Identify the stores where you're most likely losing sales.
ROSA analyzes weekly store-level EPOS data to catch on-shelf availability (OSA) issues automatically. Instead of sending field teams everywhere, it helps you focus on the stores where action is likely to have the greatest impact.
Field teams shouldn't drive on intuition.
Traditional field teams rely on static schedules. Reps visit the same stores on a set cadence, while sudden out-of-stocks, misplaced tags, or phantom inventory go unnoticed elsewhere — until the weekly sales drop appears.
- ✕Fixed routesVisiting healthy stores while problem locations lose revenue.
- ✕National blind spotsSeeing a sales drop, but not knowing which store or SKU is blocked.
- ✕Wasted budgetSpending travel time and labour on stores that don't need help.
- ✓Data-driven priorityVisits are triggered only where an issue exists and pays back to fix.
- ✓Store-level precisionPinpoints specific stores and SKUs with unusual sales drops.
- ✓Targeted ROIDirects field resources solely to locations needing intervention.
How it works
ROSA turns your weekly EPOS data into a prioritised, verified field programme — automatically.
Analyse your sales data to detect issues
ROSA reviews your weekly store-level EPOS data and identifies stores with unusual sales behaviour.
Prioritise the stores that need attention most
Each store receives a score based on the likelihood of an availability issue, letting your team focus on the highest-priority locations first.
Resolve issues with targeted field execution
Tasks are automatically sent to our merchandisers or your own field team. Every visit is photo-verified, creating a continuous improvement loop.
Know where to act first.
What we can fix in store
We don't just detect issues — we fix them. Our merchandisers can act during standard hours, evenings, and weekends to tackle in-store issues when they matter most.
A real-life example
Based on 1,500 visits across a United Kingdom programme over nine weeks.
Frequently asked questions
Q What data does ROSA need to work?
ROSA works primarily with weekly store-level EPOS data — the sales data retailers provide to their suppliers. This is the most comprehensive and cost-efficient data source as it covers every store and SKU without requiring physical visits. Where retailer EPOS data is not available, ROSA can also run on crowd-collected in-store image recognition data.
Q How does ROSA identify out-of-stock situations from sales data?
ROSA analyses sales patterns at the individual store and SKU level, comparing each store's recent sales against its own historical baseline. A store showing significantly lower-than-expected sales for a given SKU — particularly a period of near-zero sales — is flagged as a probable availability failure. This anomaly detection approach surfaces issues that would not be visible from aggregate sell-in or panel data.
Q What happens after ROSA identifies a problem store?
Flagged stores are prioritised by the estimated revenue impact of the availability failure, and corrective tasks are automatically generated and sent to Roamler's merchandising teams or the brand's internal field team. Every visit is photo-verified, creating a closed loop from detection to resolution.
Q How is ROSA different from a standard retail analytics tool?
Standard analytics tools tell you that you have a problem — a sales drop visible in the data. ROSA goes further: it identifies which specific stores and SKUs are likely causing the problem, prioritises them by commercial impact, and deploys physical corrective action to fix it. It combines data detection with field execution in a single workflow.