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Contribution Details
| Type | Conference Presentation |
| Scope | Discipline-based scholarship |
| Title | Instant Customer Base Analysis: Re-assessing the performance of managerial heuristics |
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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 2017 |
| Event Type | conference |
| Event Location | Los Angeles, CA, USA |
| Event Start Date | June 7 - 2017 |
| Event End Date | June 10 - 2017 |
| Abstract Text | Valuing customers is essential to any firm and enables marketers to identify key customers. Customer lifetime value (CLV) is a 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. To model CLVs in continuous non-contractual business settings, probabilistic customer attrition models such as the Pareto/NBD model are the preferred choice in literature and practice. Their ability to simultaneously forecast both, customer’s actual lifetime and future transactions is unique. However, empirical evidence suggests that standard probabilistic customer attrition models do not outperform basic management heuristics. A possible explanation is that standard probabilistic customer attrition models do not consider important contextual factors, such as direct marketing or regularity purchase patterns. Recently an implementation that allows the inclusion of such time-varying contextual factors for the continuous non-contractual setting has been proposed. In this study, we compare the predictive accuracy of this model extension, the standard Pareto/NBD model, and managerial heuristics based on three key metrics: (1) distinction of active and inactive customers, (2) forecasts of future purchase level and (3) aggregated purchase volume of the entire customer base. The comparison is carried out for multiple datasets and multiple prediction horizons. 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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