Real world consumer segmentation
BI, Reporting & Business Use Cases
- Shopkick has over 30M downloads, but the userbase is very heterogeneous. Anecdotal evidence indicated a wide variety of users for whom the app holds long-term appeal.
- Marketing and other teams challenged Analytics to get beyond basic summary statistics and develop a holistic segmentation of the userbase.
- Shopkick's data science team used SQL and python to gather data, clean data, and then perform a data-driven segmentation using a k-means algorithm.
- Interpreting the results is more work -- and more fun -- than running the algo itself. We'll discuss how we transform from ""segment 1"", ""segment 2"", etc. to something that non-analytics users (Marketing, Operations, etc.) could actually benefit from.
- So what? How did team across Shopkick change their approach given what Analytics had discovered.