Use Lead Scoring → Scoring Rules to control points, AI fit grading and qualification conditions. This requires authority to change the workspace’s scoring policy and agreement on what qualifies a lead. Wait for saved policy data before interpreting any controls or default values.
What you’ll see
Review and change rules
- Expand Tiers & rules. Use Tier ladder and Filter tier to find a rule. Select it, inspect its dimension and applicable profile, then adjust its points or Rule enabled state. The worked example explains that individual contribution, not a guaranteed final score.
- Review ICP AI grading. AI grading enabled, Most points AI grading can add, and Sub-score weights affect grading. Leave the advanced prompt, schema and formula version pins alone unless support directs a specific change.
- Open Thresholds & promotion. Review Preserve manual decisions and each stage’s Mode, Minimum score, Required fields, and Excluded tags. Manual, Score only, and Score plus required fields are different promotion policies.
- Under Custom rules, use Add rule for a supported condition and action. Review its label, dimension and enabled state; available actions include adding/subtracting points, setting dimension points and flagging for review.
- Use Save draft to persist changes. Review all pending scoring edits before Publish, and explicitly decide whether Rescore on publish should remain selected. Publishing is a consequential policy action.
Confirm and recover
Verify the saved draft or active version, then use a separately authorized representative-lead test to evaluate the intended behavior. A changed threshold alone does not prove historical leads were rescored. If saving fails, retain the error and inspect the loaded policy before retrying. A linked settings page saves separately from this draft.
Screenshots
The screenshot for this guide is shown above, under “What you’ll see”.