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Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting

Citations

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Cited by:

  1. Risselada, Hans & Verhoef, Peter C. & Bijmolt, Tammo H.A., 2010. "Staying Power of Churn Prediction Models," Journal of Interactive Marketing, Elsevier, vol. 24(3), pages 198-208.
  2. K. W. De Bock & D. Van Den Poel, 2012. "Reconciling Performance and Interpretability in Customer Churn Prediction using Ensemble Learning based on Generalized Additive Models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/805, Ghent University, Faculty of Economics and Business Administration.
  3. Coussement, Kristof & Benoit, Dries Frederik & Van den Poel, Dirk, 2009. "Improved Marketing Decision Making in a Customer Churn Prediction Context Using Generalized Additive Models," Working Papers 2009/18, Hogeschool-Universiteit Brussel, Faculteit Economie en Management.
  4. M. Ballings & D. Van Den Poel & E. Verhagen, 2013. "Evaluating the Added Value of Pictorial Data for Customer Churn Prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/869, Ghent University, Faculty of Economics and Business Administration.
  5. Uner, M.Mithat & Guven, Faruk & Cavusgil, S.Tamer, 2020. "Churn and loyalty behavior of Turkish digital natives: Empirical insights and managerial implications," Telecommunications Policy, Elsevier, vol. 44(4).
  6. A. Prinzie & D. Van Den Poel, 2005. "Constrained optimization of data-mining problems to improve model performance: A direct-marketing application," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/298, Ghent University, Faculty of Economics and Business Administration.
  7. Chou, Ping & Chuang, Howard Hao-Chun & Chou, Yen-Chun & Liang, Ting-Peng, 2022. "Predictive analytics for customer repurchase: Interdisciplinary integration of buy till you die modeling and machine learning," European Journal of Operational Research, Elsevier, vol. 296(2), pages 635-651.
  8. Makoto Abe, 2015. "Deriving Customer Lifetime Value from RFM Measures:Insights into Customer Retention and Acquisition," CIRJE F-Series CIRJE-F-962, CIRJE, Faculty of Economics, University of Tokyo.
  9. D. Van den Poel, 2003. "Predicting Mail-Order Repeat Buying. Which Variables Matter?," Review of Business and Economic Literature, KU Leuven, Faculty of Economics and Business (FEB), Review of Business and Economic Literature, vol. 0(3), pages 371-404.
  10. Yeung, Alice H.W. & Lo, Victor H.Y. & Yeung, Andy C.L. & Cheng, T.C. Edwin, 2008. "Specific customer knowledge and operational performance in apparel manufacturing," International Journal of Production Economics, Elsevier, vol. 114(2), pages 520-533, August.
  11. M. Ballings & D. Van Den Poel, 2012. "Kernel Factory: An Ensemble of Kernel Machines," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/825, Ghent University, Faculty of Economics and Business Administration.
  12. Van den Poel, Dirk & Buckinx, Wouter, 2005. "Predicting online-purchasing behaviour," European Journal of Operational Research, Elsevier, vol. 166(2), pages 557-575, October.
  13. Bogomolova, Svetlana, 2016. "Determinants of ex-customer winback in financial services," Journal of Retailing and Consumer Services, Elsevier, vol. 32(C), pages 1-6.
  14. Hanen Khanchel & Karim Ben Kahla, 2019. "Job Dissatisfaction and Turnover Crises in Tunisia," Business and Management Research, Business and Management Research, Sciedu Press, vol. 8(3), pages 53-73, September.
  15. Mihai ȚICHINDELEAN & Claudia OGREAN & Mihaela HERCIU, 2024. "Do Loyal Customers Buy Differently? Examining Customers’ Loyalty In A Self-Service Setting," Studies in Business and Economics, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 19(1), pages 350-367, April.
  16. Chandrasekhar Valluri & Sudhakar Raju & Vivek H. Patil, 2022. "Customer determinants of used auto loan churn: comparing predictive performance using machine learning techniques," Journal of Marketing Analytics, Palgrave Macmillan, vol. 10(3), pages 279-296, September.
  17. Melis, Kristina & Campo, Katia & Breugelmans, Els & Lamey, Lien, 2015. "The Impact of the Multi-channel Retail Mix on Online Store Choice: Does Online Experience Matter?," Journal of Retailing, Elsevier, vol. 91(2), pages 272-288.
