Stop-rate tests for window and table displays in fashion and footwear chains
TL;DR Lingerly puts small anonymous counters at shop windows and feature tables in fashion and footwear stores. It measures how many passers-by stop and how many of those walk in, then compares display A with display B across matched stores. Visual merchandising teams pay per campaign to learn which display earns its keep.
Fashion chains in India are adding stores at a clip. One value-fashion chain went from 765 stores in FY25 to 963 in FY26, across 313 cities, and its parent group runs more than 1,100[1]. Every refresh of windows and feature tables, often monthly and around every festival, gets designed at head office and rolled out to every store with no way to tell which design actually worked.
Sales data doesn't answer that. A display's job is to make people stop and walk in, and a hundred other things move sales.
Eye-tracking research could in theory show what shoppers look at, but it is slow, lab-grade work. A 2021 supermarket study using mobile eye-tracking glasses followed just 19 shoppers and called its own results highly preliminary[2].
The seed wanted to track shopper attention on displays, and we changed that. Measuring gaze in a live store means wearable glasses or face-facing cameras, which would need clear consent from every shopper under the DPDP Act[3], and the evidence base is thin anyway. So Lingerly measures behaviour instead. Did a person slow down and stop in front of the display, and did they then come in?
A ceiling-mounted depth or thermal counter watches the zone in front of a window or feature table. It records counts and dwell times as numbers and stores no images. Head office runs two display designs in matched stores for two weeks, then swaps them. The report shows stop rate, walk-in rate and the change in sales of featured products, with an analyst's note on what else may have moved the numbers.
Lingerly sells campaign tests to visual merchandising teams, not always-on monitoring.
The buyer is the head of visual merchandising or marketing at a fashion, footwear or eyewear chain with 30 or more stores. Brands that pay a retailer for window space are a second payer. My working assumption is ₹1.5–3 L per test campaign across 8–12 stores, with the hardware staying put for the next test.
Store counts in value fashion are growing fast[1], so a better display rolled out across hundreds of stores is worth more every year. Shelf image-recognition tools have already got brand teams used to paying for in-store execution data[4]. And the DPDP Act makes face-based attention tools riskier, which suits a sensor that stores no images[3].
There is no public figure for spending on display testing in India. The narrow signal is the number of stores at chains that refresh displays centrally - 963 at one value-fashion chain and 1,100+ at its parent group[1].
That gives you a target list of buyers, not a market size. Treat any global retail analytics figure as adjacent at best.
One shelf-recognition vendor checks planogram and display compliance from photos taken by field reps, and sells a separate eye-tracking research product[4]. Another Bengaluru vendor offers shelf and video analytics[5]. A third Indian vendor sells camera-based footfall and dwell heatmaps to Indian retailers[6].
These tell you whether a display was installed or where people walked. The gap is a controlled A/B readout on whether a display made people stop.
| Difficulty | Moderate (42/100) |
| Starting capital | ₹50–90 L |
| Time to launch | 6–9 months |
Counting stops is easy in a quiet mall corridor and a pain on a crowded high street with hawkers and two-wheelers in view. Getting landlords and store teams to allow ceiling sensors at the entrance is slow. Pulling the display effect apart from weather, sale days and staff behaviour also takes careful test design.
No sector licence, but the hardware and the shopper data both need care.
Building this outside India? The categories above usually have a local equivalent, so check with your own regulator.
Don't let AI identify, profile or recognise shoppers. The whole product depends on counting people without knowing who they are.
Want to work out the AI layer for Lingerly? Talk to Yash →
Here's what can go south -
I'd pick this for a founder from visual merchandising or retail marketing who can turn results into design advice. A pure hardware team will struggle. First prove that in two test campaigns the better display lifts stop rate by at least 15%, and that the chain changes its next rollout because of it.
Plan for roughly ₹50–90 L, mostly on sensors, installation and the first analysts. Running paid pilots with one chain first keeps hardware spend in check.
No sector licence. You need WPC type approval for wireless sensors, BIS registration for listed cameras or adaptors, and DPDP compliance, which is why Lingerly stores no images.
Yes, fashion chains everywhere test windows. Check local radio equipment rules and data protection law on in-store sensors before you install.
The free Lingerly validation kit has the line-by-line capital breakdown, the effort scoring, the radio and data-protection checklist, a 14-day validation sprint with pass/fail targets, a 90-day launch plan, 10 interview questions for merchandising teams, and 5 assumptions to test. Download the kit →
Thinking about building Lingerly or something close to it? The make-or-break work is getting clean stop counts at busy store fronts and turning them into design advice, and that's exactly the kind of thing I help founders with. If you need a hand, you know where to find me. Talk to Yash →
For illustration only. Names are fictional and ideas hypothetical; sourced facts are real but may be out of date. Nothing here is legal, financial or professional advice, and Yash Khatri is not personally liable for any use of it. Read the full disclaimer.