Pricing managers update lists weekly across thousands of SKUs, eyeballing competitor prices, cost moves, and slow-movers. Mistakes leave margin on the table or break the floor.
The AI approach
Internal × external × elasticity. Join cost + sales velocity + competitor scrape; estimate elasticity; recommend a band that respects floors/ceilings; LLM narrates the rationale.
The outcome
~+1.4 pt margin
Faster repricing. Margin lift ~+1.4 pts in pilot. Manager spends time on outliers, not the long tail.
Try itInput → Process → Output
Input — internal + external
Internal · ERP + sales
SKU
cost
price
units/wk
days stock
CL-117
82
129
412
14
External · competitor scrape (3 sources)
competitor
their price
in-stock
scraped
Comp-A
125
✓
2h ago
Comp-B
119
✓
1h ago
Comp-C
132
low
3h ago
+ promo calendar · seasonality · price floors
Process — AI pipeline
1Estimate elasticityReadPredictive
2Solve under floor / ceilingRulesSymbolic
3Forecast units at new priceReadPredictive
4Narrate the rationaleWriteGenerative
Output — recommendation (manager approves)
Click Run demo to compute the recommended price band.
SKU CL-117 · suggested re-price weekly cycle
curr ฿129
rec ฿122
comp avg ฿125
floor ฿104ceiling ฿138
elasticity (last 12w)−1.8
competitor avg฿125
recommended price฿122+5.4% units → +1.7% margin
Why ฿122: Sit just under the cluster average to capture lift while staying above floor. Elasticity of −1.8 implies +5.4% unit lift covers the ฿7 cut. Margin still 33%; days-of-stock unchanged.