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Score vs. Outcome

Scoring predicts who looks promising. Sales dispositions record who actually was. The Score vs. Outcome view puts the two side by side, so you can see exactly where your scoring agreed with sales and where it missed.

Why This View Matters​

Every scoring model makes two kinds of mistakes:

  • False positives — leads that scored high but were rejected by sales. Your scoring is over-valuing something.
  • False negatives — leads that scored low but turned out to be qualified or won. Your scoring is under-valuing something.

A short list of each is the fastest way to find what your weights are getting wrong. Pair it with the calibration suggestions and you can fix the model with evidence instead of intuition.

Where to Find It​

Go to Configure > Scoring > Calibration Review. The Score vs. Outcome section appears on the same page as your weight suggestions, so the whole feedback loop lives in one place.

Each figure uses the score that was stamped on the lead at the moment its outcome was logged — not today's score. That keeps the comparison honest: it reflects what your team saw when they made the call.

What You'll See​

False Positives​

Leads that scored at or above your "high" threshold yet were marked Rejected (Not ICP) or Rejected (No Response). Each row shows the lead, its stamped score, who rejected it, and an excerpt of their note — often the clearest signal of why scoring was wrong (for example, "Student researching, not a buyer").

By default the high threshold matches your top engagement level. You can adjust it to widen or narrow the list.

False Negatives​

Leads that scored below your "low" threshold yet were marked Qualified (SQL) or Closed Won. These are the opportunities your scoring almost missed. The default low threshold is 40; adjust it to taste.

Score Distribution​

For each outcome, the spread of stamped scores — the lowest, the 25th percentile, the median, the 75th percentile, and the highest. If your "Qualified" leads cluster at high scores and your "Rejected (Not ICP)" leads cluster at low scores, your model is working. Overlap between them shows where scoring can't yet tell good from bad.

Time to Outcome​

For each outcome, the average and median number of days between a lead being assigned to an owner and an outcome being logged. This tells you how quickly reps are working leads and clearing their queue. Long times on high-value outcomes can mean leads are sitting too long before anyone acts.

Export to CSV​

Use the Export button to download the false-positive and false-negative lists as a CSV. A section column labels each row, so you can sort the two groups in a spreadsheet, share them with a rep, or work through them as a list.

How to Act on It​

  1. Start with false positives. Read the rejection notes. If the same reason recurs (wrong company size, wrong role, wrong region), a profile rule is probably over-weighted — or you need a disqualification rule.
  2. Then review false negatives. What did these winners do that scoring ignored? That behavior may deserve more weight, or a new rule.
  3. Cross-check with suggestions. The calibration suggestions often already point at the same rules. Apply the ones you agree with.
  4. Re-check next month. As you adjust weights and log more outcomes, the false-positive and false-negative lists should shrink and the score distributions should separate.

Notes​

  • Merged-away leads and sample (demo) leads are excluded from every figure.
  • The distribution can be restricted to your current scoring rules version, so old weights don't muddy the picture.
  • Time-to-outcome only counts leads where an owner was assigned before the outcome was logged.