Kevel, a leading API-first ad serving company, has unveiled its latest innovation in the form of Kai (Kevel Artificial Intelligence), a comprehensive suite of AI and machine learning technologies designed to revolutionize the retail media landscape. This new offering, integrated into Kevel’s Retail Media Cloud™ platform, promises to enhance performance optimization, drive relevancy, and boost profitability for retail media networks.
Kai introduces two groundbreaking features: Forecast and Custom Relevancy. Forecast utilizes advanced machine learning simulations to predict inventory and campaign performance for both current and future ad campaigns. Unlike traditional forecasting tools that rely solely on historical data, Forecast considers all contextual and user audience targeting parameters, as well as pacing factors, to provide accurate projections of future campaign performance.
Custom Relevancy, the second major feature, allows retailers to incorporate their proprietary AI/ML algorithms into Kevel’s Ad Server. This ‘bring your own model’ approach enables retailers to leverage their unique insights and advanced models to determine ad relevance, tailoring the ad stack to their specific network performance needs.
The development of Kai was spearheaded by Kevel’s elite AI/ML research group, led by CTO Tim Ewald and featuring industry experts such as Paul DeGrandis, Richard Carter, and Paulo Cunha. Their collective expertise in artificial intelligence has culminated in this powerful suite of features designed to elevate the ad serving and audience segmentation capabilities for retail media.
Paulo Cunha, Retail Media Cloud GM at Kevel, emphasized the uniqueness of Forecast, stating, ‘Forecast is a first of its kind for retail media. […] This way, advertisers always know exactly what their future performance looks like and retailers can maximize their inventory yield.’ This level of predictive accuracy represents a significant advancement in campaign planning and optimization for the retail media sector.
Tim Ewald, CTO at Kevel, highlighted the transformative potential of Custom Relevancy: ‘Retailers know their customers better than anyone else, but struggle to influence their ad serving with the exceptional AI-driven optimization they use for promoting a customized user experience. That all changes with Custom Relevancy, which allows customers to plug their own ML models into our ad decision process to dynamically adjust relevancy and improve ad serving a per-user basis.’
Kai also incorporates existing Kevel features such as ad decisioning and pacing, which utilize a combination of historical data, user behavior, context, and relevancy scoring to drive ad performance. This holistic approach to ad serving demonstrates Kevel’s commitment to providing a comprehensive solution for retail media networks.
The introduction of Kai comes at a time when the retail media landscape is rapidly evolving, with more companies seeking to build their own ad platforms to compete with tech giants like Amazon. Kevel’s innovative approach positions them as a key player in enabling retailers, marketplaces, and e-commerce companies to launch sophisticated ad formats and targeting segments both online and in-store, complete with closed-loop attribution.
As the retail media sector continues to grow, solutions like Kai are poised to play a crucial role in helping retailers maximize their ad inventory and provide more value to advertisers. The ability to offer highly targeted, relevant ads based on advanced AI and machine learning models could significantly impact the effectiveness of retail media campaigns, potentially reshaping the digital advertising landscape for retailers and brands alike.
With Kai, Kevel is not only addressing current market needs but also laying the groundwork for future innovations in retail media technology. As Richard Carter, Principal Data Scientist at Kevel, noted, ‘KAI is just the start of many more innovative, unique features in our pipeline.’ This forward-thinking approach suggests that Kevel will continue to be at the forefront of retail media technology, driving advancements that could redefine how retailers monetize their digital assets and engage with advertisers.
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