Overview
Storalytic provides an operating system for physical retail that captures, interprets, and acts on store data in real-time. The platform turns existing store cameras into a behavioral intelligence layer to track store performance metrics like customer attraction, service efficiency, and flow.
Key people
In the news
- Four weeks after a change, the platform reports one of two things. It worked, with the measured result. Or it did nothing, in those words, and that suggestion is not made again for that zone. Almost no product in this market will tell a customer that its own recommendation was worthless. The reason is simple: every one of them has promised uplift somewhere, and a verdict of no change contradicts their own case studies. We never made that promise. So the admission costs us nothing except the occasional uncomfortable report. It is
- Most before-and-after comparisons in retail are measured against a flat average. That is why they are almost always flattering. If the store was already climbing, the climb gets credited to whatever you happened to change. If it was already slipping, a change that genuinely helped looks like it did nothing. Storalytic measures every change against the trajectory the store was already on. What is reported is the part that sits above that line, not the part that was coming anyway. It is a harder number to produce and a smaller one
- The loop is four steps and it is the whole product. 1. The change is logged with a date. A display moved, a route opened, a department reorganised. 2. Equal windows. The same number of trading days after as before, so nothing is cherry-picked. 3. Measured against the trend the store was already on, so a rising market is not counted as your result. 4. A verdict, in euros where there are sales behind it and labelled an estimate where there are not. A store that knows which of its moves worked gets better every quarter. A store
- An online retailer changes something on a Tuesday and knows by Friday whether it worked. Keep what did, discard what did not, run it again next week. A physical store changes something and finds out nothing. The display moves, the route changes, the range shifts. Sales go up or down along with the weather, the market and a competitor, and somebody attributes the result confidently and without evidence. That is not a failure of judgement. The instrument never existed. Online was born with a record of every step of a visit, and that
- Online, every change gets tested. In the store, every change gets assumed. That gap is why we built Storalytic. And from today, retailers can find us on the Innovation Explorer of Retail Hub, the platform where retail leaders discover the technology for their next store. What you will find there: how a retailer tests a change in a store and knows the answer. Log what you changed, from a moved display to a full refit. We measure that zone before and after, using one rule for every change, and report the result in euros. Also when
- E-commerce built its entire funnel on the click. Every session, every conversion, every drop-off... all of it traces back to that one unit of measurement. Physical retail has had nothing equivalent. Until now. The Clear Lingerer is a validated moment of real attention in a commercial zone. Not a passer-by. Not a brief pause. A shopper who deliberately stopped, faced a product, and invested attention. That moment is where every sale either compounds or dissipates. Count Clear Lingerers, and the funnel becomes visible: reach ×
- Benchmarking against a distant sector ideal discourages. A goal that looks unreachable stops feeling like a goal. That is why Storalytic measures every lever against four gears, and why the active target is always the next gear up. First gear: your own best weeks. Already happened, under your own roof, so nothing about it is theoretical. Second gear: your best comparable zone. Proven inside the same store, by the same team. Third gear: the peer cohort. The 80th percentile of comparable stores. This is where cross-store data starts
- The three gauges shown in the Storalytic platform are not three piles of loss. They measure three different kinds of value and the distinction is deliberate. ATTRACT — grow. The commercial efficiency of the floor. Unconverted demand becomes euros within reach. This is where the actions and recommendations sit. SERVE — protect. Checkout service and queue time. Wait time versus target, and revenue at risk when service breaks down. SERVE owns the whole queue story, including understaffing, because a queue that forms is a
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