NEW RESEARCH79% of shoppers say the same chain is noticeably better or worse store to store.Get the full report, free →

New research: why the same chain wins in one store and leaks in another.Get the report →

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Partners · Data and POS platforms

Show your customers how and why each initiative performed, store by store, inside the software they already use.

Your customers already use your platform to see what their stores sold. Seurat adds a read on how each change actually contributed to sales: each store vs. its twin, with a confidence interval and a verdict. The read can sit inside the AI assistant your customers already use, so "did the new LTO pay off?" gets a store-by-store answer, and the reasons behind it, instead of a sales chart.

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A still from the twin film: Store 27's weekly sales vs. its twin, split into the changes behind them: +$2,600 from a new pizza launch, −$900 from a new competitor, −$400 from a snack aisle reset and −$150 from an energy drink deal.

What your customers get today

  • Dashboards and AI assistants report what happened: sales were up 4% the month the promotion ran.
  • They can't isolate the effect of the promotion from everything else happening at the same time, or tell how sales would have moved without it: weather, gas prices and the season move sales every week.
  • So the questions that matter, like what drove a result, where it worked and whether it will repeat, go to a consultant or a spreadsheet outside your platform.

What a read adds

  • Each store vs. its twin: what the store sold vs. what it would have sold without the change.
  • A verdict for every store: pays for itself, no significant effect or costs money, with a confidence interval.
  • From the chain down to each store: a flat result opens up to show where the change worked and where it didn't.
  • What drove each result: what the change took from other items, what sold with it, and whether customers came back.
  • No new data to collect: the read uses the weekly sales by store your platform already holds.

How it works inside your platform

  • Your customer asks a question in your platform or its AI assistant: did the new LTO sandwich pay off, and at which stores?
  • The read runs on the sales already in your platform. It can run on your own servers, so your customers' data doesn't have to leave them.
  • The answer comes back inside your platform: the chain's result, each store's result, and what to do next.

What you get

  • An answer your customers can't get from a standard dashboard or today's AI assistants.
  • Something new to offer every customer, built on data you already hold.

Talk to us about building it in

We'll show you a read on sample data first, then on one of your customers' chains, with their permission.

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