Goal: identify segments of customers by geography using unsupervised learning in order to personalize search ranking
- Feature collection from clickstream activity; aggregate to CBSA geo level
- Non-negative matrix factorization to reduce features into themes
- t-SNE to further reduce to 2D
- k-means clustering to identify geo segments, testing multiple values for k
- validate robustness of results using clustering metrics
- Refresh (if needed) analytical datasets from Snowflake (5 tables) - takes ~3min
- Connect to an RStudio server environment and pull in the above tables – takes ~1min
- Perform ETL inside R-Server, or in desktop
- Once final ADS is complete, run analysis
- Manual work was done to trial and error which parameters are ideal; Code could be re-run on those decided parameters without manual intervention; if we wanted to reassess the parameters or add new explanatory variables, that would require some manual work
- Export Cluster mapping csv
- Upload Cluster mapping table to Snowflake for ingestion into Search ranking program
