Lead Scoring Criteria
What to score, and how to weight it.
Real lead scoring criteria examples and ready-to-use rule templates for B2B SaaS, e-commerce, manufacturing, professional services, and real estate.
The four families of scoring criteria.
Every lead scoring model mixes these four categories. The art is picking the right signals for your business and assigning weights that reflect what actually predicts a sale.
What they did on your site
Behavioral
Who they are
Firmographic
What almost stopped them
Friction & Drop-off
Where they came from
Source & Channel
Rule templates by industry.
Copy, adapt, and deploy. Each template shows the criteria that matter most for that vertical, with example point values and qualification thresholds.
B2B SaaS
Trial-driven, long sales cycle
| Criteria | Points |
|---|---|
| Pricing page view (2+ times) | +25 |
| ROI / case-study page view | +20 |
| Trial or demo request started | +30 |
| Integration page view | +15 |
| Security / compliance page view | +15 |
| Return visit within 48h | +10 |
| Careers page view | −20 |
| Bounced in < 10s from pricing | −15 |
Qualification threshold: 70+ = sales-qualified; 40–69 = nurture; <40 = drip
SaaS buyers research heavily before trial. Weight pricing, ROI, and integration signals highest. Filter out job seekers early.
E-commerce
Transaction-driven, short cycle
| Criteria | Points |
|---|---|
| Product page view (3+ in session) | +20 |
| Add to cart | +30 |
| Checkout initiated | +40 |
| Coupon / discount page view | +10 |
| Return visit within 24h with same product | +15 |
| Abandoned cart | +25 (retarget trigger) |
| Bounced from product in < 5s | −10 |
| Refund policy page view | −5 |
Qualification threshold: 80+ = urgent retarget; 50–79 = email sequence; <50 = catalog
Speed matters. A cart abandoner who returns within 24 hours is hotter than a new visitor at checkout. Retarget fast.
Industrial / Manufacturing
RFQ-driven, enterprise deals
| Criteria | Points |
|---|---|
| Spec sheet / CAD download | +30 |
| RFQ form started or submitted | +40 |
| Capability / equipment page view | +20 |
| Case study in same vertical | +15 |
| Long session (> 5 min) on product pages | +15 |
| Return visit within 14 days | +10 |
| Careers / investor page view | −15 |
| Blog-only visit, no product pages | −5 |
Qualification threshold: 75+ = direct sales outreach; 45–74 = inside sales; <45 = marketing nurture
Industrial buyers download specs before contacting sales. A spec download is often worth more than a pricing view in this vertical.
Professional Services
Trust-driven, relationship sale
| Criteria | Points |
|---|---|
| Services / methodology page view | +20 |
| Team / about page view | +15 |
| Case study in same industry | +25 |
| Contact / consultation form started | +30 |
| Pricing / rate sheet view | +10 |
| Return visit within 7 days | +10 |
| Blog-only, no service pages | −10 |
| Short bounce from homepage | −10 |
Qualification threshold: 65+ = book a call; 35–64 = nurture with content; <35 = newsletter
Trust signals outweigh pricing in professional services. Case studies and team pages are strong predictors of consultation requests.
Real Estate
High ticket, emotional decision
| Criteria | Points |
|---|---|
| Property listing view (3+ in session) | +20 |
| Mortgage / financing calculator used | +25 |
| Neighborhood / school guide view | +15 |
| Schedule a tour click | +35 |
| Saved property / created alert | +20 |
| Return visit with same listing | +15 |
| Contact agent form abandoned | +10 (follow up fast) |
| Bounced from listing in < 15s | −10 |
Qualification threshold: 70+ = agent call within 1h; 40–69 = automated tour invite; <40 = market report
Real estate buyers research neighborhoods and financing before committing. The financing calculator is often the strongest intent signal.
When to use rules, when to use AI.
Rule-based scoring is a great starting point. It forces you to articulate what you think predicts a sale, and it surfaces disagreements between sales and marketing quickly. But it has a hard ceiling: it cannot discover signals you didn't think to include, and it goes stale as buyer behavior shifts.
AI scoring removes that ceiling. The model observes every micro-signal, learns which combinations predict revenue on your site, and retrains continuously as new conversions come in. You don't maintain weights; the model does.
Our recommendation: start with a rule template above to align your team. Then switch to an AI model like Catch before they bounce's within 30 days so the model can learn from real conversions instead of opinions.
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Lead scoring criteria — FAQ.
What are the most important lead scoring criteria?+
Behavioral criteria usually matter most: pricing-page visits, repeated sessions, scroll depth, form engagement, and return visits. Firmographic criteria (company size, industry, job title) help but only if the lead is already showing intent. Friction signals — rage clicks, form abandonment, exit intent — are underrated; they often separate buyers from browsers.
Should demographic or behavioral criteria weigh more?+
Behavioral criteria should weigh more in most models. A VP at a target account who bounces in 8 seconds is not a hot lead. A junior employee who visits pricing three times and returns the next day is. Demographics filter; behavior predicts.
How do lead scoring criteria differ by industry?+
B2B SaaS cares about pricing views, trial starts, and ROI-page engagement. E-commerce cares about product views, add-to-cart, and checkout initiation. Industrial/ manufacturing cares about spec-sheet downloads, RFQ form submissions, and long session duration. The underlying principle is the same: weight the actions that historically precede a purchase in your specific funnel.
How many criteria should a lead scoring model include?+
Start with 8–12 criteria. Too few and the model misses nuance; too many and it becomes brittle and hard to maintain. Focus on the signals you can actually capture reliably. Catch before they bounce's AI model monitors 30+ behavioral signals automatically and learns which ones predict revenue on your site — no manual rule maintenance needed.
How often should lead scoring criteria be reviewed?+
Manual rule-based models should be reviewed quarterly because buyer behavior shifts with seasons, product changes, and competitive moves. AI models retrain continuously and adapt without human intervention. If you're still tweaking point values by hand, schedule a monthly sanity check at minimum.
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