Markdown optimization for retailers

Hit the stock level and get maximum possible profit margin with discount depth differentiated on SKU-level, optimal markdown sequentions, and analytical prognoses on goal achievement.

Market Test

Single platform to reach every pricing goal

Whether you are a multi-brand, mono-brand, online or omnichannel retailer, Competera provides a set of solutions covering all types of pricing approaches.

Market-driven pricing

Use common pricing rules and customizable decision trees to get full control over pricing.

Markdown optimization

Liquidate excess inventory while maintaining the gross profit and profit margin.

Demand-based pricing

Identify cross-dependencies between products in portfolio to maximize the revenue.

Hybrid Pricing

Cover 100% of your product portfolio and get scalable results by combining various pricing approaches.

Cover all types of markdown use cases

Depending on the industry, retailer's type, and business objectives, Competera offers the best blend of tools to solve every markdown case.

Limited stock

Sell out all items before the new season begins (for apparel, footwear, and other industries dependant on seasonality)
Stock clearance

Clear excess inventory before the product end-of-life stage is reached (for all industries, e.g., electronics, beauty, etc)
Outlet stores

Liquidate old collections through outlet stores, factory outlets, and other clearance-specific stores (for all industries)

Stop losing money with traditional markdown

Traditional approach

Pressured to clear off shelves in due time, retailers shortfall in profit margin, undermine price perception, and compromise on overall financial health.

‘Blanket’ discounts
Diluted margin
Uncertain probability of hitting stocks

Markdown optimization

Using Competera platform, retailers set up and manage regular elasticity-based markdown campaigns with predictable and controllable results.

Discount differentiation at SKU-level
Maximized margin
Suggestions on sequential discounts and predictions on hitting stocks

What is under the hood of markdown optimization?

Competera’s RNN analyzes retailer’s historical sales data to recommend an optimal discount at an SKU-level so the targeted stock level is reached with a maximum margin rate.

Based on set parameters (max. promo depth, markdown’s time frames, expected stock level), the platform’s time-series based algorithm generates the prognoses on hitted the stock level and gained margin.

Data input

Historical sales (min 2 years)
Historical promo (min 2 years)
Promo calendar
Product description
Product stock availability

Execution

Suggesting sequential discount periods
Calculating cross elasticities and sales cannibalization effect
Differentiated approach instead of blanket discounts
Preventing profit margin from drop

How leaders win with Competera?

A leading Eastern European apparel retailer Intertop reached the point of 10.3% gross profit saving along with 200 b.p. of profit margin saving after only 6 weeks of using Competera.

"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"