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Place Intelligence was engaged by the data and insights team for one of the worlds leading outerwear retailers to build the world’s most advanced mobility index of mountain users in the United States


Our team was approached by a global heritage clothing and sports equipment retailer to develop a national data model to understand key audience groups and their geo-community profiles across the United States of America.

Our client required an evidence-based framework to optimise the discovery of brick and mortar store locations and strengthen product-market fit for its premium sportswear lines.


A world leader in technical and high performance sailing, ski and hiking apparel, our client wanted to understand the activity profiles of its targeted skiing, sailing and hiking customers.

Place Intelligence created a national data model of every ski mountain, marina and hiking trail across the United States to identify the rhythms of audience behaviour and identify potential for new flagship stores.

In addition, we built a series of data models of every ski resort in North America revealing deep insights into how long people spend outdoors and and support an understanding of what type of equipment they might require for their activities.

This work built an index of more than 500 ski mountains, 10,000 hiking trails, and 1200 marinas across the USA. This index was used to understand customer behaviours for discrete activity-based audience types.

Our team built a national real estate intelligence platform, with variable modelling for the optimised discovery of new store locations based on targeted ski, sailing and hiking audience cohorts.


  • Privacy prioritised audience identification: discover place based audience cohorts at a national scale, based on activity preferences
  • Audience analytics: audience analysis cross-correlated with customer location data derived from online sales and shipping
  • Trade potential: mapped variances between competitor stores, existing customer locations and potential customer home zip codes to inform the location a new flagship stores


  • Interactive data models in 30 cities across 5 million buildings
  • Dynamic national real estate intelligence platform
  • Comprehensive data analytics on audience, linked to home location and income
  • The retailer was able to enhance and personalise their customer experience
  • Data cross-correlated with online shopping patterns across the USA

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