At one point in its long history of providing communities across the country with sports and outdoor equipment, Academy Sports and Outdoors identified a few underperforming stores in its network. Naturally, the retailer began investigating the problem. Critical tools in this research were its three key datasets — STI: PopStats™, STI: LandScape™, and STI: Spending Patterns™.

The research revealed that the problem was not the locations, but the stores’ merchandising strategies. The underperforming stores were not stocked in a locally relevant way to fit their neighborhood demographics. The diagnosis inspired the company to rethink its merchandising strategies and make locally focused changes. As soon as they made changes, sales increased.

This scenario is just one example how the power of geodemographic data can solve problems and help companies make business decisions beyond strictly the real estate department. In fact, Academy has been extending its market research services outside of its real estate department for several years — bringing the power of data to several other departments in the company.

STI Data Informs Business Decision

Gaining critical insight for site selection is the reason why Academy switched to STI data after using another company’s data for several years. “We’d been hearing about PopStats in our industry. When our contract was coming up for renewal with another vendor, we took the opportunity to evaluate PopStats and found that it was superior,” said Rich Babson, Real Estate Research at Academy.

“PopStats was more accurate and more current, which is especially important the further out we get from the decennial U.S. Census. With our previous data product, the further we got from the Census, the bigger the population estimate variances were from reality. But with PopStats we’ve found that the data stays consistent and the error rate stays small. This is important to us because the more accurate the data is, the better informed our decisions are.”

Along with PopStats, Academy also uses LandScape neighborhood segmentation data and Spending Patterns consumer spending data. LandScape has helped Academy identify its ideal neighborhood segments. Spending Patterns informs product demand analysis.

Neighborhood-Specific Merchandising

To better understand its consumers, Academy performed an in-depth study to determine its ideal neighborhood lifestyle segments using LandScape. In particular, it wanted to identify consumers who fit its ideal customer personas, including outdoorsmen, military personnel, soccer moms, and fitness buffs.

Using LandScape’s 72 neighborhood segments, the company looked at neighborhoods where its best- and lowest-performing stores were located. From there, it identified patterns that shaped its understanding of its ideal neighborhood segments.

“One of the main things I like about LandScape is that the segments are the same across the nation versus our former vendor’s product, which was heavily influenced by regional consumer characteristics,” explained Rich.

“With the old system, the consumers in the segments in which we do well — in Texas and Oklahoma, for example — were not home to the same consumers who live in Florida and North Carolina. With LandScape, I get consistent neighborhood segments across the country.”

Academy now uses its LandScape-based neighborhood segmentation insight in all site selection research. What’s more, the merchandising and marketing departments rely on neighborhood segmentation research, as well.

“For sporting goods, it’s critical to know who our customers are beyond the demographic data,” said Rich.

“Trade areas with the same demographic characteristics can be inhabited by people with very different lifestyles. Knowing the exact lifestyles of our ideal customers gives us critical consumer insight. It’s much more profitable for our company to identify areas with large pockets of our ideal customers and, similarly, to avoid areas with the wrong types of consumer lifestyles.”

Along with PopStats and LandScape, the SpendingPatterns data plays an important role in helping Academy determine each store’s ideal merchandising mix. Before opening a new store, the research team creates a demand analysis to assess what products will fit best with the lifestyles of the consumers living in that area.

In this way, the stores minimize some products, such as athletic apparel, and maximize other categories, such as sports team apparel, depending on the SpendingPatterns data. Rich particularly likes that the data is consistent across the country, so they can depend on the insight no matter where in the U.S. they are researching.

Research-Driven Business Decisions

For real estate, the research department uses STI data in a variety of ways including:

• Creates executive committee presentations for every new store location
• Conducts property analysis for new locations, including lifestyle segments
• Conducts existing store analysis to improve performance issues
• Employs regression analysis to understand which variables impact store performance
• Creates forecasts using over 300 store-level attributes

Examples of research projects for other departments include these requests:

• Advertising Department – requested maps of zip codes in trade areas for advertising distribution and distance calculators to brand-name competitors
• Promotions Department of a Regional Store – requested a map of the shooting schools within a specific proximity to its stores, so the stores could reach out and set up partnerships with them
• Human Resource Department – requested maps of healthcare facilities located near contracted e-commerce employees to provide them with medical services information to meet labor compliance regulations
• Merchandise Department – requested analog models to support merchandising decisions for each department within each store

In fact, conducting research for the merchandising department is a major focus of Academy’s trade area research, because there are such big regional differences in its customer bases. For example, some areas are home to hunters or fishermen, while others are team-sport-oriented.

“This analysis has proven to deliver a significant impact on our merchandising,” notes Rich. “Every store in which it’s been executed has experienced significant performance improvements. That’s the decision-making power we’ve come to expect from our powerful suite of STI datasets.”

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