Recover up to 6% of margin

Stop losing margin by shifting to portfolio-based AI pricing based on own price elasticities and cross-dependencies between products

Traditional pricing approaches lead to recurring margin losses

Eager to boost financial performance of particular products, managers rely on traditional SKU-centered pricing. It creates cannibalization effects which damage price perception and lead to strategic cash leakages in the long run.
“As long as we used an SKU-centered approach, we could have gained a sales boost for a single SKU but the related products immediately dropped in volume”.
Gustav, Product manager
at a large European footwear retailer
Traditional pricing approaches undermine sustainable profitable growth
Traditional pricing approaches undermine sustainable profitable growth

Portfolio-based AI pricing is the cure

Shifting from SKU-centered to portfolio-level pricing, businesses can balance sales and profits so the bottom line metrics grow sustainably.
To help retailers make the transformation, Competera offers a unique solution capable of taking into account all cross-product dependencies within a category to make sure every price recommendation is optimal in terms of the overall business performance. Such an approach enables the businesses to gain:
  • recovery of up to 6% of previously lost margin
  • increased customer lifetime value
  • up to 60% promo pressure reduction
  • increased average selling price / basket value
  • an average of 8% revenue rebound
Portfolio-based pricing is the cure

How portfolio-based AI pricing works?

Competera's portfolio pricing relies on a
demand-based pricing engine powered with neural networks measuring products own price elasticity and cross-elasticities to ensure that goals on both the product and the category level are achieved.

The algorithms can work on separate assortment groups, which allows parallelization and scalability. The accuracy of every recommendation is achieved through context-dependent price elasticities and a high-performance solver capable of shoveling through billions of possible price combinations to find the right one.

Data input

  • Transactions (min 2 years)
  • Promo calendars (min 2 years)
  • Price lists
  • Inventory
  • Product reference tables


  • Measuring the response of demand to price changes
  • Processing historical data to evaluate the impact of pricing and non-pricing factors
  • Selecting optimal prices for the whole category
  • Aligning goals on category and portfolio levels

No more black boxes: every recommendation is explained

Figure out the reasoning behind the optimal price recommendations.
Competera interpretability features allow to:

  • get insights on what was behind the Price Optimization engine’s decisions;
  • check out how the set limitations have impacted the search range;
  • find out what the demand elasticity curves look like;
  • understand how the new price point impacts own product sales and what halo effect it has on other products in the category.
No more black boxes

See advanced AI pricing software in action

Competera helped a large consumer electronics retailers with $500 million in annual turnover to improve profitability by recovering a 17.9% of gross margin
“Price wars continue becoming more intense and dangerous. And setting optimal prices has no alternative as a means of effective profit and revenue management. With price optimization, retailers can find the right balance needed to stay profitable and maximize margin"
author photo
Tatyana Moiseenko
Commercial director at Foxtrot
See advanced pricing software in action
See advanced pricing software in action

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