Markdown optimization: saving profit margin
The apparel retailer Intertop used Competera platform to maintain profit margin
Intertop used Competera’s platform for its summer sales 2019 campaign to hit three goals within a six-week market test:
- Liquidate excess inventory while keeping the gross profit and profit margin
- Test Competera pricing platform’s effectiveness
- Speed up repricing
Challenge
High pressure to clear off shelves fast while maintaining the gross profit and profit margin
Repricing takes too much time
Solution
Regular elasticity-based markdown suggestions
Analytics for well-informed pricing decisions available with one click
Results: Intertop reached all the set goals
The company is planning to scale Competera’s solution to optimize its offers for its upcoming collection.
200 b.p.
Profit margin saving
10.3%
Gross profit saving
15 min
Repricing time
Starting Intertop's journey with Competera
Challenge
-
Profit margin and gross profit margin losses
When crafting prices, managers do not consider demand elasticity and thus do not create optimal offers for every item.
-
Brand managers are overloaded with data
They need to analyze dozens of parameters, including business goals and KPIs.
-
Repricing takes hours
Managers need to monitor sales dynamics manually.
Solution
Tailored pricing and discounts allowed increasing gross profit and profit margin
The market test featured 420 lines of 4 brands: Timberland, Clarks, Geox, Tommy Hilfiger
Test group
Managers used elasticity - based markdown recommendations for weekly repricing cycles.
Control group
Managers continued pricing manually with the same regularity.
Competera factored in all of Intertop's business rules: thresholds, repricing steps, rounding rules. The platform analyzed millions of data points of historical data to craft markdown suggestions.
How it works — in simple terms
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.
Apparel-specific logic behind calculating optimal markdown recommendations
In many cases, apparel and footwear retailers launch “blanket” discounts to get rid of old inventory and free up space for a new collection by a certain date. This leads to huge losses in profit margin.
To avoid that, Competera’s algorithms take into account such constraints as stock (not to deepen discounts to a point when the demand goes higher than the number of products available) and the level of gross profit.
What’s more, Competera’s algorithms calculate such discounts that do not disrupt the sales of other products offered with a smaller discount or no discount at all. Also, demand forecasting spans over several weeks (as opposed to usual 7 days) to see if a certain product that is unlikely to sell with the deepest discount possible within a week will be sold out within four weeks or more, but always by a certain date.
Results: Profit margin saving 200 b.p.
The test group exceeded the control group by three parameters
- Control Group
- Test Group
- Performance boost
The test group exceeded the control group by three parameters
- Control Group
- Test Group
- Performance boost
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A consumer electronics retailer maximized revenue without losing their margins
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Competera Pricing Platform helps retailers to craft optimal offers
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