Using context to improve the effectiveness of segmentation and targeting in e-commerce

In e-commerce, where competition is tough and customers’ preferences can change quickly, it is crucial for companies to segment customers and target marketing actions effectively. The process of segmentation and targeting is effective if the customers grouped into the same segment show the same behavior and reaction to marketing campaigns. However, the link between segmentation and targeting is often missing. Some research contributions have recently addressed this issue, by proposing approaches to build customer behavior models in each segment. However customers’ behavior can change with the context, such as in many e-commerce business applications. In these cases, building contextual models of behavior would provide better predictive performance and, in turn, better targeting. However, the problem of including context in a segmentation model and building predictive behavior model of each segment consistently is still an open issue. This research aims at providing an answer to the following research issue: how to include context in a segmentation model in order to build an effective predictive model of customer behavior of each segment. To this aim we identified three different approaches and compared them by a set of experiments across several settings. The first result is that one of the three approaches dominates the others in certain conditions in our experiments. Another important result is that the most accurate approach is not always the most efficient from a managerial perspective.

► We present a framework to incorporate context into segmentation.
► We identify three approaches: pre-filtering, post-filtering and profiling.
► Pre-filtering has the best performance and outperforms the un-contextual approach.
► This effect increases with market granularity and contextual degree of knowledge.
► The study raises a trade-off between performance and management cost.