Lead Scoring
Lead Scoring assigns points to prospects based on characteristics and behaviors that indicate buying likelihood. A prospect who visits pricing pages, attends a demo, and engages with email content accumulates points. Higher scores = warmer prospects who deserve sales attention first.
What Is Lead Scoring?
Lead scoring quantifies lead quality. Rather than treating all leads equally, scoring helps sales teams prioritize time. A scored lead might receive 100 points for a demo attendance, 50 for a pricing page visit, 25 for an email open. Prospects exceeding a threshold (e.g., 300 points) route to sales; others go to nurture. Plura tracks conversation engagement as a leading scoring indicator: detailed questions, objection handling, and timeline mentions all increase lead score.
Demographic vs. Behavioral Scoring
Best practices combine both:
- Demographic Scoring: Company size, industry, location (fit with your ideal customer)
- Behavioral Scoring: Website visits, email engagement, demo attendance, demo length (buying signal)
- Together they reveal readiness: High demographic + low behavioral = wrong company; Low demographic + high behavioral = poor timing
Why Lead Scoring Matters
Without scoring, sales wastes time on low-quality prospects while missing warm leads. Scoring ensures the hottest prospects get sales attention immediately. Even a 10% improvement in lead quality can increase conversion rates by 20-30% because sales spends less time on tire-kickers.
FAQs related to
Lead Scoring
What's a good lead score threshold?
Depends on your scoring system. If max score is 1,000, threshold might be 300-400. If max is 100, threshold might be 60-70. Start with your best customers' scores and make that your threshold.
How do I determine point values?
Start simple. Assign points based on conversion correlation: If demo attendees convert at 40% but email openers convert at 5%, demos get more points. Refine over time based on actual conversion data.
Should I automatically route high-score leads to sales?
Yes, but add a human check. A lead could score high on bots (fake engagement) or wrong timing. Sales should review before outreach, but high-scoring leads should be contacted within hours.
What if someone scores high but doesn't convert?
Your scoring model is wrong. Investigate: Is this prospect an outlier, or is your model systematically incorrect? Use actual conversion data to recalibrate point values for different activities.
Can I use AI for lead scoring?
Yes. Machine learning models that analyze thousands of leads identify patterns humans miss. Over time, AI scoring becomes more accurate than manual models.