Scowtt’s Predictive CRM Models Drive 38% ROAS Growth for Education

The Opportunity

The education’s long lead-to-application cycles exposed the limits of value-based bidding, which relied on a narrow set of deterministic CRM signals and was further constrained by 90-day ID expirations. To overcome these challenges, the education customer partnered with Scowtt to unlock CRM signals across all lead stages and incorporate counselor engagement, enabling predictive models that more accurately identified high-potential applicants and drove stronger enrollment outcomes.

The Approach / Google Products Utilized

Scowtt collaborated with a university to build a comprehensive ingestion framework for web and CRM signals. This included prospective student web engagement, application stages, counselor interactions, enrollment data, and micro-conversions, enabling accurate predictions of which prospects were most likely to apply and enroll.

Leveraging its proprietary sequential ML technology, Scowtt used clickstream data not only to boost prediction accuracy but also to generate both positive and negative signals for conversion uploads. Coupled with AI-powered lead scoring, automated offline conversion uploads into Google Ads for near real-time optimization, and A/B pilots against standard targeting, this approach unlocked a more complete and effective activation of AHR’s data.

The Result

Leveraging its proprietary sequential ML technology, Scowtt tuned CRM data to capture the multiple pathways of signals and micro-conversions. Within minutes, the system could identify prospective students likely to apply, assigning each lead a predicted conversion value weeks before they took any down-funnel actions. These predictive signals were then sent back to Google in real time, allowing its algorithms to learn continuously and providing 10x richer data than the deterministic uploads used in traditional Value-Based Bidding, eliminating long feedback cycles and enabling far more responsive optimization.