Using Context to Improve Predictive Modeling of Customers in Personalization Applications

TLDR

Contextual information is widely regarded as important for predicting customer behavior, yet no systematic study has quantified its impact in personalization applications. This study investigates the importance of contextual information for predicting customer behavior and explores how to incorporate it into customer models. The authors conduct an empirical study across a broad range of experimental conditions to evaluate contextual modeling. The results demonstrate that context improves individual customer behavior modeling, can be inferred from existing data with reasonable accuracy, and that cleverly modeling context yields significant performance gains, offering data miners and marketers opportunities to enhance predictive accuracy while reducing contextual data collection costs.

Abstract

The idea that context is important when predicting customer behavior has been maintained by scholars in marketing and data mining. However, no systematic study measuring how much the contextual information really matters in building customer models in personalization applications has been done before. In this paper, we study how important the contextual information is when predicting customer behavior and how to use it when building customer models. It is done by conducting an empirical study across a wide range of experimental conditions. The experimental results show that context does matter when modeling the behavior of individual customers and that it is possible to infer the context from the existing data with reasonable accuracy in certain cases. It is also shown that significant performance improvements can be achieved if the context is "cleverly" modeled, as described in this paper. These findings have significant implications for data miners and marketers. They show that contextual information does matter in personalization and companies have different opportunities to both make context valuable for improving predictive performance of customers' behavior and decreasing the costs of gathering contextual information.