Scoring
Scoring is how Apex turns raw visitor behavior into a prioritized list of your best leads. Every contact gets a numeric score (0–100) and a quality bucket — hot, warm, cool, or cold — so you know who deserves attention right now.
Two Scoring Tracks
Apex supports two scoring approaches, and your conversion model determines which one applies:
| Track | Best for | What it measures |
|---|---|---|
| MQL (Marketing Qualified Lead) | Sales-assisted businesses | Intent signals, ICP fit, account quality, engagement depth, risk factors |
| PQL (Product Qualified Lead) | Self-serve / PLG businesses | Activation progress, product usage, expansion signals, retention behavior |
| Dual | Hybrid models | 40% MQL + 60% PQL composite score |
MQL Scoring
MQL scoring evaluates five dimensions:
- Intent — Are they looking at high-intent pages like pricing or demo requests?
- ICP Fit — Do they match your ideal customer profile (company email, role, industry)?
- Account — Is the account itself a good fit (domain quality, company size signals)?
- Engagement — How deeply are they engaging (page count, session duration, return visits)?
- Risk — Negative signals like disposable email addresses or bot-like behavior.
Each dimension has a configurable weight (all five must total 100). The weighted scores combine into the final 0–100 total.
PQL Scoring
PQL scoring evaluates four product-usage dimensions:
- Activation — Has the user completed key setup steps or reached an activation milestone?
- Usage — How frequently and deeply are they using the product?
- Expansion — Are they exploring advanced features or inviting team members?
- Retention — Are they coming back consistently over time?
Dual Scoring
For hybrid businesses where both marketing signals and product usage matter, dual scoring blends both tracks: 40% MQL + 60% PQL. Each contact gets separate MQL and PQL breakdowns plus the composite total.
Quality Buckets
Every score maps to a quality bucket based on configurable thresholds:
| Bucket | Default threshold | Meaning |
|---|---|---|
| Hot | 75+ | Ready for outreach or upsell |
| Warm | 55–74 | Showing strong interest, nurture actively |
| Cool | 35–54 | Early-stage interest, keep engaging |
| Cold | Below 35 | Low engagement, not yet qualified |
These thresholds are adjustable in the scoring models configuration.
Scoring Rules
MQL scoring uses a rule engine where each rule defines:
- A condition — what to look for (URL match, UTM parameter, event type, page count, session duration, form field, or custom data)
- An operator — how to evaluate (equals, contains, greater than, regex, exists)
- Points — how many points to award (or deduct, for risk factors)
- A dimension — which of the five dimensions this rule affects
Rules are evaluated against each visitor's event data. Points accumulate per dimension, get normalized, then weighted to produce the final score.
Tip
Apex provides default scoring templates tuned for your vertical. You can use the defaults as-is or customize every rule and weight.
How Scores Are Used
Scores appear throughout Apex:
- Contacts page — Sort and filter contacts by quality bucket. Hot leads float to the top.
- Journey view — Each contact's timeline shows their current score and bucket.
- Experiment analysis — See how experiments affect lead quality, not just conversion rates.
- Actuators — Trigger webhooks or Slack notifications when scoring thresholds change.
- Causal Ledger — Scoring model changes are recorded on the timeline so you can correlate them with metric shifts.
Next Steps
- Configure scoring models to customize weights, rules, and thresholds
- Understand conversion models to see how your business type affects scoring
- View the contacts page to see scores in action