An analyst, not a dashboard
The brief tells you what matters each morning. Every finding comes with its evidence, the calculation behind it and a recommended action — and the analysis pages hold every number behind it.
Three things you should know — before 9am
The first screen is a short brief, not a report: yesterday's numbers, the stores needing attention, inventory at risk, and the three most important findings with a recommended action for each.
- Picks one finding per area — stores, inventory, pricing — so the brief isn't three versions of the same problem
- Ranks by rupee impact and statistical strength
- KPI cards for every standard retail period, with like-for-like comparisons
- Each finding links to its full analysis and evidence
Eleven detectors that run after every sync
Each detector is a transparent statistical method. None of them is a black box, and thresholds can be tuned per organization.
Last 14 days vs a weekday-adjusted 8-week baseline, scaled by the chain trend; robust outliers only.
Top-selling SKUs out of stock at a store compared with peers, with estimated lost revenue.
SKUs projected to run out at several stores, using demand velocity that isn't hidden by lost sales.
Chain margin change split into category rate, mix and discount effects.
Stores growing faster than the chain while their margin falls.
Stores discounting far above peers; categories discounting far above their own history.
Categories growing or declining against the chain, confirmed by the weekly trajectory.
Sales per employee against peer stores.
Stock that isn't selling, valued at cost.
Regions diverging from the chain, and the stores driving it.
Channels whose return on ad spend has dropped materially.
Findings about the same store are merged into one — so a revenue gap caused by stockouts is reported once, with the stockout as the cause. Every finding can be acknowledged, resolved or dismissed, and expires automatically once it stops being true.
“Why am I seeing this?” on every finding
Open any insight to see the detector, the metric, the comparison, the period, the affected stores and products, each step of the calculation, and the underlying data.
- Observed facts are shown separately from likely explanations
- Calculations are shown with formulas and exact values
- Optional plain-English briefing, built only from that evidence
Why each store is up or down
Every store page ranks what drove the change — categories, transactions, inventory availability, discounting — and compares the store with the chain average, its region and its most similar stores.
- Revenue trend against the comparison period
- Category performance and top products
- Stock at risk in that store
- Revenue per salesperson
- 1.Watches−29.7%−₹4.11L
- 2.Footwear−21.7%−₹1.94L
- 3.Transactions−4.4%945 → 903
- 4.Inventory availability−4.0 pts96% of top 50 in stock
- 5.Discount rate+3.1 pts6.5% → 9.6%
Stop losing sales to empty shelves
The inventory command center shows days of supply, stockout risk and revenue at risk for every SKU in every store, and suggests transfers from stores holding surplus.
- Filters for critical, < 3 days, < 7 days, overstock and slow-moving
- Velocity measured before a stockout, so lost sales don't hide demand
- Transfers keep 21+ days of cover at the source and prefer the same region
- Product pages show sell-through, discount rate and velocity trend
| SKU | Store | On hand | Days of supply | Risk | Revenue at risk |
|---|---|---|---|---|---|
| SKU-0182 | Store 22 | 4 | 1.6 days | Critical | ₹1.1L |
| SKU-0182 | Store 3 | 6 | 1.7 days | Critical | ₹1.6L |
| SKU-0264 | Store 22 | 0 | 0 days | Out of stock | ₹1.9L |
| SKU-0182 | Store 26 | 8 | 2.6 days | Critical | ₹1.3L |
| SKU-0042 | Store 17 | 0 | 0 days | Out of stock | ₹0.9L |
Reports your leadership team will read
Daily Retail Brief, Weekly Management Review and Monthly Business Review — KPI summary, biggest wins and problems, inventory risks, store and product performance, and recommendations. Download as PDF.
Upload stores, products, sales and inventory. Column names are mapped automatically, dates in any common format.
A vendor-neutral data model. Shopify, Square, Lightspeed and database connectors are on the roadmap.
Owner, Admin, Analyst, Regional Manager and Store Manager — each sees exactly what they should.
See what your analyst finds in your data.
Book a 30-minute walkthrough, or explore the live demo with a full year of sample data from a 30-store chain.