Two sites, one city. The smaller market won.

The decision

A metal buildings manufacturer came to us weighing two candidate sites in the same metro, Kansas City. Both were viable. Only one would be next. The question looked simple: which location would generate more revenue?

Simple questions like this are where the expensive mistakes hide. A site is a multi-year commitment. Get it right and it compounds. Get it wrong and it drags the whole portfolio and stalls the next opening.

What anyone could see

By the numbers you can pull in an afternoon, it wasn’t close. Site A sat in the denser, busier part of the metro: roughly six times the households and nearly five times the businesses within reach.

More people, more activity, more of everything. Most site-selection tools, and most gut instincts, would plant the flag at Site A and never look back.

What the model predicted

Site B, with a fraction of the population, was projected to earn about 32% more, roughly $2.5 million in additional first-year revenue. The location that looked second-best on every visible metric was the one the model put first.

Why the bigger market lost

Because raw population isn’t what drives this business. Tupll doesn’t score a location on how many people are nearby. It scores it on how many of the right people and businesses are nearby, where “right” is defined by the manufacturer’s own performance history.

The model is trained on where this company already makes money and what those winning locations have in common: the mix of households, the surrounding industries, and the buying behavior that actually converts into orders. Measured against that standard, Site B’s smaller market was the better market. It had far fewer people, but a much higher concentration of exactly the ones this manufacturer sells to. Site A’s large population was mostly noise, households and businesses that look good in a count and rarely become customers.

Count heads and you pick Site A. Understand the market and you pick Site B.

The lesson for site selection

The gap between $7.8M and $10.3M was invisible to anyone looking at the obvious numbers. It only appeared once the location was measured against the client’s own revenue, not against generic population and business counts.

That gap is the difference between a good location and an expensive one. It is also the entire point of a predictive model: not to describe a place, but to tell you what your brand will actually do there, before you sign or build. The bigger market is not always the better market, and the only way to know which is which is to let your own performance history define what “better” means.

Actual Tupll engagement, 2026. Figures rounded; client withheld for confidentiality. Revenue figures are model predictions.