Glossary:

Lead Management & Qualification

Master the essential revenue and financial metrics that drive B2B SaaS success. From ARR and MRR to retention metrics and customer economics, these terms are critical for understanding pipeline health, forecasting growth, and making data-driven decisions.

Lead Scoring

Short Definition

A data-driven method that gives numerical scores to leads based on fit (demographics) and behavior (actions) to rank and prioritize sales-ready opportunities.

Definition

Lead scoring adds up points from two areas: fit scoring uses facts like job title, company size, and industry; behavior scoring tracks actions like website visits, email opens, or demo requests. Marketing calls leads MQLs at middle scores, while sales chases SQLs at high scores. This creates better handoffs and more deals closing.

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Teams use tools like HubSpot or Marketo to run scores automatically. Points drop over time if leads go quiet. Updates from won deals keep the system accurate.

Why Lead Scoring Matters

  • Sales reps save time by calling leads 3x more likely to buy.
  • Clear score rules stop fights between marketing and sales.
  • Scored leads bring in 2-3x more money than random ones.
  • Low scores go to cheap email nurture.
  • Data shows which traits make winners to improve targeting.

How to Create a Simple Lead Scoring Model

Follow these steps for a basic model:

  1. List Fit Points
    Check best customers. Give +30 for VP titles, +25 for big companies (500+ staff), +20 for right industry.

  2. Score Behaviors
    Demo requests get +50, webinars +30, three page views +15. Take away points for unsubscribes (-20).

  3. Pick Cutoffs
    MQL at 50-75 points; SQL at 80+. Use past data where 20%+ of MQLs turn into real chances.

  4. Add Up Scores
    Lead Score =     Fit Points + Behavior Points - Decay
    Example:     Sales Director (+30) at $50M company (+25) who asked for demo (+50) = 105 (SQL ready).

  5. Check Every 3 Months
    High scores should close 15%+. Change points based on new wins.

Example Scoring Model

Criterion Max Points Example
Job Title (Fit) +30 VP Sales: +30; Manager: +10
Company Revenue (Fit) +25 $50M+: +25; <$10M: -5
Demo Request (Behavior) +50 +50
Email Reply (Behavior) +25 +25
Total Score 130 max SQL ready

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Lead Scoring Thresholds

Score Range Lead Status Next Action
0-49 Unqualified Automated nurture
50-79 MQL Sales review
80+ SQL Direct outreach

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Key Metrics

  • MQL-to-SQL rate: Target 25-40%.
  • Close rate by score: Top scores close 15%+.
  • Coverage: Score 90%+ of leads.
  • Decay: Drop inactive 20% monthly.
  • Pipeline fill: Scored leads make 70%+ quota.

Common Mistakes

  • No negative points lets bad leads through.
  • Using too many rules (~20+) confuses everyone.
  • Fixed cutoffs miss busy seasons.
  • Marketing deciding alone causes misalignment with sales.
  • Not testing against real closed deals keeps you from validating the model.
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The Fix: Use 8-12 rules max, meet sales weekly, test cutoffs monthly.

Frequently Asked Questions

How is fit scoring different from behavior scoring?

Fit looks at unchanging facts like title; behavior watches changing actions like clicks. Use both for best results.

What tools are good for lead scoring?

HubSpot, Marketo, Pardot are popular options for marketing teams; sales teams tend to prefer Salesforce. Spreadsheets often work for small teams.

Should scores decay over time?

Yes, drop 5-10 points weekly with no action to focus on hot leads.

What's a good MQL-to-SQL conversion rate?

20-35% shows good scoring; under 10% means rebuild.

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