Cosmetics & Personal Care Retail Merchandising Cloud Software for Vietnamese Business

The audit depth depends entirely on the channel. Inside a chain beauty bay, the full checklist applies: shade and variant completeness, tester condition, gondola end-cap presence, and whether a beauty advisor is staffing the counter. A supermarket or minimart shelf drops the advisor and end-cap checks and looks only at facings and any secondary display. At a tạp hóa stocking a handful of everyday personal-care lines, there's no display to assess, so the check is a straightforward availability read. Behind the scenes, each outlet class pulls its own brief automatically, and the resulting scores still roll up together by region and channel.

Each gondola or gifting-display window is set up as a task, with its own start date, agreed location, and a POSM list attached. A merchandiser photographs it three times over the window: on install, partway through, and at dismantle, with GPS attached to each shot. Against that timeline, AI checks the branding and SKU presence match the brief and raises an exception the moment a display fails to go up, gets shifted, or gifting sets and testers run dry in the pre-Tết build-up.

Your own SKU library trains the model, everything from small cream jars and roll-on deodorants through to shampoo and conditioner bottles, so it recognises what's actually on the shelf rather than a generic catalogue. A crowded minimart display, glass-counter glare at a chain bay, and the lighting swing between a supermarket aisle and a dim tạp hóa shelf are all conditions it's built to work through. When confidence drops on a photo, a person reviews it instead of the system guessing, and each retraining pass folds in new launches and Tết packaging so accuracy doesn't stall for weeks at a time.

Going into a buyer or distributor meeting, a key-account manager pulls together shade-tray and variant completeness, tester condition, gondola end-cap occupancy, and how the brand's share of shelf compares to its rivals, all in one scorecard. Because every number traces back to a GPS-stamped photo, a chain or distributor trying to claim credit for a bay that was never actually serviced runs straight into evidence that already sits on file.

Every cosmetics or personal-care line carries a batch code and expiry date, and that pair gets scanned or keyed in whenever a merchandiser runs an audit. From there the platform cross-checks against its batch master, catching creams, sunscreens and haircare packs that are drifting toward expiry so they get pulled before write-off becomes the only option. Stock sitting on a shelf with no visible batch code gets flagged too, giving a brand something concrete to push back with when counterfeit or grey-market goods turn up through informal channels.

Each bay's shade tray, whether it's foundation, lipstick, powder, nail polish, hair colour or a skincare variant, gets set up as a SKU-level check, and a merchandiser photographs and taps out whatever's missing during the visit. Tester condition works the same way: a cracked compact or an empty sampler gets logged and photographed on the spot. AI then reads that photo for tray and variant completeness, and once shades fall below threshold or testers keep going missing over consecutive visits, an exception fires automatically, a pattern that tends to surface fastest in the busiest bays around Ho Chi Minh City and Hanoi.

A stock-out gets logged right at SKU and outlet level, in the moment, during the visit itself. Behind that log, the system works out why: shoppers cleared it out, someone lost track of it, or the distributor is behind on a replacement. Because a dashboard breaks daily availability down by chain, region and category, a key-account manager doesn't have to wait for a weekly report to push the issue to a distributor or buyer, which is exactly the speed that matters when a new launch or the Tết gifting season is underway.

Every GPS-stamped photo gets scanned by AI for two things at once: your own SKUs and whatever competitor packs are sitting nearby. That detection feeds share of shelf, calculated as facings per brand against the category total, and the moment an unfamiliar competitor SKU shows up, it gets flagged exactly like a stock-out would. Because the trend gets reported by region, a brand can catch a rival skincare or haircare launch picking up space in the South well before the same pattern shows up in the North, rather than finding out only after the category has already moved on to its next launch.

The outlet brief itself carries the planogram: shade-tray layout, tester placement and end-cap spec for that particular chain account. On every visit, a merchandiser checks the shelf against it, and AI reads the bay photo to confirm facings and shade order line up with what was contracted. Any drift from the planogram opens an exception on its own, photo evidence attached, so the field team and the key-account manager are working from the same picture instead of a phone call.

Three different views come off the same data. Trade marketing tracks how campaigns are rolling out: deployment rate on POSM, the audit pass rate, and whether flagged actions actually get closed. For their meetings with chains and distributors, key-account managers pull up the relevant scorecards directly. Leadership works from the top: a national execution score, its breakdown by region and channel, where competitors are gaining or losing share of shelf, and how the end-cap and gifting-display install programme is tracking. Anyone can drill from the country number down to a region, an outlet, or a single photo, and 1Voice AI will answer a plain-language question about any of it along the way.