Matratzen Concord is an older company. And, as with most companies, the complexity of the IT infrastructure grows with size. When I started in May 2019, there were major difficulties in controlling product data.
Problem: Underutilized product data
However, a clean data structure is the lifeline for online activities. My task was to untangle the data and process it for individual channels with their respective requirements.
I needed a tool that would enable me to configure multiple channels from a raw data feed and manipulate individual data.
Solution: Product feed optimization and A/B testing
That's when I decided on using DataFeedWatch as our feed management solution for Matratzen Concord. It provided me with a centralized location to manage all product data and easily distribute it across multiple channels.
Product feed optimization
First, we harmonized the presentation of product data, which over the years had become inconsistent due to maintenance inaccuracies. This was quickly and easily possible via the internal fields of DataFeedWatch, allowing us to provide a uniform representation for all channels simultaneously.
Then, we adapted to the different requirements of the channels. We manipulated individual values to, for example, adjust the length requirements of titles depending on the channel.
The channel templates and feed reviews allowed us to confidently sell on multiple channels without worrying about products being rejected for not meeting each channel’s specific requirements.
A/B testing
Finally, we began conducting A/B tests to optimize performance. This can also be easily solved with the internal fields. Now, our data flow is clearly regulated.
All data runs unfiltered into a large raw data feed, and thanks to DataFeedWatch, we can distribute it as needed to all channels.
Results: 72% decrease in CPO
Successes quickly followed. Our CPO in Google Shopping is currently 72% lower compared to the period before DataFeedWatch. The digital area of Matratzen Concord has multiplied its revenues tenfold in the last four years, and clean product data has played a big part in this.
Conclusion
The implementation of DataFeedWatch has helped to streamline our product data management and allowed us to run A/B tests for performance optimization.
The significant reduction in our CPO and the tenfold revenue increase in our digital area bear testament to the transformative power of clean, well-managed product data.
I am looking forward to continued successful cooperation, as our next goal is internationalization and I am sure that DataFeedWatch will provide us with strong support.
