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Case study: retail store catchment areas

Building on the retail store location analysis this case study focuses on catchment areas of stores.

 Let’s take a look at how to establish a store location that has access to more customers than its direct competitor.

Building the Analytics Pipeline


  • A high street store is looking to open another store.
  • The current store is currently competing for customers with a rival store. Their catchment areas overlap.
  • Customers will only visit the store which is closest to their postcode by travel time.
  • A new store would ideally gain new customers from postcodes that haven’t had access to any store yet as well es poach existing customers from a competitor.

Alteryx – create isochrones

  • Use the Geocoder macro to generate longitude and latitude from store postcodes.
  • Specify travel times and modes of transport with the Isochrones macro.

Customer profiles

  • Input surrounding postcodes and use the spatial match tool to combine isochrones and postcode data.
  • We are only working with postcodes that fall into a catchment area.

Finally, we’re using the Travel Time Matrix macro to determine the travel time between each store and postcode by transport method.


The Alteryx output contains postcodes that fall into isochrones of several stores. We can use Tableau to dynamically allocate a postcode to the nearest store.

Our current store is based in Stratford while the competitor has a location in the City of London. By adding our potential new store locations (Tottenham, NW, N) we can determine how many customers we can reach with each new location.

This analysis uses TravelTime, an API and set of macros that let users search and analyse location data using minutes instead of miles. Find out more here.

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