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How a Global Footwear Retailer Evaluated Hundreds of Locations Using Near’s Data

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Situation:

A global footwear and apparel retailer with thousands of retail stores and franchises globally across North America, Europe, Asia, Australia, and New Zealand was interested in diversifying its current store locations. The company has a portfolio of brands targeted to different personas, including serious athletes, casual athletes, sneaker enthusiasts, kids, and more.

Challenge:

In the past, the retailer had focused on locations inside shopping malls, with a strong majority of their locations in malls. As malls have faced challenges and lost anchor tenants, the retailer looked to diversify its locations to also include a mix of freestanding stores, strip malls, and downtown high street type locations. The company has a goal to have half of their North America stores in off-mall locations in the next few years.

With the large investment required to open new and optimize existing locations, as well as the added challenge and nuance of evaluating off-mall locations, the retailer wanted to ensure that their market planning maximized ROI. They launched a market planning team to apply data science to critical real estate decisions around site selection and disposition, expansion, relocation and even store formatting.

Like many retailers, in the past the footwear retailer had relied on traditional methods like site visits that evaluated qualitative aspects around the feel of a site and the people walking around. While experience and market knowledge continued to be important in understanding locations, the team needed better data to improve the accuracy and scalability of their models. The market planning team looked to leverage multiple sources of data, including location intelligence, to make their key decisions.

Multi Site Comparison
Site Selection
& Disposition
Data Privacy
Expansion
& Relocation
Consumer Behavior Data
Store Formatting
& Merchandising

Solution:

The retailer turned to Near to get access to their consumer behavior data driven by location intelligence to better understand customer profiles, trade areas, pathing, and more of prospective locations.

More than 50 team members at the retailer regularly utilize the data, providing value across multiple teams including real estate, customer experience, product allocation, and finance. 

The data has been a critical aspect of several strategy initiatives:

Driving the Off-Mall Strategy

As the retailer looks to have half of their locations in off-mall locations, the data provides visibility into less traditional locations including standalone stores, downtown streets, and strip malls.

Growing Number of Concept Stores

The retailer has concept stores – such as community stores and power stores. The company is looking to grow to the number of concept store locations by 4x globally in the next few years.

Balancing Supply and Demand

With key insights around consumer behavior patterns and demographics, the retailer is better positioned to source high-quality products that will meet the specific demand by location.

Optimize Market Plan by Brand and Market

Overall, the retailer is looking to increase sites and square footage as an organization, but different markets and brands have different needs. In some parts of the world and for some brands, the retailer is growing quickly, while in other markets they may be overpenetrated.

To show how the retailer’s process works, we look at how they might evaluate sites for a potential location in a suburban strip mall:

  1. Create polygons around any center they want to look at, even a subset of a strip mall.
  2. Pull key insights for the study area:
    • Who is shopping there today?
    • Where else do they shop?
    • Where do they live?
    • What are their demographics
  3. Within a matter of hours, the team is able to understand the consumer for a prospective location that allows them to determine whether to continue validating that center or rule it out.
  4. The team can easily create dashboards and visualizations that help stakeholders on other teams make more informed decisions as well.

Results:

The partnership with Near helped the footwear retailer confidently optimize its market planning, with results including:

Evaluating hundreds of sites over the course of the first 18 months, with 100+ of those sites being chosen for a new location

Leveraging the data to drive key strategies including identifying off-mall locations and sites for new concept stores

Empowering 50 internal stakeholders with data for planning and decision-making, powering dashboards and visualizations.

Organizations using the data include real estate & leasing, consumer experience, product allocation, finance and more.

The retailer looks forward to continuing to leverage the data to navigate key strategic moves around their off-mall strategy, concept stores, and overall sustainable business model.

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