How Analytics Drives Smarter Retail Growth Through Site Selection

By Charles Hogan, co-founder and CEO, Tranzlogic. 

Not enough businesses are using data analytics, and it’s not because there’s a data shortage. A new survey found that more than half of companies with devices that collect data aren’t using it for customer insights. Another found that many tech executives don’t think their companies can fully use the data they gather. This reluctance to use data can be costly, especially when businesses undertake expensive expansion programs without all the information they need to make the best possible orientation, site, and timing decisions for new stores.

Adding a new location is expensive and requires a huge investment in market research, site planning, hiring, and more. Successful expansions increase revenue and deliver solid returns on investment. Failed expansions drain a company’s cash and time. By analyzing customer and market data, businesses can make smarter expansion plans based on real information rather than hunches or the movement of the herd. Let’s look at five expansion processes you can improve with data, such as: customer age, location, income level, family size and status, interests, and spending habits.

See if your current location is the right one

Before your business expands, it’s a good idea to use your data to evaluate your existing locations, to make sure they’re oriented toward customers in the immediate area. For example, a sandwich shop chain in a large city uses location and market data to customize offerings at each location. One location in a neighborhood full of young singles offers a regular happy hour, while locations in neighborhoods with families offer kids-eat-free lunch deals. In this way, you can maximize the value of your existing locations, which can in turn raise the performance benchmarks you set for new locations.

Decide where your business should expand

Data analysis can help you identify other locations – locally, regionally, or nationally – that align with your customer base. Rather than guess or follow competitors into locations, search the data for areas with high concentrations of prospective customers that match your existing customers’ age range, income level, spending habits, and more. For example, the sandwich shop chain might compare two possible locations: one near 80,000 households, and one near 100,000. The cost to open would be the same in each location, but in the first, data analysis shows a potential market penetration of 19%, compared to 10% for the area with more households. Choosing the location with fewer households but higher potential penetration results in more actual customers, a lower cost per customer, and a better return on investment.

Once you’ve selected a neighborhood, your data can also help you compare and evaluate specific commercial site within that area. For example, you can assess your potential sites and choose the one in the area that looks most like your existing successful locations and has the highest forecast returns.

Calculate how many locations an area can support

Overexpansion is a costly mistake that businesses can avoid by studying the market data for each area where they do business or may expand. For example, a grocery store chain analyzes market data for an entire metro area before adding another store. They want to know where customers who fit their profile live, how far they currently travel to reach a store, how much they spend per visit, and how often they shop. By choosing locations that meet demand without cutting into existing stores’ customers, the grocery chain gets the best return on its expansion budget.

Know when it’s time to open a new location

Setting the right pace for expansion can be a challenge. Ongoing review of market and customer data makes it easier to time expansions to maximize ROI. The sandwich shop chain, for instance, may notice that a neighborhood outside its current area is changing—young people with disposable income are moving in, and they’re more likely than existing residents to eat out or pick up takeout. Local media coverage makes it seem like this neighborhood is changing overnight, tempting business owners to consider expansion now.

However, a careful look at the data shows that the trend is moving slower than the hype. The sandwich shop can use data-backed projections to know when an expansion will have the customer base to support it and to avoid losing money to low traffic by expanding too soon.

Measure new location performance

Once you open a new location, it’s important to track its performance, and here customer data is valuable as well. It allows you to benchmark and track the location’s market penetration, cost per customer, lifetime value of each customer, and your return on investment. These metrics can help you refine your expansion strategy further with each new location. They can also show you if certain locations are underperforming, give you indications of how to correct underperformance, and show you when it’s time to close a location–for example, if the neighborhood demographics are changing in ways that erode your customer base.

The bottom line is that data analytics can help your business evaluate its current locations, choose sites and time openings for additional locations, and monitor each location’s performance over time. The information is already out there—it’s up to your business to make smart use of it.

Contributed by Charles Hogan, Tranzlogic co-founder and CEO, who has over 20 years of experience in fintech, data analytics, retail services and payment processing industries. Follow on twitter @Tranzlogic