Customer Wallet Share Estimation for Manufacturers Based on Transaction Data
The value of customers for any business cannot be over-emphasised, and it is crucial for companies to develop a good understanding of their customer base. One of the most important pieces of information is to estimate the share of wallet for each individual customer. In the literature a related concept is often referred to as customer equity that provides aggregated measures such as the business market share. The current trend in personalising marketing campaigns have led to more granular estimation of wallet share, than the entire customer base or aggregated segments of customers. The current trend in personalising marketing and business strategies have lead to more granular estimation of wallet shares than the entire customer base or aggregated segments of customers. Existing research in this area requires access to additional information about customers, often collected via various surveys. However, in many real-world scenarios, there are circumstances where survey data are unavailable or unreliable. In this paper, we present a new customer wallet share estimation approach. In the proposed approach, a predictive model based on decision trees facilitates an accurate estimation of wallet shares for customers relying only on transaction data. We have evaluated our approach using real-world datasets from two businesses from different industries.
KeywordsWallet share estimation Customer equity Random Forest Real-world case study
- 9.Liaw, A., Wiener, M., et al.: Classification and regression by randomforest. R News 2(3), 18–22 (2002)Google Scholar
- 11.Merugu, S., Rosset, S., Perlich, C.: A new multi-view regression approach with an application to customer wallet estimation. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 656–661. ACM (2006)Google Scholar
- 12.Rosset, S., Perlich, C., Zadrozny, B., Merugu, S., Weiss, S., Lawrence, R.: Wallet estimation models. In: International Workshop on Customer Relationship Management: Data Mining Meets Marketing (2005)Google Scholar
- 13.Subramaniam, L.V., Faruquie, T.A., Ikbal, S., Godbole, S., Mohania, M.K.: Business intelligence from voice of customer. In: 2009 IEEE 25th International Conference on Data Engineering, pp. 1391–1402. IEEE (2009)Google Scholar