ON-DEMAND
Turn Your ICP Targets Into Pipeline
How to build lead scoring models for high-converting campaigns
In this on-demand webinar, accelant Account Strategist, Laura Conway walks you through how to design, implement, and optimize lead scoring models in HubSpot using both fit and intent data.
In this webinar you'll learn:
Strategy
Design a scalable lead scoring strategy aligned to your ABM objectives
Implementation
Build and deploy HubSpot scoring using ICP fit and intent-driven rules
Analysis
Track how qualified leads convert and where accounts drop off in funnel
Optimization
Improve scoring with AI insights and performance-based feedback
About the speaker
Laura Conway
Laura Conway is an Account Strategist at accelant and a seasoned marketing leader with over a decade of experience in HubSpot.
She brings deep strategic expertise from working with mid-market organizations across financial services, technology and SaaS, and energy distribution. Laura specializes in helping businesses align marketing and sales through smart automation, lifecycle strategy, and data-driven execution.
FAQ
Q: What is predictive lead scoring?
Predictive lead scoring uses data patterns from past conversions to estimate which leads are most likely to become customers. It helps prioritize outreach beyond simple rules-based points.
Q: What’s the difference between a lead and an MQL?
A lead is any contact in your database. An MQL is a lead that meets your qualification criteria (typically fit + intent) and is ready for a sales follow-up process.
Q: How do you build an effective lead scoring model from scratch?
Define your ICP and what “qualified” means, then score both fit and intent and validate it against real conversion outcomes. Start simple and refine over time.
Q: What is the purpose of lead scoring?
Lead scoring helps your team focus on the right prospects and route leads consistently. It improves efficiency, follow-up timing, and pipeline conversion.
Q: How is lead scoring calculated?
Most models assign points for fit (who they are) and intent (what they do), then use thresholds to trigger statuses like MQL. The best scoring is tested and adjusted using performance data.