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

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.

[1]  Paul A. Pavlou,et al.  Understanding and Mitigating Uncertainty in Online Exchange Relationships: A Principal-Agent Perspective , 2007, MIS Q..

[2]  Marko Sarstedt,et al.  Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods , 2017, Journal of the Academy of Marketing Science.

[3]  Rebecca Walker Naylor,et al.  Beyond the “Like” Button: The Impact of Mere Virtual Presence on Brand Evaluations and Purchase Intentions in Social Media Settings , 2012 .

[4]  Larry Weber,et al.  Marketing to the Social Web: How Digital Customer Communities Build Your Business , 2007 .

[5]  Sumeet Gupta,et al.  What motivates customers to participate in social commerce? The impact of technological environments and virtual customer experiences , 2014, Inf. Manag..

[6]  Marija J. Norusis,et al.  SPSS 16.0 Statistical Procedures Companion , 2003 .

[7]  Ravi Bapna,et al.  Do Your Online Friends Make You Pay? A Randomized Field Experiment on Peer Influence in Online Social Networks - Online E-Companion Appendix , 2014, Manag. Sci..

[8]  Tracy L. Tuten,et al.  Creative Strategies in Social Media Marketing: An Exploratory Study of Branded Social Content and Consumer Engagement , 2015 .

[9]  Andrew T. Stephen,et al.  The Effects of Traditional and Social Earned Media on Sales: A Study of a Microlending Marketplace , 2012 .

[10]  Gayatri Swamynathan,et al.  Do social networks improve e-commerce?: a study on social marketplaces , 2008, WOSN '08.

[11]  Sterling A. Bone,et al.  Service encounters, experiences and the customer journey: Defining the field and a call to expand our lens , 2017 .

[12]  Wenhua Liu,et al.  Understanding relationships among customer experience, engagement, and word-of-mouth intention on online brand communities: The perspective of service ecosystem , 2017, Internet Res..

[13]  Eyal Biyalogorsky,et al.  Setting Referral Fees in Affiliate Marketing , 2003 .

[14]  Stephen Henry Conway,et al.  A Cautionary Note on Data Inputs and Visual Outputs in Social Network Analysis , 2014 .

[15]  Kevin J. Trainor,et al.  Performance implications of customer-linking capabilities: Examining the complementary role of customer orientation and CRM technology , 2010 .

[16]  M. T. Ballestar,et al.  Consumer behavior on cashback websites: Network strategies , 2016 .

[17]  Dylan Walker,et al.  Creating Social Contagion Through Viral Product Design: A Randomized Trial of Peer Influence in Networks , 2010, ICIS.

[18]  Marco Bertini,et al.  Cashback Is Cash Forward: Delaying a Discount to Entice Future Spending , 2018, Journal of Marketing Research.

[19]  P. Rousseeuw Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[20]  Olivier Toubia,et al.  Deriving Value from Social Commerce Networks , 2009 .

[21]  Sumeet Gupta,et al.  Classifying, Measuring, and Predicting Users’ Overall Active Behavior on Social Networking Sites , 2014, J. Manag. Inf. Syst..

[22]  Marco Bertini,et al.  Cashback Is Cash Forward: Delaying a Discount to Increase Future Spending , 2015 .

[23]  D. Hoffman,et al.  How to acquire customers on the Web. , 2000, Harvard business review.

[24]  Calvin Jones,et al.  Understanding Digital Marketing: Marketing Strategies for Engaging the Digital Generation , 2009 .

[25]  Yi-Chun Ho,et al.  Online Cash-back Shopping: Implications for Consumers and e-Businesses , 2017, Inf. Syst. Res..

[26]  M. T. Ballestar,et al.  Social Networks on Cashback Websites , 2016 .

[27]  Ye Qiu,et al.  Increasing Retailer Loyalty Through the Use of Cash Back Rebate Sites , 2020, Mark. Sci..

[28]  Yi-Chun Ho,et al.  Online Cash-Back Shopping: Implications for Consumers and e-Businesses , 2017 .

[29]  Sinan Aral,et al.  Identifying Social Influence: A Comment on Opinion Leadership and Social Contagion in New Product Diffusion , 2010, Mark. Sci..

[30]  Peter J. Rousseeuw,et al.  Finding Groups in Data: An Introduction to Cluster Analysis , 1990 .

[31]  Patrick Gunnigle,et al.  Mapping networks: Exploring the utility of social network analysis in management research and practice , 2017 .