Lead Scoring

Lead Scoring is a framework for ranking leads by how likely they are to buy, using two dimensions:

1. Fit scoring (demographic/firmographic) – Who the lead is

• Factors: job title, seniority, department, company size, industry, revenue, tech stack, geography
• Question it answers: “Is this the type of company and person we sell to?”

1. Engagement scoring (behavioral) – What the lead has done

• Factors: website visits, key page views (e.g., pricing, product), content downloads, email opens/clicks, event/webinar attendance, product sign-ups, trial usage
• Question it answers: “Is this person actively researching a solution like ours?”

The combined score determines:

• Which leads are prioritized for sales
• How leads are routed (e.g., to SDR vs. AE vs. nurture)
• How urgently and with what sequence sales/marketing should follow up

Interpreting Fit × Engagement

• High Fit + High Engagement → Top priority, fast sales follow-up
• High Fit + Low Engagement → Good target, needs nurturing and activation
• Low Fit + High Engagement → Likely researcher, partner, student, or non-ICP; handle with lighter-touch nurture
• Low Fit + Low Engagement → Lowest priority; often deprioritized or left in passive nurture

Common Scoring Approaches

1. Point-based scoring

• Assign positive points for desirable attributes/actions (e.g., +20 for Director+ title, +15 for pricing page view)
• Assign negative points for disqualifiers (e.g., -30 for student email, -40 for non-target industry)
• Use thresholds to define lifecycle stages (e.g., MQL ≥ 100 points)

1. Grade-based scoring

• Separate Fit grade (A–D) and Engagement grade (1–4)
• Examples:
• A1: Ideal ICP and very active → immediate SDR/AE outreach
• B2: Good fit, moderate engagement → prioritized nurture + timely follow-up
• C3: Marginal fit, low engagement → nurture program only

1. Predictive scoring

• Uses machine learning trained on historical data (e.g., which leads became opportunities/customers)
• Outputs a likelihood-to-buy score or tier
• More accurate at scale but requires strong data volume, quality, and governance

Common Mistakes

• Scoring only on engagement without fit
• Result: Very active but poor-fit leads flood sales queues and waste rep time.
• No score decay over time
• A lead that engaged 12–18 months ago but has been inactive still appears hot.
• Fix: Implement time-based decay so old activity gradually loses weight.
• Too many MQLs (threshold too low)
• Sales gets overwhelmed and stops trusting MQLs.
• Fix: Raise thresholds, tighten ICP criteria, and validate against conversion rates.

RevOps’ Role

Revenue Operations (RevOps) typically owns:

• Designing and implementing the scoring model in the MAP/CRM (e.g., HubSpot, Marketo, Salesforce)
• Defining and adjusting fit and engagement criteria with Sales & Marketing
• Setting and recalibrating MQL/SQL thresholds based on real conversion data
• Adding decay logic, negative scoring, and disqualification rules
• Continuously validating the model against outcomes (MQL → SQL → Opportunity → Closed Won)

Effective lead scoring is a living system: it’s regularly reviewed, tested, and refined as ICP, product, and go-to-market motions evolve.