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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

1

Navigate to Scoring Models

Go to Territories → Scoring Models in the left sidebar.
2

Add new scoring model

Click + New Scoring Model (top-right button).
3

Name the model

Enter a Criteria Name (the scoring model name) and optionally a Description.
4

Save or configure

Click Save or proceed to configure rules and thresholds.
PG:AI can also auto-generate a Baseline Scoring Model based on your territory settings (jobs, employee groups, insight criteria). This is the fastest way to get started.

Configuring a Scoring Model

Score Band Thresholds

Score bands define how account scores are categorised into traffic-light labels. Configure three thresholds:
Adjust these thresholds based on your scoring distribution. If most accounts cluster around 50, consider tightening the bands.

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.
Example ruleset from a baseline model: To edit rules, click Edit Rules to open the rules editor. You can add, remove, and modify individual rules.

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
Use the preview to validate your scoring model before publishing. Click Reset to recalculate scores.

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.
1

Configure rules and thresholds

Set up your rules and score band thresholds.
2

Validate in preview

Review the preview to validate scoring looks right.
3

Publish

Click Save to publish the model. Published models score all accounts in the assigned territory automatically.
Scores recalculate as underlying data changes.

Assigning a Scoring Model to a Territory

1

Open territory settings

Open the territory and go to Territory Settings (gear icon).
2

Select Scoring Model tab

Select the Scoring Model tab.
3

Choose a model

Choose the scoring model to assign. Once assigned, the territory’s Companies view shows the PG:AI Score column with scores from this model.

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.
Accounts are scored from 0–100. The badge thresholds correspond to your Score Band Thresholds.

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
ICP-weighted model:
  • 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
Activity-weighted model:
  • Heavy weight on jobs and employee group growth
  • Moderate weight on insight criteria
  • Good for identifying accounts with momentum
Multi-model comparison:
  • Create 2–3 models with different weighting strategies
  • Assign each to a different territory (or clone territories)
  • Compare which model best predicts success

Troubleshooting