Price Optimization Software for a 2-5% net margin boost

Price Optimization Software

ML-driven price recommendations solution for retailers to boost profit margins by 5% and increase sales by 15% using demand predictions.

Journey to Price Optimization

We believe that modern Machine Learning technologies could power humans with better decisions

Expert made decisions
The better the experts, the better the decisions
Expert + BI
Retail analytics software helped to make insights more accessible
Expert + BI + Automation
Human-supervised automation ensured faster decisions
Competera + ML + Goals
Machine Learning guided by business goals leads to better results

How Our Retail Pricing Software Works

Our pricing tool powered by a dense neural network combines strategy, business rules, ML, and data to recommend optimal prices and forecast the effect on the demand. The model guarantees 90-98% short-term prediction accuracy.

1 Add history of 2+ years data sources

- Sales transactions
- Stocks
- Assortment info
- Repricing activities

2 Define business targets

Choose the right repricing model to increase sales volumes or revenue.

3 Define business constraints

To control the repricing outcomes, add multiple business rules - from maximum number of repriced products, groups of products locked for repricing to costs & MAP.

4 Get optimal prices

Apply price recommendations to reach your business targets.

Don’t take our word for granted! Your journey with Competera:

Try a 60-day Market Test

Your journey with Competera:

  • 1 Data pocessing
  • 2 Market test definition
  • 3 Models training & simulation
  • 4 Market test launch
  • 5 Scale & Evolve

Try a 60-day Market Test:

Don’t take our word for granted!

Competera Сustomers

Foxtrot Jumia Deloitte Ulmart Rde Staples Mothercare

View all

How our price optimization models worked out

“Pricing races are growing non-stop. Setting the optimal prices is the key instrument to manage retail profits efficiently. The main question is what a reasonable price range and adjustment to increase sales and keep the margin optimal are.”

Commercial Director, Foxtrot

Foxtrot, an omnichannel Eastern European consumer electronics retailer with $500 million in annual turnover, wanted to stop copying competitors’ pricing moves and maximize revenue without risking profit margins.

Competera helped Foxtrot to:
- Maximize revenue without losing profit margins
- Save time for negotiations with suppliers
- Switch from reactive to proactive pricing

Machine Learning Solution Solves Real-world Retail Problems

Sell stocks

Actions

Optimize prices while taking into account the profit margin of your entire product portfolio

Results

- Loyal customers are focused on high-margin products
- The profitability of your product portfolio remains unchanged

React to the rise of purchasing prices

Actions

Apply a differentiated approach to pricing. Run several repricing cycles with uneven price ranges to reach KPIs gradually

Results

- Retain profitability for your entire product portfolio
- Maintain revenue

Boost sales of fast moving products

Actions

Do not change prices, or raise prices for part of your assortment to improve the price perception of chosen products

Results

- Fast-moving products that consistently generate revenue

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