Advanced Retargeting Techniques Boost Conversions For This E-Commerce Brand

Baur Versand is an e-commerce company based in Burgkunstadt, Germany. The company was established in 1925 by a shoe merchant, Friedrich Baur, it later became a part of the Otto Group in 1997.

Baur Versand began life as a mail order company, and grew to become one of the largest in Germany. The Baur Versand team had the foresight to embrace digitalization in 1999, and is continuing to work to cement its reputation as an e-commerce leader.

Baur Versand operates in a competitive e-commerce environment, and the company had already adopted best-practice approaches including data driven attribution, audience segmentation, and product category splits. However, the Baur Versand team wanted to find novel ways to further improve upon their marketing strategy. Specifically, the company wanted to find ways to improve its marketing performance while maintaining the same level of Return On Ad Spend (ROAS). To achieve this, Baur Versand worked with RTB House to integrate additional signals, based on internal data, into a Deep Learning powered retargeting campaign.

The first step was to work closely with the client’s marketing team to establish clear goals, and to integrate their additional data into our retargeting solution. We also worked to improve the fidelity of Baur Versand’s product ranking system, and integrate it into our already successful retargeting methodology.

Deep Learning enabled the RTB House team to determine what creatives to show to users, where to show them, and at what moment it would have the greatest impact. The data provided by Baur Versand enabled us to further tailor this solution to the company’s unique needs.

Instead of simply showing a user a product they had already viewed, the algorithm would check the product ranking for similar offers in the same category. If any of these had a higher product ranking than the one previously viewed by the user, this more valuable product would be shown instead, maximizing the ROI for Baur Versand.

This holistic approach demonstrates the flexibility of the Deep Learning solutions RTB House uses, and the value of combining multiple data sources together, making them more than the sum of their parts.

The approach proved to be successful. By using product score to carefully retarget high-value product lines, and match them with the appropriate user, RTB House was able to drive an uplift in conversion rate of 33%, without compromising ROAS.

Additionally, this approach enabled us to increase the overall likelihood of a user converting once they clicked on an ad. This, in turn, boosted the average value of each order by 20%, making every ad view significantly more valuable than previous methods were able to achieve.