Abstract
In this paper, we examine the pattern of correlation among consumer price sensitivities for customer purchase incidence decisions across complementary product categories. We use a hierarchical Bayesian multivariate probit model to uncover this pattern. We estimated this model using purchase incidence data for six categories involving three pairs of complementary products.
Our results show a new and interesting pattern of correlation among price parameters of complementary products. For example, we find that the correlation of own-price sensitivities of complementary products is negative. These results are consistent across the three complementary pairs of products. We also investigate the reason for this counterintuitive result.
Finally, we present some managerial implications of our model. We show how our model can be used for cross-category targeting decisions by retailers. We find that compared to nontargeted discounting, the average profitability gain from customized discounting across the three category pairs is only 1.29% when complementarity is ignored, but this gain improves to 8.26% when full complementarity is taken into account. We also investigate whether ignoring the complex pattern of correlation has implications for managerial actions regarding targeting and optimal discounting. We find that retailers can make misleading inferences about the impact of targeted discounts when they ignore cross-category effects in modeling.