Synopsis

This submission presents an application of data fusion and Artificial Intelligence (AI) to a specific operational challenge. Using the latest trends in machine learning, GemSeek (part of the Future Thinking Group) developed a functional technique to model different survey research outcomes and apply these outcomes to internal customer data sets including CRM, transactional data, and others. Our model has been fully embedded into operational practices and has helped successfully predict, target and reduce customer churn. This approach successfully combines multiple data sources and provides a full 360 view of customer behaviour. It also bridges the gap between utilisation of seemingly unrelated data i.e. survey research based on small sample and large internal databases.

Bridging the gap between research analytics and data stored in customer databases has been a persistent headache in the industry. The ability to apply findings from survey research to the entire customer base gives companies powerful tools for better customer targeting, increased sales, and improved retention, while optimising research efforts to understand individual customer perceptions. Thus, companies can maximise data ROI and achieve operational synergies throughout teams and departments.