September 2007 Franchising World
Achieve maximum market optimization with minimal “cannibalization” of customers through customer analytics.
By Tom Buxton
Market optimization is one of the major goals of any retail franchise. By optimizing a given market, franchises realize many significant benefits, including efficiencies of scale in distribution, staffing and marketing.
However, in an attempt to optimize a market, franchises can create new problems. If a franchise system has many stores in a single trade area, sooner or later it will ask these important questions: “Will the new stores take customers from the existing stores?” “How do franchises know when this market is truly optimized?”
If the stores are too close together, one will draw business from or “cannibalize” another. If the stores are too far apart, then revenue is being left on the table.
The high cost of guesswork
If optimizing retail markets is so essential, one would guess that retail franchises have developed incredibly effective methods to measure the affect of one store on another when selecting retail sites. Believe it or not, this all-important issue is often left to guesswork.
Huge amounts of money are riding on store location decisions, and a single poor location can have a significant affect on a company’s bottom line. Yet, franchise companies and franchisees continue to select locations through a combination of industry knowledge and gut feelings or, at best, a rudimentary analysis. While there are a few retail professionals who have the talent to consistently select outstanding locations based on “walking around experience,” most of them lack this vital skill. That’s why so many new stores are built with extremely high hopes, only to disappoint their owners with extremely low performance.
The answer: customer analytics
Today, there is a powerful tool that can overcome the challenge of market optimization: the science of customer analytics. In effect, customer analytics replaces guesswork with hard facts; gut feelings with objective data. By profiling their customers and analyzing their market areas, retail franchises can learn exactly where to build new stores for maximum market penetration and revenue. Customer analytics allows systems to predict which customers are likely to patronize a new store, how much cannibalization can be expected and how much the new store is likely to earn. This boosts market share for the franchise company and gives added confidence to franchisees.
For this reason, many leading retailers are now using customer analytics as the basis for strategic decisions of all kinds. But how does it work?
Step One: Knowing the Customer
Most franchise organizations spend a great deal of time and effort on perfecting their distribution systems, accounting practices, marketing strategies and staffing processes. However, little time is spent on the key success factor in their business: the customer.
Marketing technology has improved dramatically in the past decade, providing new ways to find customers and establish productive and profitable relationships with them. In today’s digital marketplace, customers leave trails of data everywhere: when they go shopping with a credit card, use a reward card, watch television, subscribe to magazines or search the Internet. Through customer analytics, franchised businesses can use these massive streams of data to gain valuable insights for retail franchises.
Historically, franchise companies have based their decisions about site locations on simple demographic data. However, demographics alone reveal very little about a person. Two people who have the same age, education level and income can have wildly contrasting lifestyles. One may love National Public Radio, while the other listens to conservative talk radio. One may read Entertainment Weekly magazine, while the other subscribes to Southern Living. In demographic terms they look identical, but they are actually very different people.
Group-level analysis also fails to provide what we need. An analysis may show a large number of “sports enthusiasts” in a given trade area, but what sport? Do they prefer golf or fishing, tennis or mountain climbing?
To be effective, customer analysis must go far deeper than demographics and group-level analysis; and mine down to the household-level and psychographic analysis. To really understand their customers, systems must know many things about them, including their lifestyles, retailer preferences, brand loyalties and media choices.
Step Two: Building the Customer Profile
Every franchise values its most profitable customers: those individuals who regularly patronize the stores and become a continuing source of revenue. Through customer analytics, franchise systems can closely examine the lifestyles and preferences of their best current customers. Where else does this customer shop? What TV stations, radio stations and magazines does this person like? To what brands is this customer most loyal? In this way, a detailed, customized portrait of a franchise’s “best customer” emerges.
Armed with a profile of this best customer, it’s possible to search any trade area in the United States for high concentrations of people who have the same characteristics. When a franchise organization finds a location with many individuals similar to their best customers, they’ll have a potentially lucrative site for a new store.
Step Three: Creating an Effective Model
The traditional method of measuring the people in a trade area is to surround the area with concentric rings and count the households within that ring. A much better way is through a “drive-time gravity model,” which measures a trade area by the time it takes each customer to drive to a retail location. This is a much more precise predictor of customer behavior than a group of concentric rings, because it’s based on the way people really live.
Through the use of the customer profile and the drive-time gravity model, a wide range of useful and in-depth information is revealed. An actual dollar value is assigned to each household within a particular trade area, telling the franchise organization what each household will likely be worth to a particular store location. When a new store location is being considered, the same data reveals which customers will probably patronize the new store. By applying this science to an entire trade area, franchise systems can know with a high degree of certainty when the market has been truly optimized.
As one might expect, customer analytics has far-reaching applications for franchise organizations. Once a customer profile and drive-time gravity model has been developed for a system, it’s possible to examine any trade area in the United States for excellent locations. Franchise companies can quickly learn the density of prospective customers in any market, how many stores the market can support and which regions of the nation offer the highest probability of success. With the answers to these questions in hand, franchise companies have a clear road map for near-term and long-term expansion.
Rethinking Retail Site Selection
Without question, the process of retail-site selection has made giant strides, thanks largely to advances in customer analytics. Unfortunately, many retailers are still using outdated tools that put them at a big disadvantage in an increasingly competitive marketplace. Buying property and building stores in America is simply too costly to leave to chance. In the coming years, the most successful retailers will be the ones who use this new generation of tools to know their customers, optimize existing markets and expand into new areas with confidence.
Tom Buxton is the president and CEO of Buxton. He can be reached at 817-332-3681 or firstname.lastname@example.org.