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.

The increasing availability of GPS-embedded devices has introduced a new dimension in digital market especially location-based services. In practice, the location data is used to understand and predict consumer mobility behavior and trend for various purposes. In this paper, we propose two methodologies to first identify suspicious location from consumer location data and to infer location at both individual device and device to device level based on systematic solution. Using stay-point clustering and suspicious patterns we identified from extensive analysis, 20-30% of records with location were observed to be suspicious. After removing inaccurate location data, we have employed scalable heuristic approach to backfill records with location even for devices that originally had no available location. Our model showed the accuracy within 50 meters at 95thpercentile across different countries, including Japan, Indonesia, India, and the United States with 10-15% increase in the number of records with location and 5-10% increase in new number of devices with location.

Identifying valuable customers as well as retaining them has become key component for any business to succeed in this competitive market. Businesses have also realized that relying solely on its own transactional data, might not be sufficient any longer, to meet the required objectives. There is a need to partner and leverage the power of big data available from the external data sources to add more value. In this paper, we are detailing the methodology of mashing up Mobilewalla's high scale mobile consumer data with one of the world's largest online food delivery company in order to revamp their retention and acquisition strategy. In this deployment, Mobilewalla has helped the client, a) to identify the new potential high impact customers from Mobilewalla ecosystem, and b) to predict the unfavorable transitions such as high impact customers getting churned or falling into low impact category. We observed that correctly identified high impact customers by Mobilewalla' customer acquisition model had 21.41% higher average revenue per user (ARPU) than the expected ARPU from high impact customers. Further, the customer retention model can help the client to spend 80% of their retention budget dollars optimally.

Democratization of information access brought about by digital distribution has resulted in two contradictory phenomena: the ability to personalize consumer experience, and greater anonymity of users. These intensify when information is consumed on mobile devices, particularly because techniques to profile users on desktop web do not work on mobile smart-devices. Yet, the already large and still fast-growing field of mobile advertising require activation of audience segments against mobile advertising campaigns. Of particular importance are age and gender segments of mobile users, as these user characteristics are required for targeting a large number of ad campaigns. To date, there are no practical methodologies available in the literature that allow for accurate identification of age and gender of mobile users, at scale.In this paper, we propose a scalable machine learning approach to infer the age and gender of mobile users. We have successfully tested and implemented the gender prediction model for 8 countries, additionally the groundwork has been laid out for implementation of age inference. The output is integrated with our commercial products, furthermore, it is used as a part of custom client deliveries. We inferred gender for more than 500 million devices and there is a notable increase in number of devices with gender label (post prediction) within our current dataset. We have also inferred age for 17 million devices in Australia.