Why keep choosing markets by founder instinct once a specialty chain reaches 40 locations? It worked for the first 25, when a 12 market expansion study could lean on an experienced VP of Real Estate, strong broker ties in core regions, and site criteria refined through trial and error. At greater scale, markets that felt right received attention while others were dismissed, with too little speed or accountability in the choice.
This hypothetical case reflects a pattern across chains growing from 20 to 60 locations. A process built on a founder's or seasoned executive's ability to match patterns from personal experience begins to crack when expansion outpaces institutional learning, or reaches markets the senior team knows less directly.
Instinct and experience matter. The question is what analytical support guides them, and whether it produces consistent, defensible comparisons across candidate markets.
What Gut Feel Really Encodes
When a seasoned retail real estate executive says a market "feels right," that judgment usually captures a genuine pattern: population density, competition, co tenancy suited to the brand, or the quality of available real estate. Those impressions often align with meaningful performance signals.
Gut feel cannot be transferred reliably, applied consistently by different evaluators, or audited later. Two experienced VPs may rank the same 12 market shortlist very differently. If one leaves or a new development director arrives, the chain rebuilds knowledge instead of accumulating it. When a gut based choice performs poorly, it is also difficult to tell which signal was misread and which market change was unforeseeable.
A scoring structure does not displace seasoned judgment. It gives that judgment a shared operating format, making the logic visible instead of leaving it implicit.
A Scoring Framework
For the hypothetical 40 location chain's 12 market study, structured scoring required choosing the signals to measure, setting their weights, and combining them into comparable scores for each market.
The selected signals covered three groups. Market fundamentals included trade area population within a category appropriate drive time catchment, household income distribution, and category spending volume. Competitive context covered direct competitors in the trade area, category supply square footage relative to demand, and the recent balance of competitor openings and closings. Network fit measured the share of the trade area already served by a company location and the incremental revenue a new store could realistically add after cannibalization.
Each signal received a 1 to 5 score against benchmarks from the existing store portfolio. A market resembling the top quartile stores in population and spending scored 5 on those measures. One below the historical lower bound for viable stores scored 1. Weights reflected the category economics: competitive density mattered more than population size because the format had worked in smaller markets with the right competitive dynamic.
When Scores Changed the Ranking
Scoring the 12 markets instead of ranking them by instinct produced several outcomes the team had not fully anticipated.
Three markets shortlisted mainly through broker relationships fell below the viability threshold on market fundamentals. Two had suitable foot traffic sites, yet trade area category spending could not support volume targets at their operating cost structure. They left the shortlist, not because the broker relationship was dismissed, but because the score gave the VP of Real Estate a defensible basis for the discussion.
Two smaller metros left off the original shortlist scored strongly once competitive density received the proper weight. Both were regional centers with strong category spending and few direct competitors. Intuition led teams often screen out smaller markets because of size bias, not actual economics. The scores made that bias visible.
More importantly, scoring gave leadership a structured way to discuss which markets deserved site level review. With all 12 scored, clear leaders, borderline candidates, and markets below threshold were apparent without litigating every case in a presentation.
Scoring Has Limits
A market score is not a site evaluation. It identifies markets worth the cost of detailed review, while real estate quality, landlord terms, and street level conditions still need on the ground work. A high scoring market may lack a viable site at acceptable lease terms. A borderline market may contain an exceptional location that changes the economics.
Scores also omit operational readiness for entering a new market. Distribution logistics, staffing pipelines, and marketing reach can determine success regardless of location data. Site selection analysis properly brackets these execution factors, but the broader expansion decision still has to include them.
Structured scoring does not make location choices simple. It makes the reasoning visible, consistent, and open to improvement. If a high scoring market misses expectations, the team can identify which signals held up and which did not, then revise criteria or weights for the next study. Implicit pattern matching offers no comparable feedback loop.
A Framework, Not a Formula
For chains with 20 to 60 locations building a more structured evaluation process, the first priority is not a perfect framework. Document the criteria experienced team members already use implicitly, quantify them enough to compare markets side by side, and track predictions against outcomes.
The framework gets better through use. After three or four scored market studies, it becomes clearer which signals predicted performance and which were noise. Weights can then fit the category and operating model more closely. The process becomes teachable to the next hire in weeks instead of years.