> For the complete documentation index, see [llms.txt](https://academy.carto.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://academy.carto.com/creating-workflows/workflow-templates/retail-and-cpg.md).
# Retail and CPG
### Estimate population around top performant retail stores
This example demonstrates how to use workflow to filter out the top retail stores that belong to a specific category and computes the population living around them.
[**Download example**](https://storage.googleapis.com/carto-workflows-examples/files/estimate_population_around_retail_stores.sql)
### Commercial Hotspot Analysis. Identifying Optimal Locations for a New Pizza Place
Identifying an optimal location for a new store is not always an easy task, and we often do not have enough data at our disposal to build a solid model to predict potential revenues across an entire territory. In these cases, managers rely on different business criteria in order to make a sound decision for their expansion strategy. For example, they rely on defining their target market and segmenting population groups accordingly in order to locate the store closer to where the target market lives (e.g. areas with a great presence of youngsters).
In this example, we are going to use the [Hotspot Analysis](https://docs.carto.com/carto-user-manual/workflows/components/statistics#hotspot-analysis) component to explore good locations to open a new Pizza Hut restaurant in Honolulu, Hawaii. We will use H3 as our geographic support and population and distance to existing Pizza Hut stores as our criteria to identify hotspots. For a detailed description of this use case read [this guide](/advanced-spatial-analytics/spatial-analytics-for-bigquery/step-by-step-tutorials/opening-a-new-pizza-hut-location-in-honolulu.md).
[**Download example**](https://storage.googleapis.com/carto-workflows-examples/files/commercial_hotspots.sql)
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