Real retail problems.
Measurable outcomes.
Illustrative scenarios modeled on the operational patterns Data Archos targets — not specific named engagements.
Manual, weekly reporting across disconnected POS terminals meant stockouts and margin erosion on private-label SKUs surfaced days after impact.
- Unified 11-stage ingestion pipeline across every POS terminal
- Automated anomaly alerts for stockout risk
- Weekly demand forecast pushed directly to store ops
A fragmented catalog across small-format stores made it hard to tell which SKUs earned their shelf space, so pricing and assortment calls lagged actual demand by weeks.
- Elasticity-based pricing signals down to the store level
- Store-to-store inventory rebalancing to cut excess
- Action Center alerts flagging underperforming SKUs early
Promotions were evaluated quarterly in spreadsheets, making it impossible to tell which campaigns drove incremental revenue versus cannibalized full-price sales.
- Veronica natural-language analytics for the marketing team
- SKU-level promotion ROI scoring
- Cross-store benchmarking on seasonal assortment
Seasonal demand swings caused chronic overstock in slow regions and stockouts in high-velocity stores, with no shared visibility between merchandising and store ops.
- 4-week SKU-level demand forecasting
- Store-to-store inventory rebalancing signals
- Automated Action Center alerts for regional buyers
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