Scalable Household Identification using Mobile Engagement Data - A Weighted Two-Mode Network Approach.

It has become increasingly important for marketers to understand the customers at the household level for more meaningful and contextual targeting. In this paper, we propose a scalable graph-based approach for identifying households among mobile users, by constructing a two-mode network with mobile apps’ engagement data. The proposed solution was tested rigorously for multiple countries by benchmarking against census bureau and/or third-party data. The results in form of mean household size were found remarkably close to the public data, differing only by 3% in some countries. Additionally, we demonstrated two real-industry use-cases - first where the features were derived from the household data to predict the creditworthiness of new customers (credit risk modelling), and second where the household data was consumed directly by a telecom company to acquire and/or retain customers.