Identify preferred store locations to enable personalized marketing
The Big Picture
A leading payment provider wanted to understand the relationship between a shopper’s home location and preferred store locations in order to improve marketing efforts and make store-related business decisions.
The solution deployed iterative a machine-learning algorithm on geo location data and merchant data to identify patterns in shopping behavior. The team identified source data from transactional, merchants, and geo sources, creating extensive rules to propel the initial cleansing and mining.
The data was matched with external data sources to identify merchant locations using a scalable matching solution that leveraged probabilistic search techniques.
A two-way learning algorithm was applied to estimate merchant and cardholder location. This merchant location data was used to identify transaction level data for customers. Insights from the analysis were used to improve personalized marketing tactics.
The program achieved an accuracy level of 97%, 72%, and 58% for Mine, Match and Learn stages respectively while a fill rate was achieved of 13%, 55%, and 99% respectively. The derived insights will help the client in providing card linked offers and obtain shopping insights for retailers. Additionally, the initiative identified digital targeting opportunities based on customer home and work locations.