Geek looking at data

Key Considerations for Retailers Developing a New Store Prototype

Published Aug 5, 2020, 01:49 PM by Joe Whitley
How we helped a retailer with very limited data successfully expand their new store prototype in new and existing markets.

Further impacted by the effects of the COVID-19 pandemic, the retail industry is continuing to experience several trends influenced by a changing consumer demographic with different needs, purchase preferences, alternative distribution channels and a changing competitive environment. In many cases, to remain competitive, retailers are facing the need to either change their current concept or develop an entirely new store concept. Store planning decisions might include a change in store size, a shift in merchandise assortment or price point, an alternative channel of distribution or a shift in site location (e.g. from a mall to a non-mall location).

Implementing a new store concept without a clear understanding of the bottom-line effects of these and other changes can be risky and expensive with no guarantee of success. Instead, big-box retailers should start using available data sources and advanced research and analytics to develop a new store prototype to inform store planning strategy and answer some of the many questions on their minds.

The case study below demonstrates how a leading U.S. specialty retailer in building products and supplies developed a predictive model for a new store prototype with minimal sample sales data. We also offer key considerations for retailers interested in developing a new store prototype and the types of questions they can seek to answer by participating in the process.

Big box retailer case study: Developing a new store prototype and predictive sales model

A leading US specialty retailer in building products and supplies sought an aggressive expansion plan for a new store prototype. While their current base of 83 stores ranges from 45,000 s.f. to 150,000 s.f. in floor space, their new prototype would include 80,000 s.f. in floor space. Their business challenge was to develop a predictive model for a new store prototype with a minimal sample of 15 stores with sales and customer transaction history.  

Environics Analytics' location research combined available databases on census demographics, household segmentation, businesses, retail sales, consumer expenditures, mobile movement data and applied advanced location and consumer research to develop a predictive sales model for their new concept. The location and consumer research required a multi-phased, building block approach to identify critical insights for site model development.

Our research included EA's mobile movement and PRIZM Premier segmentation data as input for the site model. We were able to determine how far visitors travel to a store location and competitor store location to understand the relationship between store size and trade area size in terms of time traveled. We could then identify core segments including their usage behavior and market potential by segment to develop customer profiles.

The completed model enables this client to:

  • Estimate new store sales within 12% of actual in the first year.
  • Measure a primary trade area including the effects of cannibalization and competition on prospective sites as an input for the sales estimate.
  • Apply the site model in an Environics Analytics consumer insights and market intelligence tool to generate a custom Site Detail Report and thematic map for each prospective site (See illustration below). The Site Detail Report helps to explain those factors that were identified in the forecasting model that contributed to a sales forecast in existing and new markets and can be easily accessed by both corporate and field real estate users. This report also includes non-modeled variables to help support the site planning decision.

Sample Site Detail Report


Key considerations for retailers developing a new store prototype

A carefully thought out research process should be designed to seek answers before making subsequent recommendations.

If there is an existing concept or prototype that is being evaluated for future expansion, research may include a combination of historical sales, store size, customer transaction history and external data sources to determine key performance drivers. In those situations where a prototype does not exist, research may begin with an analysis of primary competitors using available data sources and GIS mobile analytics. These insights are then applied to determine white space opportunities for the new concept. 

Currently, available data sources and research analytics make it possible to answer these and other questions on the minds of retailers and store planners today:

  • Who is the consumer audience for my products and services and what is the current and projected size of the consumer audience by market?


  • What are the consumer channel preferences for bricks and mortar, online, delivery, etc.?


  • What is the current and projected consumer demand for products and services by market?


  • What is the relationship between store size and trade area draw for similar products and services?


  • Who are my competitors and what is their customer demographic based on? Where do these customers live and work relative to my competitors location?


  • How does knowledge of the competition and consumer demand for products and services translate into white space opportunity for my products and services?


  • How do I apply available data sources and research insights to identify and prioritize those markets that offer the greatest opportunity for success, including the number of stores each market can support?

Data and analytics can help retailers find their footing amidst changing consumer behavior. Whether deciding to change an existing store concept or starting anew, EA can help store planners use data and analytics to understand the impact of these changes on the bottom-line. 

For additional information about available tools and methods that can be used to select sites and plan your markets,  get in touch. We can help. 


You may also be interested in:

How Data Can Breath Live into Brick and Mortar Retail

How US Credit Unions and Lenders Can Improve Branch Distribution Planning

Learn more about our data and services for the retail sector


Back to top