Determine Optimal Store Locations using White Space Analysis

The Analytics Toolbox for BigQuery has been enhanced with a new set of retail-specific functions, including revenue prediction and white space analysis. White space analysis solves the question of where to open a new store by identifying locations with high expected revenue and meeting other business criteria. To complete end-to-end analysis, three steps are necessary: data preparation, model training, and finding whitespace areas. Data preparation involves enriching the area of interest with retailer and competitor data, as well as data from the Data Observatory subscription. Model training builds a revenue prediction model using Boosted Tree Regressor, while finding whitespace areas uses the FIND_WHITESPACE_AREAS procedure to identify locations within the area of interest where a new store would perform best. The procedures are now available to CARTO users and will soon power the white space analysis engine integrated in CARTO's Site Selection application.

No tracked trend matches for this post yet.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.