When the team at a 40-location specialty chain started a 12-market expansion study, they were using a process that had worked well enough for their first 25 locations: an experienced VP of Real Estate, strong broker relationships in their core regions, and a set of site criteria that had been assembled over years of trial and error. Markets that felt right got serious consideration. Markets that did not feel right got passed over.
This hypothetical scenario illustrates a pattern we see across growing chains in the 20 to 60 location range. The process that works when the founder or a highly experienced executive can pattern-match against their personal history starts to show cracks when the chain is expanding faster than institutional experience can accumulate, or when expansion extends into markets that the senior team has less direct familiarity with.
The question is not whether instinct and experience are valuable. They are. The question is what analytical infrastructure supports those instincts, and whether it is consistent enough across candidates to produce defensible comparisons.
What "Gut Feel" Usually Means
When an experienced retail real estate executive says a market "feels right," they are usually encoding a real pattern: they are noticing something about the population density, the competitive landscape, the presence of co-tenancy that matches their brand, or the quality of the available real estate stock. That encoded pattern often correlates with real performance signals.
The problem is that gut feel is not transferable, not consistent across evaluators, and not auditable after the fact. Two experienced VPs can look at the same 12-market shortlist and come away with meaningfully different rankings. When one of them eventually leaves or the chain hires a new development director, institutional knowledge gets rebuilt from scratch rather than compounding over time. And when a gut-driven decision turns out badly, it is very hard to understand what signal was misread versus what changed unpredictably in the market.
A structured scoring system does not replace experienced judgment. It gives experienced judgment a consistent format to operate through, so that the reasoning is visible rather than implicit.
Building a Scoring Framework
For the hypothetical 40-location chain conducting its 12-market study, moving toward structured scoring meant deciding what signals to measure, how to weight them, and how to aggregate them into a comparable score for each candidate market.
The signals they settled on fell into three groups. Market fundamentals: total trade area population within a drive-time catchment appropriate to their category, household income distribution, and category spending volume. Competitive context: the number of direct competitors operating within the trade area, the aggregate square footage of category supply relative to demand, and the recent trend in competitor openings versus closings. Network fit: what percentage of the trade area was already served by an existing company location, and what incremental revenue the new location would realistically add after cannibalization.
Each signal was scored on a 1 to 5 scale against benchmarks drawn from their existing store portfolio. A market with population and spending characteristics similar to their top-quartile stores scored a 5 on those dimensions. A market that was below their historical lower bound for viable stores scored a 1. The weights assigned to each dimension reflected their category's economics: for their format, competitive density mattered more than population size because their concept had succeeded in smaller markets with the right competitive dynamic.
What Changed When Scores Replaced Rankings
When the 12-market study was scored rather than ranked by intuition, several things happened that the team did not entirely expect.
Three markets that had been on the shortlist primarily because of broker relationships scored below their viability threshold on market fundamentals. Two of them had reasonable foot traffic locations available, but the category spending in the trade area did not support the volume targets at their operating cost structure. Those markets came off the shortlist, not because anyone overruled the broker relationship, but because the score gave the VP of Real Estate a defensible basis for the conversation.
Two markets that had not been on the original shortlist because they were in smaller metros scored highly once the scoring weighted competitive density appropriately. Both were regional centers with strong category spending and very few direct competitors. In an intuition-driven process, smaller markets often get filtered out early because of size bias rather than actual economics. The scoring exposed that bias.
Perhaps most usefully, the scoring created a structured discussion within the leadership team about which markets to prioritize for site-level evaluation. With 12 markets scored, the team could see immediately which were clear leaders, which were borderline, and which were clearly below the threshold, without having to litigate each one through a presentation.
The Limits of Scoring
A market score does not evaluate a specific site. It narrows the field to markets worth the cost of detailed site evaluation, but the quality of available real estate, the terms landlords are offering, and the specific street-level dynamics in each market still require feet on the ground. A high-scoring market might have no viable locations available at acceptable lease terms. A borderline market might have an exceptional location that changes the math.
Scoring also does not account for the chain's operational readiness to enter a new market. Distribution logistics, staffing pipelines, and marketing reach all affect whether a new market can be entered successfully regardless of what the location data says. These are execution factors that site selection analysis appropriately brackets out but that the overall expansion decision must incorporate.
The value of structured scoring is not that it makes location decisions simple. It is that it makes the reasoning visible, consistent, and improvable. When a high-scoring market turns out to underperform expectations, the team can look back at which signals predicted well and which did not, and adjust the weighting or criteria for the next study. That feedback loop is not possible when decisions are made through implicit pattern matching.
A Starting Point, Not a Formula
For chains in the 20 to 60 location range building out more structured evaluation processes, the most important thing is not to design the perfect scoring framework on the first try. The most important thing is to start documenting the criteria that experienced team members are already applying implicitly, quantifying them well enough to compare candidates side by side, and tracking predictions against outcomes.
The framework improves with use. After three or four market studies using scored criteria, the patterns in which signals predicted well and which were noise become visible. The weighting gets better calibrated to your specific category and operating model. The process becomes something the next hire can learn in weeks rather than years.