Behavioral data models replace static programmatic approaches with precision targeting at the exact moment of purchase intent
Yobi, the leading behavioral AI company, and Rain, the leading independent advertising agency in the United States, announced a partnership to give brands access to AI-powered personalization at the moment of purchase intent. Results show the approach is already outperforming both static segments and the industry’s leading custom algorithms – including up to an 81% reduction in cost per order across 1-day, 7-day and 14-day orders for a major online retailer. The collaboration arrives as traditional programmatic advertising hits a ceiling. For years, the industry has optimized for reach over relevance, struggling to identify the consumers actually ready to buy. Yobi and Rain are closing that gap, pairing Yobi’s proprietary behavioral models with Rain’s expertise to set a new standard for performance-driven advertising.
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“Most marketing technology has continued to focus on maximizing reach, even though reach doesn’t necessarily translate to purchases,” said Max Snow, CEO and Cofounder of Yobi. “Yobi’s behavioral AI changes that by learning from patterns across large populations using consented decision-making data. The result is marketing that can focus on real purchasing intent and the moments when consumers are most likely to make a decision.”
Through the partnership, Yobi and Rain have deployed a proprietary audience model designed to capture high-intent demand while maintaining efficient customer acquisition costs. By aligning media strategy, timing, and demand signals, Yobi and Rain help brands identify and target consumers with a high likelihood of converting within a short window, reaching them when purchase intent is at its peak. As part of this collaboration, activations will run across premium publisher inventory within Rain’s media framework, with dynamic optimization ensuring campaigns adapt in real time. Beyond performance, brands also gain a sharper analytics foundation by connecting campaign exposure and conversion data with net new consumer records – insights that extend well beyond the campaign itself.
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Closely partnered with Microsoft and built on Azure, Yobi has compiled one of the largest consented consumer datasets in the U.S. to train a proprietary foundation model on differentiated and non-public data. The data used to train the model is licensed, consented, and designed to give consumers choice and transparency around participation. At training time, deep learning AI models transform behavioral signals into vector embeddings that capture patterns of activity while preserving privacy, rather than storing raw, identity-level records. The dataset itself operates at a scale most audience platforms cannot match: 5.5 trillion transaction signals, 70 billion daily ad events, 5 billion search queries, 50 million active TVs, and consented first- and second-party data from banks, retailers, and telecom providers.
Validating a New Approach to Performance
“The biggest unlock in modern performance-minded marketing is knowing when demand actually exists,” said Kyle Knutsen, Director of Digital Video at Rain. “Yobi gives us a new layer of intelligence to identify those moments, and our job is to translate that into activation – placing the right creative in the right environments at the right time to drive real business outcomes.”
The model is already in-market across global brands in e-commerce and retail, delivering strong performance and measurable results. For one major retailer running a peak holiday campaign, the Yobi-Rain partnership drove an 81% reduction in cost-per-order on a 1-day window, 79% on 7-day, and 70% on 14-day-performance that more than offsets the premium of higher CPMs and reinforces that the strength of the audience model, not the size of the spend, is the ultimate driver. For a separate retail brand, the partnership is delivering a highly efficient CPA while outperforming a leading custom algorithm competitor in the category. Because Yobi’s models continue to learn throughout the flight, campaign performance compounds over time, a structural advantage over static targeting approaches.
