How SCHOENEN TORFS achieved staggering results by embracing advanced analytics for end-of-season discounts or markdown pricing.

A Markdown Pricing Challenge

Schoenen Torfs is a well-established player in the Belgian retail industry and perceived as a frontrunner by many.

Having regularly won prizes as a 'great place to work', the company is eager to embrace innovation but not without losing track of the human factor. Yet, it is not blind for any challenges the organization faces when it comes to profitability.

That such profitability is under pressure is no secret. It is a clear-cut given for any shoe retailer having to cope with new market entrants of the likes of Zalando & Amazon.

Deciding on end-of-season discounts (markdown pricing) was performed using a traditional process, that proved ample opportunity for improvement using advanced analytics.

Torfs invited us to challenge its current way of performing end-of-season discounting or so called 'markdown pricing'.

Yet, with one caveat. We needed to prove the business case of working with advanced analytics to improve its summer sales season results.

A Controlled Experiment

That is why during the 2020 Summer Sales, we set up a controlled experiment to prove that our algorithm-driven toolkit could outperform a human decision-maker setting end-of-season discounts.

The solution we deployed is a toolkit that uses sales data & AI techniques to suggest the most optimal markdown, week-over-week, for each item. It uses a clear objective and a customized algorithm that predicts price markdown elasticity estimates to maximize sales season turnover.

The tool can be used manually or can run fully automated and comes with a set of dashboards that enable the team to observe and understand what is happening.

The test case demonstrated that products for which this algorithm controlled the price had a 30% higher revenue on average, creating an 8% margin increase!

How Successful Was It?

During the 2020 summer sales, the test case demonstrated that products for which this algorithm controlled the price had a 30% higher revenue on average, creating an 8% margin increase.

The client was, of course, pleasantly surprised by the achieved results in an experimental setup at a reasonable price point. Moreover, the fact that we went from kick-off to go-live in under three weeks was a much applauded given. The fact that we were able to prove the business case, and demonstrate that the impact on the team is limited, opened the door for further collaboration.

The team at Torfs, therefore, decided to proceed with a full implementation of the setup.

Download the detailed client case here!

Learn more about how we demonstrated the impact advanced analytics in markdown pricing can have on a retailers' sales season margin & revenue. Find all the details in this extended client case.