Google Scholar] [Crossref]
28. Mora Cortez, R., Clarke, A. H., & Freytag, P. V. (2025). The future of B2B trade shows: Drivers of transformation from a process view. Industrial Marketing Management, 129, 151–165. https://doi.org/10.1016/j.indmarman.2025.07.010 [Google Scholar] [Crossref]
29. Neslin, S. A., Gupta, S., Kamakura, W., Lu, J., & Mason, C. H. (2006). Defection Detection: Measuring and Understanding the Predictive Accuracy of Customer Churn Models. Journal of Marketing Research, 43(2), 204–211. https://doi.org/10.1509/jmkr.43.2.204 [Google Scholar] [Crossref]
30. Oliveira, F., Belitski, M., & Perez-Vega, R. (2025). Sales digitization and sales process optimisation for firm performance: Evidence from European firms. Technological Forecasting and Social Change, 217, 124172. https://doi.org/10.1016/j.techfore.2025.124172 [Google Scholar] [Crossref]
31. Olson, D. (2004). Comparison of weights in TOPSIS models. Mathematical and Computer Modelling, 40, 721–727. [Google Scholar] [Crossref]
32. Pan, Y., Russell, G., Gruca, T. S., & Li, C. (2025). Multicategory purchase behavior: basket choice, shopping frequency, and promotional analysis. Journal of Retailing. https://doi.org/10.1016/j.jretai.2025.08.002 [Google Scholar] [Crossref]
33. Purmonen, A., Jaakkola, E., & Terho, H. (2023). B2B customer journeys: Conceptualization and an integrative framework. Industrial Marketing Management, 113, 74–87. https://doi.org/10.1016/j.indmarman.2023.05.020 [Google Scholar] [Crossref]
34. Rapp, A. A., Bachrach, D. G., Flaherty, K. E., Hughes, D. E., Sharma, A., & Voorhees, C. M. (2017). The Role of the Sales-Service Interface and Ambidexterity in the Evolving Organization. Journal of Service Research, 20(1), 59–75. https://doi.org/10.1177/1094670516679274 [Google Scholar] [Crossref]
35. Rauyruen, P., & Miller, K. E. (2007). Relationship quality as a predictor of B2B customer loyalty. Journal of Business Research, 60(1), 21–31. https://doi.org/10.1016/j.jbusres.2005.11.006 [Google Scholar] [Crossref]
36. Rebelo, C. G. S., Pereira, M. T., Silva, J. F. G., Ferreira, L. P., Sá, J. C., & Mota, A. M. (2021). After sales service: key settings for improving profitability and customer satisfaction. Procedia Manufacturing, 55, 463–470. https://doi.org/10.1016/j.promfg.2021.10.063 [Google Scholar] [Crossref]
37. Reier Forradellas, R. F., & Garay Gallastegui, L. M. (2021). Digital Transformation and Artificial Intelligence Applied to Business: Legal Regulations, Economic Impact and Perspective. Laws, 10(3), 70. https://doi.org/10.3390/laws10030070 [Google Scholar] [Crossref]
38. Ruch, M., & Sackmann, S. (2012). Integrating management of customer value and risk in e-commerce. Information Systems and E-Business Management, 10(1), 101–116. https://doi.org/10.1007/s10257-010-0152-2 [Google Scholar] [Crossref]
39. Ruiz‐de‐Arbulo‐Lopez, P., Fortuny‐Santos, J., & Cuatrecasas‐Arbós, L. (2013). Lean manufacturing: costing the value stream. Industrial Management & Data Systems, 113(5), 647–668. https://doi.org/10.1108/02635571311324124 [Google Scholar] [Crossref]
40. Ryu, D. Y., Ko, Y. K., & Ko, Y. D. (2025). RFM analysis for profiling profitable customers based on characteristics of the hotel industry. International Journal of Hospitality Management, 129, 104176. https://doi.org/10.1016/j.ijhm.2025.104176 [Google Scholar] [Crossref]
41. Sastypratiwi, H., & Nyoto, R. (2020). Analisis Data Artikel Sistem Pakar Menggunakan Metode Systematic Review. Jurnal Edukasi Dan Penelitian Informatika (JEPIN), 6, 250–257. https://doi.org/10.26418/jp.v6i2.40914 [Google Scholar] [Crossref]
42. Singh, S., Murthi, B. P. S., & Steffes, E. (2013). Developing a measure of risk adjusted revenue (RAR) in credit cards market: Implications for customer relationship management. European Journal of Operational Research, 224(2), 425–434. https://doi.org/10.1016/j.ejor.2012.08.007 [Google Scholar] [Crossref]
43. Taheri, S. G., Navabakhsh, M., Tohidi, H., & Mohammaditabar, D. (2024). A system dynamics model for optimum time, profitability, and customer satisfaction in omni-channel retailing. Journal of Retailing and Consumer Services, 78, 103784. https://doi.org/10.1016/j.jretconser.2024.103784 [Google Scholar] [Crossref]
