Pine59 + RetailNext: connecting the full retail journey

Alexei

You and I go back a long way. I remember what this pitch sounded like at Executive Forum, back when it was still just an idea and a few slides. Retail's changed a lot since then. What's changed most about the problem you're solving?

Thomas

Back then, location intelligence basically meant counting bodies. How many people visited and at what time. What's changed is that retailers stopped asking "how many" and started asking "who, and why." They want to know where a visitor came from, where else they shop, what their demographic profile looks like, and what competition is doing. The bar moved from measurement to explanation.

And honestly, retailers got more sophisticated faster than most vendors did; they'd already squeezed everything they could out of their own four walls and hit a ceiling. What they were missing wasn't more data. It was context.

Alexei

We built RetailNext on the idea that the store tells the whole story. Millions of data points, all from inside four walls. But I keep hearing the same question from retailers: the product's right, the staffing's right, so why is this location still underperforming? Why do they need to see what's happening outside the store too?

Thomas

Because in-store data tells you what happened, not why. You can see traffic dipped on a Tuesday, conversion slipped in Q2, one location lags another that looks identical on paper. The store can't tell you why, because the answer usually isn't in the store. It's who's in the trade area, where they're going instead, which competitor just opened two miles away, whether the whole catchment softened or just your part of it.

When a retailer tells me "the product's right, the staffing's right, and it's still underperforming," that's almost always a signal the explanation lives outside the store. You can't fix what you can't see, and half the picture is beyond the door.

Alexei

Give me a real example. What decision does a retailer make differently with this combined view that they couldn't make before?

Thomas

Take an underperforming store. The classic question is: do we fix it or close it? Without outside data, that's a gut call, and usually an expensive one. With the combined view, you can tell the two apart. If foot traffic in the trade area is healthy but your capture rate is low, that's an execution problem - merchandising, staffing, hours - and it's fixable.

If the whole catchment is drying up, people leaving the area, a competitor pulling them off, that's a location problem, and no amount of in-store tuning saves it. Same symptom, opposite decision. One keeps a store open and turns it around; the other stops you from pouring money into a location that was never going to work.

Or, say a retailer's deciding where to open next. Their instinct is to build near their best-performing stores, clone the winner. But their in-store data only tells them those stores convert well, not why, or whether that magic repeats somewhere new. With the combined view, they can see what actually makes a location work - the visitor profile in the trade area, how people move through it, and which nearby brands pull the same customer.

Then they can go find that pattern somewhere they don't have a store yet. Instead of guessing off last year's sales, they're matching real market DNA. That's the difference between expanding by hope and expanding by evidence, and it's the difference between a new store that ramps in months versus one that takes years, or never does.

Alexei

There's no shortage of companies that claim to do location intelligence. Most retailers I talk to are juggling two or three vendors already, and still missing the full picture. What's actually different here?

Thomas

Most retailers are stitching. One vendor for foot traffic, another for demographics, RetailNext for in-store, and a person whose real job becomes reconciling three datasets that don't agree. You lose time, you lose accuracy at every seam, and you still don't fully trust the answer. What's different is that this partnership between RetailNext and Pine59 is integrated.

Outside and inside sit in the same view, calibrated to the same stores, so nobody's exporting CSVs and eyeballing whether the numbers line up. The integration removes the seam, and the seam was where most of the error and most of the wasted time lived.

Alexei

Let's say a retail exec only has budget for one of these: in-store or beyond-the-store data. Not both. From your perspective, what do they lose by picking just one?

Thomas

Either way, you're flying with one eye closed. In-store alone, you see execution in perfect detail and miss the market entirely - you'll optimize a store that's underperforming for reasons that might have nothing to do with the store. With beyond-the-store data alone, you understand the market but can't see whether you're actually converting it, so you can't tell a demand problem from a delivery problem. Neither half is wrong. They're just incomplete in opposite directions.

The value was never in either dataset on its own. It's in the fact that one explains the other.

Alexei

Where does this go next? What does the retailer five years from now expect that they don't have today?

Thomas

Five years out, no retailer's going to accept the customer journey being split in two - outside data in one system, inside data in another, and a human trying to draw the line between them. They'll expect one continuous view, from the parking lot to the checkout counter, where what pulls someone toward a store and what happens once they're inside are read as a single story. That's the direction, and this integration is the first real piece of it.

We're connecting the two halves that have always been managed separately, and once retailers see the whole journey in one place, they won't want to go back to guessing at the gap.