Retail markdown optimization

The challenge

Peak’s customer, a leading UK multi-channel retailer, wanted to introduce AI into its markdown and pricing decision-making process. The retailer was seeking a way of identifying the optimal price to mark a product down to, helping to protect the product’s margin as they clear lines from the stock file.

The answer to achieving a “right price, first time” strategy lies in a retail business’ vast amounts of data. However, merchandising teams often find themselves entrenched in legacy processes and disparate business systems, with the bulk of their working day spent number-crunching in spreadsheets and relying on time-poor personnel and ‘gut feel.’ This is where AI, data and AI can play a crucial role in helping to make better, data-driven decisions that deliver tangible commercial outcomes.

The application

Peak’s Markdown application, was able to ingest and unify the retailers’ data from across the entire value chain, providing merchandisers with a much clearer and predictive view of demand. The application combined data sources such as website analytics (including search and behavioral data previously not surfaced to the merchandising team), sales data and ERP data, displaying them in an easy-to-use dashboard accessed by the merchandiser, which provided a holistic view of demand per SKU.

With this clearer, predictive view of the retailer’s data, Peak’s machine learning algorithms then produced an advisory “perfect price range” on an individual product level, based on a wide range of factors, helping merchandisers with their markdown decision making and ensuring that initial markdowns weren’t too severe.

The upshot

By applying Peak’s AI-powered pricing recommendations to a segment of its inventory across online and in store, the retailer has identified some hugely significant opportunities. Utilizing Peak’s price range suggestions on just 15% of the stock file, the merchandising team was able to identify an opportunity to optimize its markdowns to drive a saving of £2.4 million ($3 million.) To put this into perspective, this figure equates to additional margin worth approximately 1% of the retailer’s overall turnover. AI is also leading to increased team productivity and significant time savings, with AI effectively super-powering the end user’s output.