Mining Lifestyle Personas At Scale In E-Commerce

Groupon is a major e-commerce company. It is unique in the sense that it is not only a vendor of goods, but also of local deals (such as restaurants, spas, activities, etc.) that reflect various aspects of a user's interests. In this sense, Groupon has a complete view of its users' lifestyle preferences. This is different from e-commerce goods vendors, who, for instance, may not have direct insight into what restaurants are preferred by their users, or what nightlife they prefer. We may say that Groupon engages the entire "lifestyle persona" of its users.Motivated by this, we consider the large scale problem of mining such "lifestyle personas" from Groupon's activity data collected over 38 million e-commerce users. This includes users drawn from one of the world's largest mobile user bases. Our solution combines domain knowledge from e-commerce with data mining and graph theoretic methods.Since Groupon offers deals and goods across the 360 degree gamut of user preferences, do lifestyle personas in its data also span this gamut? What are some of the significant personas that emerge from this data mining? Are the differences between personas gross or subtle? These are some of the questions we answer conclusively in our study.Our mined personas are both descriptive and distinctive, and offer insights into customer behaviors and lifestyles. Our work is being used to redesign the user experience, as well as to power product recommendations at Groupon.