Yobi Delivers 30X Lift in Return on Ad Spend, while Reducing Media Costs by 83%
Increase of Echo Chambers and Filter Bubbles make it harder than ever to drive Brand Loyalty
The world’s largest brands are dealing with a new consumer reality. The rise of social media algorithms has created echo chambers and filter bubbles that make it hard to cut through the noise. Consumers are only exposed to ideas and information that aligns with their existing point of view. This leaves consumer brands with very little power to build strong relationships with their customers, as consumers are becoming incresingly selective in their brand choices, demanding seamless shopping experiences that reflect their own identities.
With most brands looking to develop tighter direct-to-consumer (DTC) channels, ensuring a smooth and consistent customer journey across all touchpoints, brand executives are being forced to invest into data-driven customer experiences and attribution. Incorporating AI and machine learning to deliver hyper-personalized experiences is easier said than done, espeically with the rise of data privacy laws.
Outside of Amazon, consumer brands have been stuck with the status quo of often inaccurate, stale, and single-dimensional third party data segments built with minimal privacy guidelines. Current solutions combine a mix of look-alike modeling, surveys, location, cookies, and demographics, but haven’t delivered on their promises of improved performance.
Yobi Provides Dynamic AI-Driven Audience Segments
Consumers are dynamic – audiences should be too. Yobi derives insight from consumer behavior and creates audiences for programmatic activation that are up to 5X more performant than traditional off-the-shelf audience segments found in most DSP data marketplaces.
“Yobi’s predictive shopper audiences delivered a 30X lift in return on ad spend, at 1/6th the cost of the same audience creation using traditional segments using Oracle, IRI, and Kantar/Numerator.”
VP of Direct-to-Consumer (DTC), Consumer Brand
Data Science Use Cases
1. Instant Personalization – provide a starting point for recommender systems (overcoming the cold start problem), by enriching your users’ profiles
2. Improve Ad Conversions – create more granular audience segments by understanding your users’ behavior, to deliver the right message, through the right channel, at the right time
3. Anticipate Churn – reactivate churned customers, and identify which segments share similar characteristics, by enhancing their user profiles
4. Prevent Fraud – identify which users might be more susceptible to fraud, and what normal behavior looks like, by creating a richer customer profile
5. Optimize Pricing & Demand Forecasting – leverage consumer behavioral signals from outside your ecosystem to provide insights on price sensitivity, competitive intelligence, and market trends