TL;DR:
- Magnite introduces machine learning-driven recommendations for A/B testing in Demand Manager.
- The tool utilizes Prebid technology to enhance revenue growth for publishers.
- Machine learning offers automated Prebid optimization based on data analysis.
- Initial tests demonstrate an 80% increase in revenue for machine-generated settings.
- Publishers can easily integrate machine-generated settings into A/B tests.
- Industry experts, including LADbible and REA, endorse the innovation.
Main AI News:
Magnite, the eminent independent sell-side advertising powerhouse, has recently unveiled a groundbreaking addition to its Demand Manager product. This dynamic tool harnesses the power of machine learning to provide invaluable recommendations for A/B testing, all underpinned by the robust Prebid technology.
Demand Manager has been pivotal in arming publishers with the essential tools, insights, and connections to thrive in the ever-evolving landscape of ad exchanges, formats, and vendors. With the integration of machine learning, this product now takes a monumental leap forward, promising to elevate publishers’ revenues in this dynamic and competitive marketplace.
The newly introduced feature employs machine learning to deliver automated Prebid optimization suggestions, drawing from Prebid and ad server auction data, as well as session data. The ultimate objective? A substantial boost in revenue. What sets this apart is the simplicity it offers to publishers – with just a single click, they can activate machine-generated settings for an A/B test. The initial results of this innovation are nothing short of impressive, with a staggering 80 percent of wrappers that engaged in a machine-generated experiment experiencing a notable upswing in revenue compared to their existing settings.
Matt Tengler, the VP of Product at Magnite, explains, “Publishers are confronted with a seemingly endless number of daily choices that materially impact revenue. We developed this new feature to eliminate Prebid optimization guesswork while still giving publishers full control. Infusing A/B testing with machine learning makes it easy for publishers to measure revenue and page performance improvements. This continues to bring publishers innovative tools that focus on revenue and efficiency at the same time.”
The endorsement of this innovation extends beyond just the creators themselves. Ben Elshaw, Director of Operations at LADbible, attests to its effectiveness, stating, “We were excited to test this new Demand Manager feature to see how machine learning could improve our wrapper configurations. We were pleased to see a material rCPM increase following the implementation of the optimization recommendations. Demand Manager’s A/B testing functionality combined with machine learning recommendations is a welcome innovation that we hope to see expand in the future.”
Lewis Lee, Senior Ad Tech Specialist at REA, echoes this sentiment, saying, “After using Demand Manager’s new machine learning-driven recommendations for wrapper optimization, we were pleased to see an immediate increase in revenue. The A/B testing capabilities allow us to customize our wrapper configurations based on data and quickly test more scenarios while minimizing risk. We look forward to testing additional features that utilize machine learning to help us improve our Prebid settings.“
Conclusion:
Magnite’s integration of machine learning into A/B testing through Demand Manager represents a significant advancement for publishers in the ever-competitive advertising market. This innovative tool simplifies optimization, enhances revenue potential, and offers greater flexibility for publishers to adapt to the dynamic landscape of ad exchanges and formats. It demonstrates Magnite’s commitment to providing publishers with the tools they need to thrive in an industry where data-driven decisions are paramount.
