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Markdown Optimization Potentials for a Discounter

Markdown Optimization Potentials

The Problem

Discounters are under enormous pressure from all sides. Whether it be retail chains or global e-commerce providers – the competition is fierce. The market environment is characterized by price competition and margin pressure. Consumers benefit from the fact that fast-moving goods are concentrated and pushed into the market in the shortest possible time. The price is the most important instrument for discounters to react to fluctuations in demand.

The aim of the project with a Europe-wide discount retailer was to develop optimized pricing algorithms and quantify the potential effects of demand-driven markdown optimization. Because the right price at the right time not only increases customer satisfaction, but ultimately also leads to an increase in sales and earnings.

The Solution

Together with the customer, we first recorded the initial situation, analyzed the current price management processes and developed a holistic target picture. Based on historical data, our data scientists analyzed the historical transcription behavior and uncovered correlations and patterns. Our behavior-based clustering procedure enabled us to determine the optimal number of article, store and country clusters and categorize them according to the relevant attributes. We then defined the future pricing strategies with the management team and subsequently developed the appropriate pricing algorithms. Influential factors such as availability, competitors’ prices, season, day of the week, time of day, weather and sales channel were examined for significance and depicted in a mathematical model. With these algorithms, extensive price simulations could subsequently be carried out, which revealed the real potential in terms of sales, earnings and sell-through ratio.

The Result

The retrospective analysis revealed considerable potential for optimization in markdown management:

  • 4.7% more revenue
  • 2.8% more yield
  • 11.2% better sell-through ratio

In the final business case, the expected ROI when using the Intelligent Price Advisor was calculated, thus creating a sound basis for decision-making at the management level. The management was convinced by the results and therefore decided to implement the pricing solution.

See also:

aifora Intelligent Price Automation

aifora Dynamic Pricing