Markdown optimization:
saving profit margin

The apparel retailer Intertop used Competera platform to maintain profit margin

Intertop manages 114 brick-and-mortar stores and 14 mono and multi - brand apparel chains across 25 cities in Eastern Europe. Launched in 1994, the apparel retailer offers over five million SKUs. In 2014, the company entered the e-commerce scene.
Sixteen brand managers are in charge of over 60 brands offered by the retailer.

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

«When using machine learning in repricing, we set business goals and constraints. Machines do the rest of the job. In other words, we do not do repricing per se, but we set the rules of the game and control the results. Technology does boost the financial performance of your company.

As technology takes up routine tasks, pricing managers need to boost their analytical skills and business thinking. We have to learn to see the big picture and make not tactical, but strategic decisions»

quote picture
Ilona Baskova
Ilona Baskova
Brand Manager at Intertop

Starting Intertop's journey with Competera

Markdown optimization
Regular prices optimization + New entry cold start problem
The most accurate price recommendations based on automatic feature extraction from texts and images
Scaling across
the whole assortment
Coherent pricing approach across the geography of presence

Challenge

Intertop used "blanket" discounts
  • 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

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 1

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.

The fine tuning
Stage 2

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.
The fine tuning
Stage 3

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.

Solution
The fine tuning

Results: Profit margin saving 200 b.p.

Both the test and control group offered the same average discount price, which means that the brand and price positioning remained intact.
profit margin
  • clarks
  • tommy hilfiger

The test group exceeded the control group by three parameters

increase sales revenue
  • Control Group
  • Test Group
  • Performance boost

The test group exceeded the control group by three parameters

increase profit
  • Control Group
  • Test Group
  • Performance boost

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A consumer electronics retailer maximized revenue without losing their margins

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