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To model, or not to model: Forecasting for customer prioritization

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  • Huang, Chun-Yao

Abstract

Simple heuristics are usually deemed to be inferior to more complicated models. Although recent studies have demonstrated the usefulness of some forecasting heuristics, the questions of why and when a heuristic would work remain unaddressed. This study aims to answer such “why” and “when” questions by looking empirically at the specific context of forecasting for customer prioritization. Based on widely-applied probabilistic models, a series of simulations reveal that: (1) we are not usually able to identify the future top-X% of customers in a customer base accurately, even if we know the exact data generation process; (2) a simple heuristic can perform as well as a probabilistic model even if the model maps the data generation process exactly; (3) the relative performances of the model and the heuristics can be explained by several easily-obtainable descriptive statistics. The heuristic works because the minimal information it relies upon is relatively robust and relevant in a random world.

Suggested Citation

  • Huang, Chun-Yao, 2012. "To model, or not to model: Forecasting for customer prioritization," International Journal of Forecasting, Elsevier, vol. 28(2), pages 497-506.
    • Handle: RePEc:eee:intfor:v:28:y:2012:i:2:p:497-506
    DOI: 10.1016/j.ijforecast.2011.04.004
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    References listed on IDEAS

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    Cited by:

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    2. 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.
    3. Arno de Caigny & Kristof Coussement & Koen W. de Bock & Stefan Lessmann, 2019. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," Post-Print hal-02275958, HAL.
    4. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W. & Lessmann, Stefan, 2020. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1563-1578.

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