Good companies know who their target consumer segment is — great companies know where they live and how to get to them. Often times, defining your target consumer is a qualitative process involving lot of guess work and customer interviews. This information can be skewed by the customer or data collector.
For example: If I sell carpet, I may ask customers about their home, income and preferences. They are going to tell me they have an exquisite home in a nice neighborhood, make more money than Scrooge McDuck and want soft quality carpet that will last for 100 years without ever getting dirty. Within my company, we may think our carpet is twice the quality of the competition, so the target market is the high-income, well-educated socialites in gated communities.
Starting with unbiased data and correlating that data to historical sales performance can provide a firm footing for defining a target market. MarketStar uses ArcGIS products by Esri to pull consumer demographic and psychographic information for any geographic range. These inputs are collected using census data and regional consumer survey studies. The data is collected without a specific use in mind removing a lot bias. More than 2,000 data points are available, including basic information like age, income and population. Other applicable data points include spend data for remodels, unemployment rates, home prices and population growth rates (to name a few). Other programs, such as Microsoft’s MapPoint, also have some consumer trait data that can be used if one knows how to extract it.
Sell through data at the retail store level will help validate which inputs are more important than others. For best results, align the available demographic data with the area surrounding each store with POS data. Build a tool around a scorecard and you are ready to go. See if high-income areas also have high POS, do low-income areas have high POS? Is it a stronger correlation with sales than age? What age group is more indicative of success?
You may find you are actually selling more carpet in low-growth areas with aging populations and low incomes. Once you have some of these correlations, you can start to create a hypothesis. Couples content in their homes may have been saving for years and finally want to buy nice carpet now that the kids are out of the house. (I already have started a stash of cash.)
Finding these correlations between consumer target markets and historical performance gives companies the information needed to make strategic decisions. Companies can align resources on stores where POS isn’t available but in similar areas as high-performance stores with sales data. A gap analysis between demographic ranking and POS can show some interesting opportunities.
The key is to set up methodologies that can be applied to all points of sale. Define consumers at the store level and not as a market as a whole (every major market has areas to avoid and areas of opportunity). Remove as much bias as possible to get at the heart of what consumers are really doing.
Meaningful strategy is built on a baseline of reality.