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Contribution Details
| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | How much is a customer worth? Exploring techniques to improve the customer lifetime value prediction in retailing industry |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Number of Pages | 43 |
| Date | 2017 |
| Abstract Text | In line with customer centricity, analyzing customer lifetime value provides competitive advantage. Academics are exerted to develop and implement stochastic models for customer base analysis. A common approach is the Pareto/NBD model that predicts customer churn and transaction behavior simultaneously. In practice however, managerial decisions mostly follow simple heuristics. To implement stochastic models, their predominance in accurate predictions must be proven. This study compares the performance of the Pareto/NBD model to simple heuristics in determining active customers, predicting future transaction and spending levels, and identifying future top customers. Two real world data sets from retailers are analyzed with multiple cohorts over various prediction periods. The heuristics determine active customers more accurately than the Pareto/NBD model, while the latter excels in predicting future behavior. Furthermore, the length of the prediction period will affect the accuracy of the prediction. Hence, an explicit recommendation of any approach to managerial practice cannot be derived. |
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