The Pocus Unlocking Revenue Podcast
Episode 6: Clearbit’s lead qualification engine - Scoring, buy-in, and data with Julie Beynon and Colin White
Mar 21, 2023
Clearbit's Julie Beynon and Colin White discuss the maturity and operationalization of Clearbit's lead qualification engine, the importance of buy-in from GTM teams, and their failsafe to prevent quality leads from slipping away. They also touch on lead qualification process, challenges in automation, the role of intuition in sales, and the technical aspects of their lead qualification engine.
35:59
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Quick takeaways
- Clearbit's lead qualification engine uses intent, behavioral data, and demographics to prioritize and distinguish between low-fit and high-fit leads.
- Continuous feedback and collaboration with the sales team are essential for refining lead qualification models and considering nuanced factors that can't be captured automatically.
Deep dives
Lead qualification system and framework
Julie and Colin have built a lead qualification system based on intent, behavioral data, and demographic information. The initial scoring model was created without historical data, relying on feedback from the sales team and conversion rates of different offers. They use MadKudu for machine learning-based lead scoring. The framework prioritizes key activities and distinguishes between low-fit and high-fit leads. They also have fail-safes to capture leads that may have been incorrectly scored. Continuous feedback and collaboration with the sales team inform adjustments to the scoring model and qualification criteria.
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