Hitmetrix - User behavior analytics & recording

Determining Who Will Buy What, When

The recent advances in technology, data collection and outsourcing have given marketers new tools with the power to project markets that were unheard of a few years ago.

Who you are, what you do and where you live can be strongly correlated to your future purchasing behavior. Service bureaus with large national data warehouses can use compiled information to develop new data elements, which, along with your customer database, can build intelligence for targeting segments that exhibit the most desired behavior.

These methods can be used to evaluate and segment your best customers, acquire new prospects, cross-sell services or products, position advertising in proper media areas, locate new sales offices or locate new retail sites.

Past approaches. Choosing a site for a retail location used to be as easy as determining the corner with the highest traffic count or visiting as many sites as possible to get a gut feel for the environment. Simple methods were used that took the number of customers divided by the population to determine the ratio of customer-to-market trade area.

Innovative techniques included the Huff gravity model, which evaluated store size and distance friction to conclude that, in most cases, people who live or work near the store are most likely to shop there.

Each of these methods has been used to define site location with limited success, mostly because of limitations on data availability. New tools and resources are changing that.

Collecting the data: “Understanding who.” There are several data sources helpful in determining who your customers are. Syndicated data collected by large compilers can provide information on income, home ownership, lifestyle and more.

Primary data from surveys can give understanding of different components and variables that make up the lifestyles of customers or prospects. Customer transaction data is the most important component of information not only to answer “Who is my current customer?” but also, “When did they purchase?”, “What did they purchase?”, “Where did they purchase?” and “What did they spend?”

Credit card reverse match. Some retailers do not properly maintain a customer database and may not be aware that they have a gold mine in credit card transaction data.

Working with your bank-card processor and a service bureau, reverse matching of your credit card data can build a name-and-address file that will provide powerful information about your customers. This can provide purchase activity patterns that can be evaluated by time of day to determine whether a customer is shopping from home or from work.

Another innovative database-building technique includes collecting license tag information and reverse matching back to address. These methods allow proactive targeting to businesses or employees that are not in the retail area. You then can understand the variance of what occurs in the local neighborhood trade area vs. the daytime traffic.

Collection of the data sample is important to remove bias. All sites — including high-, medium- and low-performing locations — should be collected. Factors that will affect an urban site may be different from factors that affect a rural site.

Gravity model. Once data is collected, this trade-area analysis step allows understanding of the distance someone will travel to use the specific service. The customer file is attached with latitude and longitude information and data that predict driving time from the current retail location. This model will show the decay that occurs as distance from the individual customer's household increases.

Geographic mapping also will provide evidence of barriers to customer opportunity whether they be bridges, lack of access or other components.

Competitive factors. Using business databases that include SIC codes of the competition and sales volume within the trade area, it is possible to subtract the relative impact on prospective sites. Matching license tag information of your competitor's parking lot can provide additional competitive insight.

Market analysis. Using statistical tools such as regression or household cluster analysis of the customer base will provide detailed profiles of the demographics and lifestyles that make up the customers.

Additional knowledge about penetration within the market and future potential also will become evident. Individual cluster groups and segments can then be studied further in a more discrete method to understand which cells perform better than others and determine correlation to key influencing variables (“Why people buy”) based on their individual profiles. The resulting wisdom allows assessment of all market areas that exhibit similar characteristics.

Trade area and site scoring. Once the analysis is completed, scoring of optimum trade areas and prospective sites will narrow down those that can deliver the best opportunities for new customer growth within the known competitive environment.

Reverse site analysis. Using business econometric modeling techniques, analysis can be carried out from the site outward to evaluate the potential success of any business to locate there.

Factors that include economic input/output and commerce area analysis can predict success of a particular business to aid economic development managers within city governments.

Benefits of who, what, where and why. Using these methods will give intelligence on “Who will purchase?”, “What they will purchase?”, “Where they are located?” and “Why they will buy.”

Like weather forecasting with marketing data, these tools and methods will allow successful navigation in turbulent markets of the future.

Kurtis M. Ruf is vice president of sales, marketing and business development at Ruf Strategic Solutions, Olathe, KS.

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