Analyzing Stores: Tips & Best Practices for Retailers

We’ve been talking a lot lately about what your retail chain can and should do when you’re not opening as many new locations…but don’t want to stop doing research.

Analyzing your locations’ historic performance is one of the best ways to plan for the future. When you identify the characteristics that distinguish your best locations from your low performers, you can look for patterns and use your best judgment to apply those learnings to future site selection.

SiteSeer Tools that Enable Store Analysis

To thoroughly analyze stores or sites, you need tools like those in the SiteSeer platform. Although it’s possible to do some limited analysis in a tool like Excel, SiteSeer offers a big advantage: users can subscribe to third-party data sources to power the platform. That means a retailer can do more than analyze their own sales data to draw conclusions. They can incorporate population, socioeconomic, industry-specific and even competitor data into that analysis.

When choosing new locations, retail chains of course want to narrow the options to only sites with the highest likelihood of success. In SiteSeer, they do that by using Model Builder to create a Site Scorecard that uses key performance indicators of the most successful locations. Those KPIs might include:

• A precise minimum population within a certain distance of a site
• A specific distance to competitors
• Certain trade area demographics

When a user screens possible sites, SiteSeer compares them to the Site Scorecard they’ve created for their retail chain. Then, it assigns sites a score based on the retailer’s pre-defined KPIs/success criteria.

Determining Success Factors

Although a retailer might think they know what traits their high-performing locations have in common, Analysis can tell them for sure what makes a location successful. That’s what SiteSeer’s Analysis tool helps retailers determine.

At a high level, it works like this:

1. The SiteSeer user chooses what performance measure to analyze (sales, most likely).
2. The Analysis tool collects third-party data on all variables that the user subscribes to (e.g., income, lifestyle/socioeconomic, population, foot traffic/movement data, etc.).
3. Analysis identifies variables with the highest correlation with the performance measure. This gives retailers the big picture about the relationship between sales and a variety of factors. A positive correlation between sales and median income within a two-mile radius can uncover meaningful patterns about what seems to drive their business.

Data Opens the Door for Deep Analysis

Because Analysis is powered by data sources, a retailer using the tool can learn a lot about what makes their business successful. But critical thinking is important!

Some of the variables that Analysis finds are highly correlated with sales might come as no surprise—like a certain population or dollars spent annually on retail or dining—but others might provide unexpected insights. For example:

• A clothes retailer might find that sales are highly correlated with a non-resident population like hotel guests.
• A pizza chain might learn that sales have a negative correlation with households with residents ages 55 and higher, perhaps indicating that their concept appeals most to families with children vs. empty nesters.

These kinds of findings can be very helpful with choosing future locations, but they can tell a lot about current business at existing locations too. That can give a chain business ideas about how to better serve their current customers, reach new customers, and improve their marketing efforts to reach the right audiences.

Learn and Plan with SiteSeer

Understanding your customers and competitors is key to your success. SiteSeer can help you do exactly that. Schedule a demo today to learn how our platform will equip you with the knowledge and insights to make data-driven decisions for your business today…and tomorrow.