  18. K. Coussement & D. Van Den Poel, 2006. "Churn Prediction in Subscription Services: an Application of Support Vector Machines While Comparing Two Parameter-Selection Techniques," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 06/412, Ghent University, Faculty of Economics and Business Administration.
  19. E Lima & C Mues & B Baesens, 2009. "Domain knowledge integration in data mining using decision tables: case studies in churn prediction," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(8), pages 1096-1106, August.
  20. Verbeke, Wouter & Dejaeger, Karel & Martens, David & Hur, Joon & Baesens, Bart, 2012. "New insights into churn prediction in the telecommunication sector: A profit driven data mining approach," European Journal of Operational Research, Elsevier, vol. 218(1), pages 211-229.
  21. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
  22. Gerpott, Torsten J. & Ahmadi, Nima & Weimar, Daniel, 2015. "Who is (not) convinced to withdraw a contract termination announcement? – A discriminant analysis of mobile communications customers in Germany," Telecommunications Policy, Elsevier, vol. 39(1), pages 38-52.
  23. López-Díaz, María Concepción & López-Díaz, Miguel & Martínez-Fernández, Sergio, 2023. "On the optimal binary classifier with an application," Computational Statistics & Data Analysis, Elsevier, vol. 181(C).
  24. Gattermann-Itschert, Theresa & Thonemann, Ulrich W., 2021. "How training on multiple time slices improves performance in churn prediction," European Journal of Operational Research, Elsevier, vol. 295(2), pages 664-674.
  25. Tang, Leilei & Thomas, Lyn & Fletcher, Mary & Pan, Jiazhu & Marshall, Andrew, 2014. "Assessing the impact of derived behavior information on customer attrition in the financial service industry," European Journal of Operational Research, Elsevier, vol. 236(2), pages 624-633.
  26. Ballings, Michel & Van den Poel, Dirk, 2015. "CRM in social media: Predicting increases in Facebook usage frequency," European Journal of Operational Research, Elsevier, vol. 244(1), pages 248-260.
  27. Vera Miguéis & Dirk Poel & Ana Camanho & João Falcão e Cunha, 2012. "Predicting partial customer churn using Markov for discrimination for modeling first purchase sequences," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 6(4), pages 337-353, December.
  28. K. Coussement & D. Van Den Poel, 2008. "Improving Customer Attrition Prediction by Integrating Emotions from Client/Company Interaction Emails and Evaluating Multiple Classifiers," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 08/527, Ghent University, Faculty of Economics and Business Administration.
  29. V. L. Miguéis & D. Van Den Poel & A.S. Camanho & J. Falcao E Cunha, 2012. "Modeling Partial Customer Churn: On the Value of First Product-Category Purchase Sequences," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/790, Ghent University, Faculty of Economics and Business Administration.
  30. Jerath, Kinshuk & Fader, Peter S. & Hardie, Bruce G.S., 2016. "Customer-base analysis using repeated cross-sectional summary (RCSS) data," European Journal of Operational Research, Elsevier, vol. 249(1), pages 340-350.
  31. Martínez, Andrés & Schmuck, Claudia & Pereverzyev, Sergiy & Pirker, Clemens & Haltmeier, Markus, 2020. "A machine learning framework for customer purchase prediction in the non-contractual setting," European Journal of Operational Research, Elsevier, vol. 281(3), pages 588-596.
  32. J. Burez & D. Van Den Poel, 2005. "CRM at a Pay-TV Company: Using Analytical Models to Reduce Customer Attrition by Targeted Marketing for Subscription Services," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/348, Ghent University, Faculty of Economics and Business Administration.
  33. Behrooz Hashemian & Emanuele Massaro & Iva Bojic & Juan Murillo Arias & Stanislav Sobolevsky & Carlo Ratti, 2017. "Socioeconomic characterization of regions through the lens of individual financial transactions," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-20, November.
  34. H-Y Tsao & P-C Lin & L Pitt & C Campbell, 2009. "The impact of loyalty and promotion effects on retention rate," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(5), pages 646-651, May.
  35. Vicente G. Cancho & Dipak K. Dey & Francisco Louzada, 2016. "Unified multivariate survival model with a surviving fraction: an application to a Brazilian customer churn data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(3), pages 572-584, March.
