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Combining Demographic and Distance Data to Maximize Target-Marketing Response
Car wash owners share in common a continued reliance on direct mailings to reach current and prospective customers. Although saturation mailings may work for companies with limited geographic reach, targeted mailings are the most efficient and effective for owners within larger areas. It may seem elementary, but it is difficult to succeed at target marketing if you don't first clearly define your target market. This definition is best achieved through the consideration of demographic and distance data, yet this innovative combined approach is as yet a rarity.
Hopefully, you have joined the expanding ranks of marketers who incorporate demographic data to define their best customers. If not, a proven method of doing so involves using your current customer database - either in its entirety or with a suitably-sized random sample - to identify those customers generating the top 20 percent of your revenues. You can then pull a similarly-sized random sample of consumers from the same geographic areas - that is, from the same list of ZIP codes or other applicable geography - for the purpose of tracking how your customers compare to other consumers living in the same area. The random consumer samples are obtainable from any of the major, national consumer database compilers.
Enhance your consumer and customer files with a range of demographic and lifestyle data, such as data available from the Experian INSOURCE consumer enhancement database. These data include such details as age, gender, marital status, rent-versus-own, and other characteristics that can relate the traits of your purchasing customers to your products. Cross-tabular comparisons of the two files can reveal how your best customers differ from those in the market at large (for instance, demonstrating that your best customers tend to be older, married homeowners). Going further, logistic regression models can be developed to predict the probability of a consumer exhibiting best customer behavior. Logistic regression will typically yield 75 percent to 80 percent correct classification rates. Applying the model to the original research sample will demonstrate the percent of cases correctly classified, and the model will generate a probability score that can be translated into consumer decile rankings (that is, a percentile ranking in steps of 10 percent at a time). With the model built, target-marketing programs can focus on customers scoring in the top deciles.
If you leverage the modeling approach to identify your best customers, you are taking advantage of a vital tool. But while many car wash owners may currently use demographic data at some level, few combine this information with distance data crucial for maximizing response rates. Yet distance data are among the top three predictors of customer response.
What are distance data? Simply put, these data indicate your customer's geographic point of reference. Geographic proximity can be a key factor in the success of your target-marketing program. For example, the farther consumers must travel to reach your nearest car wash, the less likely they may be to respond to your target marketing offer. Moreover, your physical car wash locations create a billboard effect - the nearer the car wash, the more likely it will occupy significant consumer mindshare.
Traditionally, if car wash owners used distance data at all, distance was determined through a "radius-selection" -that is, all consumers within a given number of miles from a car wash were considered to be in its trade area. However, customers rarely live their lives within neatly-drawn circles, or along straight-line paths from the surrounding businesses. The road network and dominant commuting routes can be far more predictive of the consumer's ability and likelihood of responding to an offer from a given car wash.
Thus, to make the best use of geographic information in identifying and reaching your target market, today's car wash owner must:
Identifying Customer Points of Reference
Bear in mind that customers have multiple points of reference. These points may include:
Essentially, to the extent that competitors' options are available to consumers, response rates will decrease. To the extent competitors' sites are distant from consumers, response rates will increase. And if your car washes are located in proximity to traffic generators such as quick-service restaurants and high-volume retailers, response rates will increase. Likewise, outdoor advertising increases awareness. As a general rule, response rates increase as the number of reference points increase.
Combining Distance and Demographic Best-Customer Data
The combination of demographic and distance data scoring works two ways. First, it helps you identify the areas most vital to reach. Second, it helps you to identify areas that should be excluded, based on a low probability of response.
In the example of best-customer modeling above, both files should be enhanced with the measurement of the customer's distance to your nearest car wash location, as well as to your nearest competitor option. Distances may be calculated either linearly ("as the crow flies") or via the road network.
As a final but consequential point, many car wash owners may be concerned about the costs of such analyses. Today, however, we have cost-effective options for securing and processing data. Demographic and distance measures can be acquired and analyzed efficiently for your target marketing campaign.
Larry Daniel, president and CEO of Conclusive Strategies, is a widely-published expert in geographic information systems and the application of GIS technology to consumer-marketing strategy. A former president of the GeoBusiness Association, Mr. Daniel's leadership extends to lectures and publications for GIS experts. He is the co-author of two books, Inside Arcview GIS and Inside MapInfo Professional, and has served as a columnist for Business Geographics Magazine and Directions Magazine. His education includes an MA in Geography from the University of Texas at Austin and a BS in Computer Science from Bucknell University.
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