Prerequisites
Before setting up scoring models, ensure you have:
- Admin or RevOps access to your PG:AI organisation
- At least one territory created with accounts
- Recommended: Strategic insight criteria, job settings, and employee groups already configured (these provide the data that scoring rules evaluate)
Overview
Scoring models turn your territory data - job counts, team size, criteria scores, enrichment data - into one simple score per account, so you can focus on the best leads first. Navigate to Territories → Scoring Models in the left sidebar.Scoring models help you see at a glance how well each company fits your territory by turning their data (e.g. job counts, team size, criteria scores) into one simple score so you can focus on the best leads first.
Creating a Scoring Model
Configuring a Scoring Model
Score Band Thresholds
Score bands define how account scores are categorised into traffic-light labels. Configure three thresholds:| Band | Range | Badge Colour | Meaning |
|---|---|---|---|
| Negative | ≤ 40 (default) | Low | Account is a poor fit based on current data |
| Neutral | 41–59 (default) | Medium | Account has moderate signals |
| Positive | ≥ 60 (default) | High | Account is a strong fit |
Ruleset Weights
Rules define what data contributes to the score and how much weight each signal carries. Each rule has:- Rule name - describes what this rule evaluates (e.g. “Baseline rule for RevOps”)
- Type - the data source: Jobs, Employee groups, Insights, etc.
- Weight - a number (0–100) controlling how much this rule contributes to the total score. Shown as a slider bar.
| Rule | Type | Weight |
|---|---|---|
| Baseline rule for RevOps | Jobs | 3 |
| Baseline rule for Chief Revenue Officer | Jobs | 50 |
| Baseline rule for Enterprise Account Executive | Jobs | 16 |
| Baseline rule for Pipeline Generation | Employee group | (varies) |
Preview
The scoring model editor includes a Preview panel on the right side. This shows:- A list of accounts in the territory
- Each account’s PG:AI Score (the calculated score)
- The score badge (High, Medium, Low) colour-coded
- The account’s domain
Publishing a Scoring Model
A scoring model must be published to take effect. While in draft, you can adjust rules and thresholds without affecting live scores.Scores recalculate as underlying data changes.
Assigning a Scoring Model to a Territory
Understanding Scores in the Territory View
In the territory Companies tab, the PG:AI Score column shows:- The numerical score (e.g. 99.31, 88.24, 64.95, 50.00)
- A colour-coded badge: High (green/purple), Medium (yellow), Low (red/grey)
- A sentiment indicator: + Positive, Neutral, etc.
Common Configurations
Baseline model (quick start):- Let PG:AI generate a baseline model from your territory settings
- Review and adjust weights
- Good for getting started quickly
- Heavy weight on insight criteria scores (strategic fit)
- Moderate weight on jobs (hiring signals)
- Lower weight on employee groups (team composition)
- Good for prioritising strategic accounts
- Heavy weight on jobs and employee group growth
- Moderate weight on insight criteria
- Good for identifying accounts with momentum
- Create 2–3 models with different weighting strategies
- Assign each to a different territory (or clone territories)
- Compare which model best predicts success
Troubleshooting
| Issue | Likely Cause | Fix |
|---|---|---|
| All accounts score 50.00 | Model not published or no rules configured | Configure rules and publish the model |
| Scores don’t update after changing data | Model needs to recalculate | Trigger a recalculation or wait for scheduled refresh |
| Preview shows unexpected scores | Rule weights need adjustment | Review individual rule weights and adjust |
| Scoring model not available in territory settings | Model not yet created or in wrong state | Check Territories → Scoring Models for the model |
| High/Medium/Low badges seem wrong | Score band thresholds need adjustment | Adjust Negative/Neutral/Positive thresholds to match your score distribution |