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AI Case Study

Ernsting’s Family optimises pricing decisions while improving revenue and margins with machine learning

German retailer Ernsting’s Family is implementing machine learning based price optimisation technology by Blue Yonder. With the solution that retailer sets prices for all its products, across channels to tackle the short collection cycles and high pressure to sell times quickly that it is facing. As a result, it has managed to optimise markdown management while increasing its revenue and margins.


Consumer Goods And Services

Retail General

Project Overview

"During the five-month pilot phase, the Blue Yonder technology enabled the sale of selected items within a defined period of days. By doing so, Ernsting’s family achieved its goals, and with noticeably higher profits and margins on the test collection.

The pilot project included 50 clothing items from collections for women and children. Between July and November 2017, Ernsting’s family used Blue Yonder’s solution to set the prices for this collection in 50 test shops in Germany. That was compared with 50 shops that were not using the solution. The result was faster sales in the test group. Blue Yonder analysed an extensive number of indicators, including stock levels, sales, promotions, climate and weather conditions, as well as the product information for each article.

The retailer will roll-out the solution across all 1,800 stores in Germany and Austria in April 2018, as well as online for a clothing collection. Over the next three years, the solution will be implemented on its entire digital collection.

The price optimisation solution promises to deliver the best price reduction for each individual product – by size and colour, automatically. It takes into account the sales goals of each store in relation to demand and stock levels when setting prices."

Reported Results

Ernsting's family claims the following results: (Blue Yonder case study)

* 90% of an item sold within a defined period of time
* Increase in sales and margins


"Price optimization is based on machine learning instead of preset rules or hypotheses" (Blue Yonder)





"Ernsting’s family challenge was that it was facing extremely short collection cycles and therefore high pressure to sell items quickly.
Ernsting’s family was previously challenged by the short life cycle and seasonality of its range of products. Each of its stores receives products from the 12 monthly collections every two days. That means products have to sell as fast as possible."



current and historical sales data, product master data

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