Increasing revenue
without 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

Challenge

The retailer exhausted all the traditional scaling approaches.

The company used to mimic competitors’ pricing and promo decisions.

Solution

Data-driven demand-based price recommendations to ensure financial growth.

Ensuring that only true competitors influence 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 Moyiseenko

Commercial 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.

Solution

Regular demand-driven recommendations for price 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) — inelastic products

The elasticity of price is less than -1 (closer to -∞ than 0) — elastic products

Stage 2: Calculating optimal price recommendations

When we increase prices on inelastic products, this leads to a slight decline in sales items (demand) which is less significant than the increase in prices percentage-wise. Thus, revenue grows.

When we decrease prices on an elastic product, this leads to a significant increase in sales items (demand), which compensates for the decline in prices. Thus, revenue grows.

Ultimately, if the coefficient of elasticity is calculated correctly, the retailer sees revenue growth both when prices go up and down.

What’s more, Competera’s algorithms calculate not only the elasticity of a particular product but its cross-elasticity with other items in the product portfolio. Let’s imagine that the cross-elasticity between product A and product B is high. In this case, Competera’s algorithms 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. If the price of product A goes up, while the price of product B remains the same, the sales items of product B will still go up because of its cross-elasticity with product A.