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| Type | Conference Presentation |
| Scope | Discipline-based scholarship |
| Title | The role of time-varying contextual factors in latent customer attrition models |
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| Authors |
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| Presentation Type | keynote |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Event Title | INFORMS Marketing Science Conference |
| Event Type | conference |
| Event Location | Philadelphia, Pennsylvania, USA |
| Event Start Date | June 14 - 2018 |
| Event End Date | June 16 - 2018 |
| Abstract Text | Valuing customers is essential to any firm and enables marketers to identify key customers. Customer lifetime value (CLV) is the central metric for valuing customers. It describes the long-term economic value of customers and gives managers an idea of how customers will evolve over time. With the Pareto/NBD model, modeling customer lifetime value for non-contractual businesses has become a straight-forward task, however this simplicity comes at a price. Individual-level predictions of customer lifetime value often lack precision. A possible explanation is that standard probabilistic customer attrition models do not consider important contextual factors, such as direct marketing or regularity purchase patterns. However, there is no generalization of the Pareto/NBD model that allows time-varying contextual factors to be considered. This study proposes a closed-form maximum likelihood extension to the Pareto/NBD model that allows both time-invariant and time-varying contextual factors to be modelled in continuous non-contractual settings. These contextual factors can influence either the purchase or the attrition process, or both. A benchmark using multiple retailing datasets shows a significant improvement in forecast accuracy for future customer activity when explicitly modeling time-varying contextual factors. Our findings have strong implications for both, marketing practice and research. Besides giving detailed recommendations on when to use which modeling approach, we also provide practical advices for applying probabilistic customer attrition models. |
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