Why Quality Scoring Exists
Not all form submissions represent the same intent. A consumer who enters a real phone number, a matching ZIP code, and answers vertical-specific qualifiers in detail is far more likely to convert than one who fills in placeholder data to access content. Without a quality filter, buyers would receive — and pay for — leads that have zero chance of converting.
Our quality score consolidates 14 signals into a single 0–100 number so buyers can set a threshold and receive only the leads worth their attention. The score is calculated in real time, before the lead enters the ping-tree auction.
The 14 Scoring Signals
Contact Integrity (40 points)
- Phone number validity (10 pts): Is the number a real, dialable US number? We check format, carrier lookup, and known VoIP/burner number patterns.
- Email format and domain (8 pts): Valid format, real domain with MX records, not a known disposable email provider.
- Name completeness (7 pts): First and last name provided, not a single character or obvious placeholder ("test", "asdf").
- ZIP code consistency (8 pts): ZIP matches the declared city and state. A Miami ZIP code on a Jacksonville landing page is a negative signal.
- Field completion rate (7 pts): What percentage of optional fields did the consumer complete? Higher completion correlates with higher intent.
Behavioral Signals (30 points)
- Time on form (8 pts): Did the consumer spend a reasonable amount of time on the page? Submissions under 8 seconds suggest bot or auto-fill behavior.
- Scroll depth (7 pts): Did the consumer scroll through the research content before submitting? Content engagement predicts intent.
- Device and session consistency (8 pts): Is the session consistent with a real user? Bot fingerprinting, headless browser patterns, and VPN usage are negative signals.
- Return visitor (7 pts): A consumer who visits multiple pages or returns before submitting is higher intent than a first-visit, direct submission.
Vertical Qualifier Signals (30 points)
- Qualifier completion (12 pts): Did the consumer answer vertical-specific questions (loan amount, monthly electric bill, debt amount)? Skipping qualifiers is a significant negative signal.
- Qualifier plausibility (10 pts): Are the qualifier answers plausible? A $12 monthly electric bill on a solar form, or a $1,000 mortgage loan amount, are negative signals.
- Cross-field consistency (8 pts): Do the qualifiers make sense together? A declared first-time homebuyer with a 2003 purchase date is inconsistent.
Score Thresholds by Vertical
| Vertical | Network Floor | Typical Buyer Threshold | Premium Threshold |
|---|
| Insurance | 50/100 | 65/100 | 80/100 |
| Mortgage | 50/100 | 70/100 | 82/100 |
| Solar | 50/100 | 72/100 | 85/100 |
| Debt Relief | 50/100 | 68/100 | 78/100 |
Setting a higher threshold reduces volume but improves close rate. We recommend starting at the typical buyer threshold for your vertical and adjusting based on your first 30 days of conversion data.
What the Score Does Not Measure
The quality score does not predict whether the consumer will ultimately purchase. It measures intent signals at the moment of form submission — not downstream behavior. A consumer with a score of 90 may still not convert if your sales process, pricing, or speed-to-call is not competitive. Use the score as a filter for low-quality leads, not as a close-rate guarantee.