  36. Łapczyński Mariusz, 2014. "Hybrid C&RT-Logit Models In Churn Analysis," Folia Oeconomica Stetinensia, Sciendo, vol. 14(2), pages 37-52, December.
  37. A. Prinzie & D. Van Den Poel, 2007. "Random Forrests for Multiclass classification: Random Multinomial Logit," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 07/435, Ghent University, Faculty of Economics and Business Administration.
  38. Chen, Zhen-Yu & Fan, Zhi-Ping & Sun, Minghe, 2012. "A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data," European Journal of Operational Research, Elsevier, vol. 223(2), pages 461-472.
  39. Clemente-Císcar, M. & San Matías, S. & Giner-Bosch, V., 2014. "A methodology based on profitability criteria for defining the partial defection of customers in non-contractual settings," European Journal of Operational Research, Elsevier, vol. 239(1), pages 276-285.
  40. Glady, Nicolas & Baesens, Bart & Croux, Christophe, 2009. "Modeling churn using customer lifetime value," European Journal of Operational Research, Elsevier, vol. 197(1), pages 402-411, August.
  41. H-Y Tsao & L Pitt & C Campbell, 2010. "Analysing consumer segments to budget for loyalty and promotion programmes and maximize market share," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(10), pages 1523-1529, October.
  42. Bazargan, Amirhossein & Karray, Salma & Zolfaghari, Saeed, 2017. "Modeling reward expiry for loyalty programs in a competitive market," International Journal of Production Economics, Elsevier, vol. 193(C), pages 352-364.
  43. Gandomi, A. & Zolfaghari, S., 2013. "Profitability of loyalty reward programs: An analytical investigation," Omega, Elsevier, vol. 41(4), pages 797-807.
  44. Guven, Faruk, 2018. "Churn and loyalty behaviour of Turkish digital natives," 29th European Regional ITS Conference, Trento 2018 184943, International Telecommunications Society (ITS).
  45. B. Larivière & D. Van Den Poel, 2004. "Predicting Customer Retention and Profitability by Using Random Forests and Regression Forests Techniques," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/282, Ghent University, Faculty of Economics and Business Administration.
  46. Hsiu-Yuan Tsao & Lucy M. Matthews & Victoria L. Crittenden, 2012. "Balancing Market Share Growth And Customer Profitability: Budget Allocation For Customer Acquisition And Retention," Organizations and Markets in Emerging Economies, Faculty of Economics, Vilnius University, vol. 3(2).
  47. Bram Janssens & Matthias Bogaert & Astrid Bagué & Dirk Van den Poel, 2024. "B2Boost: instance-dependent profit-driven modelling of B2B churn," Annals of Operations Research, Springer, vol. 341(1), pages 267-293, October.
  48. Gázquez-Abad, Juan Carlos & Canniére, Marie Hélène De & Martínez-López, Francisco J., 2011. "Dynamics of Customer Response to Promotional and Relational Direct Mailings from an Apparel Retailer: The Moderating Role of Relationship Strength," Journal of Retailing, Elsevier, vol. 87(2), pages 166-181.
  49. Matthias Bogaert & Lex Delaere, 2023. "Ensemble Methods in Customer Churn Prediction: A Comparative Analysis of the State-of-the-Art," Mathematics, MDPI, vol. 11(5), pages 1-28, February.
  50. Marko Sarstedt & Sebastian Scharf & Alexander Thamm & Michael Wolff, 2010. "Die Prognose von Serviceintervallen mit der Hazard-Raten-Analyse – Ergebnisse einer empirischen Studie im Automobilmarkt," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 20(3), pages 269-283, April.
  51. Lessmann, Stefan & Voß, Stefan, 2009. "A reference model for customer-centric data mining with support vector machines," European Journal of Operational Research, Elsevier, vol. 199(2), pages 520-530, December.
  52. Bazargan, Amirhossein & Karray, Salma & Zolfaghari, Saeed, 2018. "‘Buy n times, get one free’ loyalty cards: Are they profitable for competing firms? A game theoretic analysis," European Journal of Operational Research, Elsevier, vol. 265(2), pages 621-630.
  53. M. Ballings & D. Van Den Poel, 2012. "The Relevant Length of Customer Event History for Churn Prediction: How long is long enough?," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/804, Ghent University, Faculty of Economics and Business Administration.
  54. Coussement, Kristof & De Bock, Koen W., 2013. "Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning," Journal of Business Research, Elsevier, vol. 66(9), pages 1629-1636.
