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Customer segmentation in e-commerce: Applications to the cashback business model

Author

Listed:
  • Ballestar, María Teresa
  • Grau-Carles, Pilar
  • Sainz, Jorge

Abstract

This paper presents a segmentation of cashback website customers. The segmentation is based on customers' commercial activity and role within the site's social network. In this social network, customers profit from the transactions they make on affiliate websites. Mixing traditional marketing strategies with word-of-mouth recommendations is crucial for the success of this business model because these recommendations boost new customer acquisitions and strengthen the loyalty of existing customers. This study shows how the customer's role within the cashback website's social network determines the customer's behavior and commercial activity on the website. The segmentation presented describes the customer journey in terms of customer profitability and seniority. The findings explain customer behavior in e-commerce and the value of applying personalized retention strategies to each cluster rather than generic strategies or customer acquisition strategies. This paper describes how customers move between clusters, enabling practitioners to increase customer loyalty and long-term profitability.

Suggested Citation

  • Ballestar, María Teresa & Grau-Carles, Pilar & Sainz, Jorge, 2018. "Customer segmentation in e-commerce: Applications to the cashback business model," Journal of Business Research, Elsevier, vol. 88(C), pages 407-414.
    • Handle: RePEc:eee:jbrese:v:88:y:2018:i:c:p:407-414
    DOI: 10.1016/j.jbusres.2017.11.047
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    Cited by:

    1. Ata Allah Taleizadeh & Alireza Mahmoudzade Varzi & Hadi Akbarzadeh Khorshidi & Mahsa Noori-daryan, 2024. "Retail pricing, cashback and refund decisions in a supply chain with e-shop and direct channels," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 23(2), pages 140-163, April.
    2. Ricardo Correia & Tássila Lima & Raquel Meneses, 2025. "Navigating tomorrow’s wallet: unveiling the distinctive financial journeys across generations," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 30(2), pages 1-17, June.
    3. Kokkoris, Michail D. & Hoelzl, Erik & Kamleitner, Bernadette, 2020. "Self-found, spellbound: The sense of own discovery shapes customer bonds with service venues," Journal of Business Research, Elsevier, vol. 113(C), pages 303-316.
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    6. Tueanrat, Yanika & Papagiannidis, Savvas & Alamanos, Eleftherios, 2021. "A conceptual framework of the antecedents of customer journey satisfaction in omnichannel retailing," Journal of Retailing and Consumer Services, Elsevier, vol. 61(C).
    7. Zuzana Svobodová & Jaroslava Rajchlová, 2020. "Strategic Behavior of E-Commerce Businesses in Online Industry of Electronics from a Customer Perspective," Administrative Sciences, MDPI, vol. 10(4), pages 1-24, October.
    8. Olha Fedirko & Tetiana Zatonatska & Tomasz Wolowiec & Stanislaw Skowron, 2021. "Data Science and Marketing in E-Commerce Amid COVID-19 Pandemic," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 1), pages 3-16.
    9. Baoliang Hu & Wei Huang & Shuai Yan & Guang Liu & Tao Zhang, 2020. "Business Model Design and Customer Loyalty: The Mediating Role of Customer Citizenship Behavior," Sustainability, MDPI, vol. 12(17), pages 1-14, August.
    10. Xu, Lina & Roy, Abhijit, 2022. "Cashback as cash forward: The serial mediating effect of time/effort and money savings," Journal of Business Research, Elsevier, vol. 149(C), pages 30-37.
    11. Fatma M. Talaat & Abdussalam Aljadani & Bshair Alharthi & Mohammed A. Farsi & Mahmoud Badawy & Mostafa Elhosseini, 2023. "A Mathematical Model for Customer Segmentation Leveraging Deep Learning, Explainable AI, and RFM Analysis in Targeted Marketing," Mathematics, MDPI, vol. 11(18), pages 1-26, September.
    12. Ballestar, María Teresa & Doncel, Luis Miguel & Sainz, Jorge & Ortigosa-Blanch, Arturo, 2019. "A novel machine learning approach for evaluation of public policies: An application in relation to the performance of university researchers," Technological Forecasting and Social Change, Elsevier, vol. 149(C).
    13. Joni Salminen & Mekhail Mustak & Muhammad Sufyan & Bernard J. Jansen, 2023. "How can algorithms help in segmenting users and customers? A systematic review and research agenda for algorithmic customer segmentation," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 677-692, December.
    14. repec:ers:journl:v:xxiv:y:2021:i:special2:p:3-16 is not listed on IDEAS
    15. Ballestar, María Teresa & Díaz-Chao, Ángel & Sainz, Jorge & Torrent-Sellens, Joan, 2021. "Impact of robotics on manufacturing: A longitudinal machine learning perspective," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    16. Kessara Kanchanapoom & Jongsawas Chongwatpol, 2023. "Integrated customer lifetime value (CLV) and customer migration model to improve customer segmentation," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(2), pages 172-185, June.
    17. Afonso Vieira, Valter & Agnihotri, Raj & de Almeida, Marcos Inácio Severo & Lopes, Evandro Luiz, 2022. "How cashback strategies yield financial benefits for retailers: The mediating role of consumers’ program loyalty," Journal of Business Research, Elsevier, vol. 141(C), pages 200-212.
    18. Lewlisa Saha & Hrudaya Kumar Tripathy & Soumya Ranjan Nayak & Akash Kumar Bhoi & Paolo Barsocchi, 2021. "Amalgamation of Customer Relationship Management and Data Analytics in Different Business Sectors—A Systematic Literature Review," Sustainability, MDPI, vol. 13(9), pages 1-35, May.
    19. Farid Huseynov & Sevgi Özkan Yıldırım, 2019. "Online Consumer Typologies and Their Shopping Behaviors in B2C E-Commerce Platforms," SAGE Open, , vol. 9(2), pages 21582440198, May.
    20. Ballestar, María Teresa & García-Lazaro, Aida & Sainz, Jorge & Sanz, Ismael, 2022. "Why is your company not robotic? The technology and human capital needed by firms to become robotic," Journal of Business Research, Elsevier, vol. 142(C), pages 328-343.
    21. Ballestar, María Teresa & Mir, Miguel Cuerdo & Pedrera, Luis Miguel Doncel & Sainz, Jorge, 2024. "Effectiveness of tutoring at school: A machine learning evaluation," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    22. Griva, Anastasia, 2022. "“I can get no e-satisfaction†. What analytics say? Evidence using satisfaction data from e-commerce," Journal of Retailing and Consumer Services, Elsevier, vol. 66(C).
    23. Halpern, Nigel & Mwesiumo, Deodat & Budd, Thomas & Suau-Sanchez, Pere & Bråthen, Svein, 2021. "Segmentation of passenger preferences for using digital technologies at airports in Norway," Journal of Air Transport Management, Elsevier, vol. 91(C).
    24. Tueanrat, Yanika & Papagiannidis, Savvas & Alamanos, Eleftherios, 2021. "Going on a journey: A review of the customer journey literature," Journal of Business Research, Elsevier, vol. 125(C), pages 336-353.

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