Optimise promotional pricing with AI

The problem

Promotions are a huge share of FMCG and retail spend, but many lose money once cannibalisation, pull-forward and funding are counted. Decisions are often based on last year's plan.

How AI helps

AI pricing platforms model price elasticity, cannibalisation and halo effects from your sales history, then recommend promo depth and mechanics and forecast the incremental profit.

How to roll it out

1. 1

   Gather 2+ years of sales, price and promo history by SKU and store or channel.

2. 2

   Agree the objective: volume, revenue or profit, and constraints such as retailer agreements.

3. 3

   Run the model on last year's promotions to see which ones actually made money.

4. 4

   Use recommendations for the next promo cycle and compare against a control.

5. 5

   Feed results back so the model improves.

Rough cost

None of the linked tools publish prices. Quote-only: Peak, Competera, Pricefx, Omnia Retail.

Tool	Lowest published plan	Free plan
Peak	See pricing	–
Competera	See pricing	–
PPricefx	See pricing	–
Omnia Retail	See pricing	–

Only prices published on vendor sites (checked 2026-10-20). Entry plans rarely cover full usage: AI features are often priced per resolution, credit or seat. Prices may display in USD outside the UK.