  55. Duan Lianjie, 2023. "Export Cutoff Productivity, Uncertainty and Duration of Waiting for Exporting," Economics - The Open-Access, Open-Assessment Journal, De Gruyter, vol. 17(1), pages 1-19, January.
  56. Leonardo José Silveira & Plácido Rogério Pinheiro & Leopoldo Soares de Melo Junior, 2021. "A Novel Model Structured on Predictive Churn Methods in a Banking Organization," JRFM, MDPI, vol. 14(10), pages 1-24, October.
  57. Johannes Habel & Sascha Alavi & Nicolas Heinitz, 2023. "A theory of predictive sales analytics adoption," AMS Review, Springer;Academy of Marketing Science, vol. 13(1), pages 34-54, June.
  58. Tan, Pei Jie & Corsi, Armando & Cohen, Justin & Sharp, Anne & Lockshin, Larry & Caruso, William & Bogomolova, Svetlana, 2018. "Assessing the sales effectiveness of differently located endcaps in a supermarket," Journal of Retailing and Consumer Services, Elsevier, vol. 43(C), pages 200-208.
  59. Danijel Bratina & Armand Faganel, 2023. "Using Supervised Machine Learning Methods for RFM Segmentation: A Casino Direct Marketing Communication Case," Tržište/Market, Faculty of Economics and Business, University of Zagreb, vol. 35(1), pages 7-22.
  60. Shane S. Dikolli & William R. Kinney & Karen L. Sedatole, 2007. "Measuring Customer Relationship Value: The Role of Switching Cost," Contemporary Accounting Research, John Wiley & Sons, vol. 24(1), pages 93-132, March.
  61. Abbas Keramati & Hajar Ghaneei & Seyed Mohammad Mirmohammadi, 2016. "Developing a prediction model for customer churn from electronic banking services using data mining," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 2(1), pages 1-13, December.
  62. Miguel Angel de la Llave Montiel & Fernando López, 2020. "Spatial models for online retail churn: Evidence from an online grocery delivery service in Madrid," Papers in Regional Science, Wiley Blackwell, vol. 99(6), pages 1643-1665, December.
  63. Daniel Baier & Ines Daniel & Sarah Frost & Robert Naundorf, 2012. "Image data analysis and classification in marketing," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 6(4), pages 253-276, December.
  64. Philipp Brüggemann & Nina Lehmann-Zschunke, 2023. "How to reduce termination on freemium platforms—literature review and empirical analysis," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 707-721, December.
  65. Rocío G. Martínez & Ramon A. Carrasco & Cristina Sanchez-Figueroa & Diana Gavilan, 2021. "An RFM Model Customizable to Product Catalogues and Marketing Criteria Using Fuzzy Linguistic Models: Case Study of a Retail Business," Mathematics, MDPI, vol. 9(16), pages 1-31, August.
  66. Edin Osmanbegovic & Anel Dzinic & Mirza Suljic, 2022. "Prediction Of Telecom Services Consumers Churn By Using Machine Learning Algorithms," Economic Review: Journal of Economics and Business, University of Tuzla, Faculty of Economics, vol. 20(2), pages 53-64, November.
  67. W. Buckinx & E. Moons & D. Van Den Poel & G. Wets, 2003. "Customer-Adapted Coupon Targeting Using Feature Selection," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 03/201, Ghent University, Faculty of Economics and Business Administration.
  68. Mitrović, Sandra & Baesens, Bart & Lemahieu, Wilfried & De Weerdt, Jochen, 2018. "On the operational efficiency of different feature types for telco Churn prediction," European Journal of Operational Research, Elsevier, vol. 267(3), pages 1141-1155.
  69. Leslie Hannah & Makoto Kasuya, 2015. "Twentieth Century Enterprise Forms: Japan in Comparative Perspective," CIRJE F-Series CIRJE-F-966, CIRJE, Faculty of Economics, University of Tokyo.
  70. Arno de Caigny & Kristof Coussement & Koen de Bock, 2020. "Leveraging fine-grained transaction data for customer life event predictions," Post-Print hal-02507998, HAL.
  71. Brandner, Hubertus & Lessmann, Stefan & Voß, Stefan, 2013. "A memetic approach to construct transductive discrete support vector machines," European Journal of Operational Research, Elsevier, vol. 230(3), pages 581-595.
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