Clear Old Stock and Recover +3% of Previously Lost Margin
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.
Retailers lose money with traditional markdown campaigns
When the season is over, overstock of non-essential products becomes a challenge for many retailers. But clearing old stock with traditional markdown campaigns leads to regular money leakages.
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
No more black boxes: every recommendation is explained
Figure out the reasoning behind the optimal price recommendations.Competera interpretability features allow to:
- get insights on what was behind the Price Optimization engine’s decisions;
- check out how the set limitations have impacted the search range;
- find out what the demand elasticity curves look like;
- understand how the new price point impacts own product sales and what halo effect it has on other products in the category.
How retailers win with markdown optimization?
Find how a European apparel retailer reached the point of 10.3% gross profit saving along with 200 b.p. of profit margin and read the story of a large North American retailer generating a 3.4% gross margin uptick with Competera.
"It was important for us to base our pricing decisions on market trends, website analytics, and other crucial data points that a pricing manager can hardly embrace all at once. Competera’s smart algorithms made our price management data-powered and proactive and saved the team 50% time from routine tasks."
"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"
