Challenge
The retailer exhausted all the traditional scaling approaches. The company used to mimic competitors' pricing and promo decisions.
Increasing revenuewithout losing profit margin
The consumer electronics retailer Foxtrot used Competera's platform to optimize pricing
Foxtrot is a major omnichannel consumer electronics retailer. Started in 1994, the company is a member of Euronics International, an international association of over 14,000 independent electrical retailers in 36 countries. Foxtrot attracts 27.8 million customers annually.
Foxtrot used Competera's platform to hit three goals within a six-week market test:
- To maximize revenue without losing profit margin
- To stop mimicking the pricing moves of competitors
- To prove the feasibility of Competera's solution
Solution
Data-driven demand-based price recommendations to ensure financial growth and stop competitor-following pricing decisions.
Results: Foxtrot hit all the set goals
Pricing managers have switched from routine to more strategic tasks, while the retailer boosted its financial performance.
+13.6%
Revenue
+51.5%
Profit margin
Saved as compared to the control group+5.8%
Sales items
+7.8%
Avg.Transaction Value
You set the targets and Competera's algorithms recommend how to reach the uplifts.
Market test- Protect margin
- Maximize Revenue
- Reduce costs
- Win market share
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.
Tatyana MoyiseenkoCommercial Director at Foxtrot
Challenge
- Profit margin losses
Pricing managers lack time and data to factor in demand elasticity to set optimal prices for every product.
- Bulky and time-consuming pricing
The in-house ERP and Excel-based pricing systems have technical limitations to store and process pricing data.
- No single database of previous pricing decisions
Pricing managers have no means to analyze and repeat the success of past pricing and promo decisions.
The market test featured two groups:
Test group
Managers used demand-based price and promo recommendations.
Control group
Managers used traditional manual pricing methods
Competera factored in all of Foxtrot's business constraints, analyzed millions of data points of historical data, and considered the demand elasticity of every product to create optimal price and promo recommendations regularly at the portfolio level.
How it works — in simple terms
The process of calculating and suggesting optimal prices for every product under management is based on taking into account price elasticity.
Stage 1: Defining the elasticity of demand coefficients
Competera's algorithms preserve the information about elasticity coefficients of products obtained during the training stage which precedes the market test.
The elasticity of price is greater than -1 (closer to 0 than -1) means inelastic products.
The elasticity of price is less than -1 (closer to minus infinity than 0) means elastic products.
Stage 2: Calculating optimal price recommendations
When we increase prices on inelastic products, this leads to a slight decline in sales items that is less significant than the increase in price percentage-wise. Thus, revenue grows.
When we decrease prices on an elastic product, this leads to a significant increase in demand, which compensates for the decline in price. Thus, revenue grows.
If the coefficient of elasticity is calculated correctly, the retailer sees revenue growth both when prices go up and down.
Competera's algorithms calculate not only the elasticity of a particular product but its cross-elasticity with other items in the product portfolio. If the cross-elasticity between product A and product B is high, Competera can suggest increasing prices on product A to hit two birds with one stone:
- to boost product A's sales items and contribute to increasing the retailer's revenue;
- to increase product B's sales items while keeping its price unchanged thanks to cross-elasticity with product A.
Results: Foxtrot hit all the set goals
Control GroupTest GroupPerformance boost
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