Is there a way to infer relocation, divorce or other life events from consumer behavior? This paper develops a predictive model based on the principle that consumer similarity derived from past transaction behavior reveals similarity in life events as well. In this project, we use a unique data set from a Fortune 500 mutual financial institution and combine various heterogeneous features in a predictive model to answer these and related questions. The main goal of our predictive model is to pro-actively detect life events from consumer demographics and online transaction behavior. We combine both the state-of-the-art socio-demographic predictive modeling with inferred consumer similarity based on transaction patterns-consumers are linked if they perform similar online transactions. The results demonstrate that the inferred consumer similarity is a key feature in predicting major life events.
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