Bonprix Sets Prices With AI-based Price Optimization

IN Price Optimization — 29 November, 2017

In a complex and changing market, retailers need to transform their internal processes to meet the challenges of rising costs and falling profits. Tackling this issue head-on, bonprix decided to use price optimization software to improve its margins. And, with data key to remaining competitive, the company also wanted to gain valuable insights via machine learning to optimise its pricing processes even further.


Using artificial intelligence (AI) to boost sales

Prior to the implementation of Blue Yonder’s machine learning Price Optimization solution, bonprix Germany was responsible for agreeing prices across the entire international market. Each market followed a price-conversion table to set prices and calculate mark-ups or mark-downs manually. However, in Russia, pricing plays a much more significant role in customer buying decisions than it does in Germany, and it was difficult to reflect this using this method. Also, it was all but impossible to rapidly react to changes in the different markets and adjust prices automatically for each product and store.

The solution

Following discussions with Blue Yonder, the retailer decided to move away from rigid price-conversion tables to an automated AI-based solution. In 2016, bonprix implemented Blue Yonder Price Optimization in its key international markets. The goal: optimized and flexible pricing decisions for every product, while improving revenue and profit. Allowing for specific price controls for different markets and product ranges, the AI-based technology is now allowing bonprix to set prices across its international markets, automatically.

The dynamic pricing solution was first implemented in the Netherlands and then rolled out internationally. bonprix recently applied the AI-based solution to all of its women’s, men’s and children’s collections in Russia. The solution was then further developed to allow bonprix to focus on the automatic pricing of individual collections.

Stock and replenishment levels are a factor in the price setting. This allows the price suggestion to consider what stock levels are at the warehouse and what is available. While the base price continues to be set by the retailer’s purchasing department, intra-seasonal price setting is now done automatically with no need for manual intervention.

Using machine learning to optimize results

With Blue Yonder Price Optimization software, bonprix has achieved higher sales across its various markets, and increased its overall results. Indeed, the mail-order retailer has seen a positive difference when it comes to all KPIs set out in its business strategy. In Russia, bonprix was struggling to meet its targets when it came to reaching new customers. But today, the Blue Yonder solution has allowed bonprix to make adjustments for every collection and differentiate prices to attract new customers. Russia subsequently saw an increase in the number of items sold and an increase in revenues.

What’s more, AB tests in the Russian market have proved that the Blue Yonder algorithms improve themselves over time, without any manual intervention. As a result, while Price Optimization provided a measurable impact and return on investment in a short period, results are likely to improve even further.

Ultimately, Price Optimization allows bonprix to set prices individually and specific to each market, while increasing its influence in the international market with consistent and improved earnings.

“With Blue Yonder, we can now simply and centrally adjust prices from day to day in individual markets. That was not previously possible at this speed.”

Florian Rüffer, Project Manager, bonprix

Beneficial side effect: Improved data quality

By working with Blue Yonder, bonprix has gained access to a treasure trove of data. The purchasing department now has the best prepared, structured and highest quality data in the company.

Beyond the initial pilot project, this data can be analyzed for other ways to make improvements to the company’s bottom line, such as calculating cross-price elasticity to determine what effect a price change on one product has on others. Other correlations can also be determined, such as price-related consumer behavior. This lets bonprix align its offerings even closer with customer expectations.

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