Retail site selection can be a risky and expensive business. Make the wrong decision about a new location and you risk wasting money, alienating customers and losing prospects to your competition. A marketer’s ability to answer the following questions correctly is vital:
· What behavior is driving customers into your stores?
· What new products and services would increase profitability in a new market?
· Which targeted groups of customers are at risk for switching to competitors? How do targeted groups view your stores versus your competitors’ stores?
· What marketing messages and promotions will be most successful?
· What niche opportunities for new locations exist in markets that you already serve?
· What life stage trends will be the catalyst for the merchandising decisions you make and the locations you choose?
That’s right. What life stage trends are driving your decisions? Today’s site selection decisions extend far past “cloning” best customers to understanding what behaviors, life experiences and attitudes went into making them your best customers. Furthermore, how are your current customers and prospects going to change in the years to come?
A new weapon in the site selection process — life stage migration trend analysis — lets marketers see how markets and consumers change over time, project the life stage trajectories of their best customers and anticipate their future needs.
By using the most accurate and timely consumer behavior information, marketers and planners now can collaborate in exciting and rewarding ways, transforming site selection into a dynamic strategic decision-making tool with long-term return on investment impact.
Take, for example, a wireless telecommunications service provider interested in finding retail locations for new stores. As part of its due diligence, the company, which we’ll call Telco X, uses life stage segmentation to produce an analysis of what its best customers look like. The new site selection process begins by finding locations whose populations most closely resemble the analyzed description of its best customers for a specific set of wireless products.
On this basis, our fictional company identifies two ZIP codes, 93108 — Santa Barbara and 91105 — Pasadena, as potential locations for its new retail site and begins the comparison process. These two ZIPs have high concentrations of two groups of targeted households that represent ideal candidates for the company’s wireless products and are a primary consideration in Telco X’s new site evaluations.
ZIP code 93108:
Target Group 1: Current household, 597
Target Group 2: Current household, 1,782
Percent of total population in ZIP: 51.8 percent
ZIP code 91105
Target Group 1: Current household, 588
Target Group 2: Current household, 1,450
Percent of total population in ZIP: 43 percent
These ZIP codes look remarkably similar in terms of their concentration of these household targets. A traditional approach would indicate that either ZIP code would be a good choice for expansion. However, let’s look at a variation of this analysis, considering the following new aspects of information:
· The migration of households from one target group to another over time.
· Their risk of switching to a competitor.
Based on changes in life stage characteristics, this analysis yields a remarkably different result. Though current-year snapshots of these two ZIP codes make them look roughly the same, after one year Santa Barbara’s potential number of high prospect customers is significantly higher for Target Group 1.
ZIP code 93018: Increase in Target Group 1 after one year: 17 percent.
ZIP code 91105: Increase in Target Group 1 after one year: 7 percent.
For Target Group 2, the story is not so good. It experiences a decrease:
ZIP code 93018: Increase in Target Group 2 after one year: -37 percent.
ZIP code 91105: Increase in Target Group 2 after one year: -36.6 percent.
Initially, a decrease in the number of Target Group 2 households makes both ZIPs look undesirable, and traditionally the analysis would stop there, at a snapshot of numbers, a sheer quantity, not quality, assessment based on the total number of households in the ZIP.
However, where are these households going during the 12 months? What is the reason for the decrease? What does it mean for Telco X?
It turns out that 18 percent of Target Group 2 households, because of life stage changes, most likely will turn into Target Group 1 households. This is good news for Telco X, because although the overall number of Target Group households declines, the aggregate number is a more loyal constituency, much less likely to switch to a competitor’s products and services.
Target Group 2 is more than twice as likely to subscribe to a competitive provider. Whereas Target Group 1 indexes at roughly equal interest in Telco X and its main competitor, Target Group 2 represents a significant risk.
Targeted households are migrating from a higher-risk-of-switching group into a lower-risk-of-switching group. A traditional look at the numbers would not reveal this information and would skew the equation in the wrong direction.
This competitive profile information will affect Telco X’s decision and let the company be strategic in its site selection, market development, competitive tactics and marketing communications. For example, after selecting a site, this insight into the competitive landscape can be used to decide proximity to competitors, product mix and merchandising options and marketing messages in creating promotions and other marketing messages.
Which ZIP code affords the best investment and long-term prospects based on anticipated population trends in life stage-related consumer behavior? Clearly, 93108, Santa Barbara, represents greater long-term, loyal customer acquisition potential and a greater ROI. Given time to market after site selection has been finalized, reliable information reflecting trends in future life stage household shifts is invaluable.
In this case, at a tactical level, the wireless company can enhance its selection process by using next generation segmentation and profiling tools to:
· Create a short list of markets whose populations included significant numbers of prospects much likelier to become target customers.
· Identify markets whose populations resemble the target market, but are migrating into life stages suited for new product offerings — markets that represent better long-term prospects.
· Find niche opportunities within currently served markets.
· Target at-risk competitor customers and likely-to-switch customers.
· Make merchandising decisions tailored to the population around the site.
· Reduce costs associated with evaluating multiple potential sites by creating a more meaningful and targeted short list.
· Tailor communications to customers and prospects in a way that moves them through the company’s product lines as they move through their life stages.
On a strategic level, the benefits of this approach include:
· Expanding and diversifying the company’s prospect universe by understanding how target households move in and out of its best-customer pool over time.
· Increasing ROI on brand-building investments by improving the company’s understanding of customer and prospect needs.
· Capturing a pool of customers with greater overall lifetime value by choosing sites with not just immediate potential, but long-term potential based on the life stage migrations of their populations.
· Reducing the overall risk of site selection mistakes.
Enhancing site selection with the use of customer segmentation tools and information lets marketers and planners add a new dimension to site selection processes and gain a better understanding of not only who the customer is but also whom the customer will be.
