How Nontraditional Variables Can Shift Retail Site Selection Results
by Brett Bayduss, on Sep 15, 2026, 7:00:03 AM
Retail site selection models are most effective when they combine customer behavior, demographic characteristics, competitive conditions, and real estate fundamentals. These traditional inputs provide the foundation for understanding where a retailer's existing stores succeed and which markets offer the strongest potential for expansion.
But a technically sound model can still miss part of the story. Every brand has distinct products, customers, operating requirements, and cultural connections. If a model relies only on widely used demographic and psychographic variables, it may identify attractive retail markets without fully explaining why certain locations are a better fit for that specific brand.
When a Strong Model Still Feels Incomplete
Site Selection Group recently completed a site selection project that illustrates this challenge. The initial analysis followed a proven methodology. We mapped the retailer's existing customers, evaluated store performance, and used psychographic segmentation to identify the behavioral qualities and lifestyle characteristics shared by its strongest customers.
We then used demographic analytics to profile those customers by factors such as age, household income, population growth, homeownership, household composition, and consumer spending. Competitive presence, market size, growth trends, and other traditional site selection factors were also incorporated into a weighted ranking and scoring model.
The result was statistically defensible and created a clear hierarchy of markets. Yet the highest-scoring markets did not provide a complete picture of where the brand was most likely to resonate. Some locations looked excellent on paper but lacked the cultural, regulatory, or product-specific conditions that helped distinguish the retailer's best opportunities.
The model was not wrong. It was simply missing variables that reflected the brand itself.
Adding Brand-Specific Variables
The next step was to move beyond standard retail datasets and identify nontraditional variables tied directly to the company's products, customers, and value proposition. These factors were tested for relevance, geographic consistency, and their relationship to existing store and customer performance before being added to the model.
Once the most meaningful brand-specific variables were incorporated and appropriately weighted, the rankings changed. Markets that had appeared similar based on demographics began to separate. Some moved higher because they demonstrated stronger alignment with the brand's customer culture and operating environment. Others moved lower because traditional demand indicators were not supported by the local conditions most important to the retailer.
This created a more customized model: one that did not simply rank attractive retail markets, but ranked markets according to their suitability for this particular concept.
What Are Nontraditional Site Selection Variables?
Nontraditional variables are data points that may not appear in a standard retail market analysis but which help explain customer demand, product relevance, market acceptance, or operating risk for a specific brand. They can vary substantially by retailer.
Product Ownership and Purchasing Patterns
A truck dealership or truck-oriented retailer may want to understand which states and metropolitan areas generate the highest pickup-truck sales, registrations, or ownership rates. Two markets may have similar incomes and population growth, but the market with a stronger truck culture may offer a more natural customer base.
Insurance Costs and Consumer Economics
Insurance rates can influence both product affordability and household discretionary spending. For automotive, recreational, property-related, and other insurance-sensitive categories, high premiums may reduce what consumers can spend or alter the total cost of ownership. In other cases, lower rates may create a meaningful market advantage.
Legislation and Regulatory Alignment
Certain retailers need to understand whether state or local laws support, restrict, or create demand for their products. Licensing rules, product restrictions, tax policy, environmental requirements, incentives and consumer-protection regulations can make otherwise similar markets perform very differently.
Political and Policy Direction
Recent elections may also signal future changes in regulation, taxes, infrastructure spending or public policy. A newly elected leader whose agenda supports a retailer's industry or product category could improve long-term market conditions, while the opposite may introduce risk. Election outcomes should not be used as a short-term proxy for demand, but they can help identify policy direction that deserves further evaluation.
Events, Culture and Local Identity
Annual events and local traditions can reveal cultural alignment that demographic statistics cannot capture. A market that hosts NASCAR races, major outdoor recreation events, agricultural fairs, motorcycle rallies, or multiple automotive gatherings may attract consumers whose interests closely match a brand. Event attendance, frequency, and visitor origin can provide additional evidence of that connection.
Why These Variables Change the Recommendation
Traditional variables often identify whether a market has enough people, income, spending power, and growth to support a store. Nontraditional variables help answer a different question: Does this market have the specific conditions that make consumers more likely to connect with this brand?
That distinction matters when several markets have similar demographic profiles. A conventional model may score them within a narrow range. Brand-specific data can expose meaningful differences in product adoption, cultural affinity, regulatory risk, household economics, or event-driven demand.
The goal is not to add unusual data simply to make a model appear more sophisticated. Each variable should have a clear business rationale, reliable geographic coverage, and a reasonable relationship to customer behavior or store performance. Variables should also be tested to avoid double-counting factors already represented elsewhere in the model.
How Retailers Can Build a More Customized Model
A more complete retail site selection process should include several steps:
- Begin with existing customer and store-performance data to understand where the concept already succeeds.
- Use demographic and psychographic segmentation to identify the characteristics and behaviors shared by the retailer's best customers.
- Develop hypotheses about the product, brand, operating model, and local conditions that may influence performance.
- Identify alternative datasets that measure those conditions consistently across markets.
- Test each variable against known results and incorporate only the factors that add explanatory value.
- Adjust weights, review ranking changes, and apply local market due diligence before making a final recommendation.
This approach keeps customer analytics at the center of the process while allowing the model to reflect qualities that conventional datasets may overlook.
Conclusion
Demographic analytics and psychographic segmentation remain essential components of retail site selection. They provide a strong, objective foundation for identifying target customers and comparing markets. However, they should not be treated as the complete answer.
The best models combine traditional measures of demand with carefully selected variables that reflect the retailer's products, customer culture, regulatory environment, and economic realities. Nuanced local factors, captured through the right data points, can reveal why one market is better suited to a brand than another market with a nearly identical demographic profile.
By incorporating nontraditional variables into the analysis, retailers can move from a general ranking of attractive markets to a customized strategy built around the factors that truly drive their success.
