In the presence of successive product generations, most consumers are repeat buyers, who may decide to purchase a future product generation before its release. As a result, after the new product generation enters the market, its sales often exhibit a declining pattern, thus rendering traditional bell-shaped product life-cycle models unsuitable for characterizing consumers’ time to product upgrades. In this study, we propose an Exponential-Decay Proportional Hazard Model (Expo-Decay model) to predict consumers’ time to product upgrade. The Expo-Decay model is parsimonious and easy to interpret and performs better than or as well as existing models in prediction accuracy. We apply the Expo-Decay model as well as three extensions to study consumers’ upgrade behaviors for a sports video game series. Empirical results reveal that consumers’ previous adoption and usage patterns can help predict their timing to product upgrades. In particular, we find that (i) consumers who have adopted the immediate past product generation are more likely to upgrade; (ii) players who play previous generations more often tend to upgrade earlier; (iii) consumers who specialize in a small subset of game modes demonstrate a lower probability to upgrade. When comparing the Expo-Decay model and its extensions, we find that more complex model extensions do not lead to better prediction performance than the baseline Expo-Decay model, while a time-variant extension that updates the values of covariates over time outperforms the baseline Expo-Decay model with static data.
|Publication status||Published - Nov 2020|
China Europe International Business School (CEIBS)
- Predictive analytics
- product upgrade
- survival analysis
- proportional hazard model