An Integrated Framework to Recommend Personalized Retention Actions to Control B2C E-Commerce Customer Churn

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2015 by IJETT Journal
Volume-27 Number-3
Year of Publication : 2015
Authors : Shini Renjith
DOI :  10.14445/22315381/IJETT-V27P227


Shini Renjith"An Integrated Framework to Recommend Personalized Retention Actions to Control B2C E-Commerce Customer Churn", International Journal of Engineering Trends and Technology (IJETT), V27(3),152-157 September 2015. ISSN:2231-5381. published by seventh sense research group

Considering the level of competition prevailing in Business-to-Consumer (B2C) E-Commerce domain and the huge investments required to attract new customers, firms are now giving more focus to reduce their customer churn rate. Churn rate is the ratio of customers who part away with the firm in a specific time period. One of the best mechanism to retain current customers is to identify any potential churn and respond fast to prevent it. Detecting early signs of a potential churn, recognizing what the customer is looking for by the movement and automating personalized win back campaigns are essential to sustain business in this era of competition. E-Commerce firms normally possess large volume of data pertaining to their existing customers like transaction history, search history, periodicity of purchases, etc. Data mining techniques can be applied to analyse customer behaviour and to predict the potential customer attrition so that special marketing strategies can be adopted to retain them. This paper proposes an integrated model that can predict customer churn and also recommend personalized win back actions.


[1] Hennig?Thurau, Thorsten, and Alexander Klee. "The impact of customer satisfaction and relationship quality on customer retention: A critical reassessment and model development." Psychology & Marketing 14, no. 8 (1997): 737-764.
[2] Bolton, Ruth N., P. K. Kannan, and Matthew D. Bramlett. "Implications of loyalty program membership and service experiences for customer retention and value." Journal of the academy of marketing science 28, no. 1 (2000): 95-108.
[3] Neslin, Scott A., Sunil Gupta, Wagner Kamakura, Junxiang Lu, and Charlotte H. Mason. "Defection detection: Measuring and understanding the predictive accuracy of customer churn models." Journal of marketing research 43, no. 2 (2006): 204-211.
[4] Jamal, Zainab, and Randolph E. Bucklin. "Improving the diagnosis and prediction of customer churn: A heterogeneous hazard modeling approach." Journal of Interactive Marketing 20, no. 3?4 (2006): 16-29.
[5] Hadden, John, Ashutosh Tiwari, Rajkumar Roy, and Dymitr Ruta. "Churn prediction: Does technology matter." International Journal of Intelligent Technology 1, no. 2 (2006): 104-110.
[6] Van den Poel, Dirk, and Bart Lariviere. "Customer attrition analysis for financial services using proportional hazard models." European Journal of Operational Research 157, no. 1 (2004): 196-217.
[7] Nath, Shyam V., and Ravi S. Behara. "Customer churn analysis in the wireless industry: A data mining approach." In Proceedings-annual meeting of the decision sciences institute, pp. 505-510. 2003.
[8] Lu, Junxiang, and O. Park. "Modeling customer lifetime value using survival analysis—an application in the telecommunications industry." Data Mining Techniques (2003): 120-128.
[9] Hung, Shin-Yuan, David C. Yen, and Hsiu-Yu Wang. "Applying data mining to telecom churn management." Expert Systems with Applications 31, no. 3 (2006): 515-524.
[10] Adomavicius, Gediminas, Zan Huang, and Alexander Tuzhilin. "Personalization and recommender systems." State-of-the-art decision making tools in the information-intensive age (2008): 55-100.
[11] Walter, Frank Edward, Stefano Battiston, and Frank Schweitzer. "A model of a trust-based recommendation system on a social network." Autonomous Agents and Multi-Agent Systems 16, no. 1 (2008): 57-74.
[12] Renjith, Shini, and C. Anjali. "A Personalized Travel Recommender Model Based on Content-based Prediction and Collaborative Recommendation." International Journal of Computer Science and Mobile Computing, ICMIC13 (2013): 66-73.
[13] Adomavicius, Gediminas, and Alexander Tuzhilin. "Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions." Knowledge and Data Engineering, IEEE Transactions on 17, no. 6 (2005): 734-749.
[14] Renjith, Shini, and C. Anjali. "A personalized mobile travel recommender system using hybrid algorithm." In Computational Systems and Communications (ICCSC), 2014 First International Conference on, pp. 12-17. IEEE, 2014.
[15] Renjith, Shini, and C. Anjali. "Fitness Function in Genetic Algorithm based Information Filtering-A Survey." International Journal of Computer Science and Mobile Computing, ICMIC13 (2013): 80-86.
[16] Hajeer, Safaa I. "Comparison on the Effectiveness of Different Statistical Similarity Measures." International Journal of Computer Applications (0975–8887) 53, no. 8 (2012): 14-19.
[17] Karp, Andrew H. "Using logistic regression to predict customer retention." The Northeast SAS User Group (NESUG) (1998).
[18] Cramer J.S. The Logit Model: An Introduction. Edward Arnold (1991). ISBN 0304541113.
[19] Kumar, Narander, Vishal Verma, and Vipin Saxena. "Cluster Analysis in Data Mining using K-Means Method." International Journal of Computer Applications 76, no. 12 (2013): 11-14.
[20] Kaur, Manpreet, and Usvir Kaur. "Comparison Between K-Means and Hierarchical Algorithm Using Query Redirection." International Journal of Advanced Research in Computer Science and Software Engineering 3, no. 7 (2013).
[21] Srinivasan, Srini S., Rolph Anderson, and Kishore Ponnavolu. "Customer loyalty in e-commerce: an exploration of its antecedents and consequences." Journal of retailing 78, no. 1 (2002): 41-50.
[22] Van Meteren, Robin, and Maarten Van Someren. "Using content-based filtering for recommendation." In Proceedings of the Machine Learning in the New Information Age: MLnet/ECML2000 Workshop, pp. 47-56. 2000.
[23] Ekstrand, Michael D., John T. Riedl, and Joseph A. Konstan. "Collaborative filtering recommender systems." Foundations and Trends in Human-Computer Interaction 4, no. 2 (2011): 81-173.

Business-to-Consumer (B2C) E-commerce, Customer Churn, Predictive Analytics, Cluster Analysis, Personalization.