44. Tamaddoni Jahromi, A., Stakhovych, S., & Ewing, M. (2014). Managing B2B customer churn, retention and profitability. Industrial Marketing Management, 43(7), 1258–1268. https://doi.org/10.1016/j.indmarman.2014.06.016 [Google Scholar] [Crossref]
45. Vaid, S., & Feinberg, F. M. (2025). Digital lead generation platforms: Rightsizing the seller base. Journal of Retailing. https://doi.org/10.1016/j.jretai.2025.06.007 [Google Scholar] [Crossref]
46. Varsha P S, Chakraborty, A., & Kar, A. K. (2024). How to Undertake an Impactful Literature Review: Understanding Review Approaches and Guidelines for High-impact Systematic Literature Reviews. South Asian Journal of Business and Management Cases, 13(1), 18–35. https://doi.org/10.1177/22779779241227654 [Google Scholar] [Crossref]
47. Voorhees, C. M., Boylan, N. M., Bauer, C., Fombelle, P. W., & Jenkins, M. R. (2025). Conceptualizing post-sales relationship management in B2B markets: review, synthesis, and recommendations for future research. Journal of Business Research, 201, 115700. https://doi.org/10.1016/j.jbusres.2025.115700 [Google Scholar] [Crossref]
48. Votto, A. M., Valecha, R., Najafirad, P., & Rao, H. R. (2021). Artificial Intelligence in Tactical Human Resource Management: A Systematic Literature Review. International Journal of Information Management Data Insights, 1(2), 100047. https://doi.org/10.1016/j.jjimei.2021.100047 [Google Scholar] [Crossref]
49. Warner, K. S. R., & Wäger, M. (2019). Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal. Long Range Planning, 52(3), 326–349. https://doi.org/10.1016/j.lrp.2018.12.001 [Google Scholar] [Crossref]
50. Watajdid, N. I., Lathifah, A., Andini, D. S., & Fitroh, F. (2021). Systematic Literature Review: Peran Media Sosial Instagram Terhadap Perkembangan Digital Marketing. Jurnal Sains Pemasaran Indonesia (Indonesian Journal of Marketing Science). Urnal Sains Pemasaran Indonesia (Indonesian Journal of Marketing Science), 20(2), 163–179. [Google Scholar] [Crossref]
51. Wiersema, F. (2013). The B2B Agenda: The current state of B2B marketing and a look ahead. Industrial Marketing Management, 42(4), 470–488. https://doi.org/10.1016/j.indmarman.2013.02.015 [Google Scholar] [Crossref]
52. Wijekoon, S., Vesal, M., & O’Cass, A. (2025). A configurational approach to strategic entrepreneurship: Unlocking customer success in new ventures in emerging economies. Journal of Business Research, 201, 115689. https://doi.org/10.1016/j.jbusres.2025.115689 [Google Scholar] [Crossref]
53. Wu, M., Liu, Y., Chung, H. F. L., & Guo, S. (2022). When and how mobile payment platform complementors matter in cross-border B2B e-commerce ecosystems? An integration of process and modularization analysis. Journal of Business Research, 139, 843–854. https://doi.org/10.1016/j.jbusres.2021.10.019 [Google Scholar] [Crossref]
54. Wu, Q., Khattak, M. S., Anwar, M., Hani, I. B., & Hujran, O. (2025). CEO passion, digitalization, and family firm performance: A socio-emotional wealth perspective. Digital Business, 5(2), 100144. https://doi.org/10.1016/j.digbus.2025.100144 [Google Scholar] [Crossref]
55. Yavuz, M. S., & Çalik, H. (2025). AI and ML patent intensity and firm performance: A machine learning-based lagged analysis. European Research on Management and Business Economics, 31(3), 100291. https://doi.org/10.1016/j.iedeen.2025.100291 [Google Scholar] [Crossref]
56. Zhang, Q., & Seetharaman, P. B. (2018). Assessing lifetime profitability of customers with purchasing cycles. Marketing Intelligence & Planning, 36(2), 276–289. https://doi.org/10.1108/MIP-03-2017-0059 [Google Scholar] [Crossref]
Metrics
Views & Downloads
Similar Articles
- The Role of Value and Growth Stocks in Portfolio Returns: Insights From the Nigerian Stock Market
- The Impact of Environmental, Social, Governance (ESG) and Profitability on Firm Value Moderated by Firm Size
- Assessment of the Impact of Environmental Operating Costs on Return on Assets: Evidence from Listed Breweries in Nigeria
- Mobile Money and Digital Financial Services Ecosystem in Adamawa State
- A Quantitative Approach of Professional Skepticism and Fraud Detection among Malaysian Internal Auditors