Customer education has long been measured by completions, satisfaction scores, and ticket deflection—but these metrics rarely translate into revenue impact or earn a seat in strategic conversations. As organizations push for stronger alignment between learning, product adoption, and growth, education teams must evolve from reporting activity to delivering insight.

In this session you'll be introduced to a practical framework for Education-Led Growth (ELG), where learning behavior becomes a measurable signal for retention, expansion, and customer health. At the center is the concept of the Education Qualified Lead (EQL)—a model that combines account fit with high-value learning behaviors to identify when a customer is primed for deeper adoption or expansion.

Participants will explore a critical—and often overlooked—insight: customers who demonstrate both breadth (engagement across multiple product areas) and depth (advanced, role-specific mastery) are significantly more “sticky,” showing stronger retention, higher product adoption, and greater expansion potential. Rather than focusing on volume-based metrics, this session reframes learning data as a predictive input into business outcomes.

By the end of the session, participants will leave with practical frameworks, templates, and a working model they can immediately apply to demonstrate how customer education doesn’t just support the business—it drives growth.

In this session you will learn:

• To define and operationalize Education Qualified Leads (EQLs) using behavioral and account data
• How to differentiate between low-intent activity and high-impact learning signals
• How to map learning behaviors to product adoption, retention, and expansion outcomes
• How to design a CE-driven component within a customer health scoring model
• To use breadth and depth of learning as predictive indicators of customer “stickiness”

It's an introductory topic that benefits from a little bit of experience. The discussion will center around building a structure that will work in the participant's own  environment.