Straight answers, before you ask.

Tupll is a site selection methodology: a machine learning driven evaluation system that predicts how a location will perform for your specific brand. It is trained on your own locations and revenue history, then blends demographic signals, business activity, competitive context, consumer behavior, and local economic patterns into a single predictive score for any candidate site.

From your side, it is simple: you give us your location addresses and revenue history, and we handle everything else. Behind the scenes, we pull 40 to 50 variables for every zone across multiple radius bands, use traditional statistics to find which variables actually matter for your brand, then run several machine learning models and combine them into one predictive score. All of that complexity stays on our side of the table.

Because the variables that drive performance are mostly invisible from the street. Two corners can share traffic counts and rooftops while differing completely in household mix, nearby industries, buying behavior, and access friction. Our Kansas City case study shows a site with six times fewer households predicted to out-earn the obvious pick by 32 percent, because it held exactly the right customers for that brand.

Raw population counts everyone; revenue comes from the subset who actually buy what you sell. And it is not only people and households: the companies in an area matter just as much, because the mix of nearby businesses and industries shapes demand, daytime traffic, and B2B opportunity. The model weighs who and what is nearby, not how many: the blend of homes, companies, and buying behavior your best-performing locations share. A large population that rarely converts is noise. A smaller area dense with your exact buyers is a market.

No. A radius report describes an area with off-the-shelf averages. Tupll predicts your revenue in that area, built from your own performance history. The difference shows up when the two disagree, which is exactly when a generic report would have pointed you at the wrong site.

Every score traces back to inputs you can inspect: which variables mattered, how they were weighted, and why a site ranked where it did. Your CFO can open the hood instead of trusting a mystery number. That is the difference between a defensible forecast and a black box.

Two columns: your location addresses and revenue by site for the past one to three years. That is the whole ask. Most teams already have it in a spreadsheet.

About fifteen minutes. There is no IT project, no data team, and no integration work. Finding out whether your data qualifies takes about two minutes; pulling the file together once you decide to move takes about fifteen.

On the analytics side, none. Data cleaning, modeling, scoring, and the written report are all on us. You still tour the sites, work with your broker, and negotiate the deal; the analysis underneath those decisions is fully done for you.

There is still a path, and the investment is actually lower because there is no existing location data to analyze. Our first location model applies the full statistical and GIS methodology without supervised machine learning. It will not be as precise as a model trained on your own history, but it beats guesswork by a wide margin, and we will tell you plainly which approach your data supports.

It stays yours. We use your locations and revenue history to build your model and score your candidate sites, and for nothing else. The model built on your data works only for your brand, which is exactly why its predictions are worth more than a generic index.

Very, with one caveat. The score reflects the relative health of a location based on who and what is around it. It will never be the exact revenue figure, because the rest depends on you: choosing a practical, accessible building and running it well. When those are in place, our predictions land in a tight range. When we have gone back years later to check, the few that missed had specific reasons, like a weak manager.

Against reality. We backtest models on held-out locations and run a year-one look-back comparing predicted performance to actual first-year sales. Validation is a stage of the methodology, not an afterthought, and it is why the forecast holds up in front of a CFO.

Those hand you data and dashboards; you still have to turn maps into a decision. Tupll hands you the decision layer: a revenue prediction score for each candidate site, built on your own numbers, with the reasoning exposed. You keep whatever tools you use today. Tupll adds the prediction layer on top.

No. Tupll has no brokerage ties, earns nothing on the transaction, and does not sell the space. The only thing we are optimizing for is calling the location correctly, which is exactly what you want from the party scoring your sites.

You know your brand better than we ever will, and that is the point. What no one can hold in their head is an entire market: within a single metro there are many zones, and performance varies widely between them. We look objectively at where your locations have done well and poorly, then apply that pattern to areas you have not entered yet. You bring the brand knowledge; we bring the data work that is hard to do by hand.

New model development is a one-time $12,999 for brands with existing locations, and the first location model is a one-time $7,999 for brands opening their first location. Think of either as a once-in-a-lifetime bill: you never pay it again unless you ask us to refresh the model years down the road. The model keeps working for you for years to come, and all you pay for going forward is per-site evaluations at $120 per location, purchased per batch with a nine-location minimum.

A custom machine learning model trained on your revenue history, plus a full evaluation of your candidate sites. Deliverables include a market viability heat map, a location scorecard, scenario forecasts and rankings, and an executive strategy summary written for the room where the money gets approved.

Once your model is built, you can score new candidate locations against it as you expand, at $120 per location in batches of nine or more. Hand us whatever you have: a street address, a latitude and longitude coordinate, or an entire market you want mapped. It is up to you. The model stays specific to your brand, so every new batch benefits from everything it already learned.

Yes. With no sales history to train on, we apply the first location model: our full statistical and GIS methodology to find where your first location is most likely to succeed. When you have locations and revenue later, that history can graduate into a full custom model.

B2C and B2B alike: retail, restaurants, service businesses, manufacturing, you name it. If the decision is where to put a physical location and the stakes are high, the methodology applies. The model has evaluated locations across consumer retail, restaurant, service, and industrial settings, and it adapts to each brand because it is trained on that brand's own performance.

A complimentary field guide to the character and operating environment around a top-scoring site: who is there by day and by night, what clusters commercially, how people get around, with street-level photos and cited public data. The evaluation report is pure data; the Location Brief adds a dash of color for the humans reading it, so a place becomes more than a score. It describes the place. It does not predict revenue; that is the model's job, and the two are deliberately separate.

Yes. We have evaluated 2,342 locations over 15+ years of model building, with 124 verified builds and leases based on our reports. Our published Kansas City case study shows the model predicting a 32 percent revenue advantage for a site that every eyeball metric said should lose.

A decision-ready shortlist, not another dashboard: ranked candidate sites each with a revenue prediction score, the documented model logic and signal weights, cannibalization and coverage flags, and a clear next-site call your team and your CFO can act on.