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Scenarios & Recommendations lets you create what-if versions of your territories and receive AI-powered suggestions for optimising coverage. A scenario inherits accounts, scores, and data from a live territory - then you make changes, model different structures, and compare outcomes without affecting anything live. The platform generates recommendations based on scoring model outputs, rep capacity, geographic proximity, and other signals.

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Key Capabilities

Scenario Management

Create what-if scenarios from any live territory. Override account assignments, add or remove accounts, and review summaries showing changes vs. the base territory.

AI Recommendations

Generate AI-powered recommendations based on scores, capacity, and geography. Accept or decline individually or in bulk. Track recommendation acceptance rates and outcomes.

Planning Tools

Calculate geographic proximity between reps and accounts. Model different rep allocations and capacity constraints. Apply scenario changes back to live territories when ready.

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What Scenarios and Recommendations Do

Scenarios let you ask “what if” questions about your territory:
  • What if I changed the scoring model?
  • What if I adjusted the weights?
  • What if I added or removed accounts?
  • What if I carved the territory differently?
Recommendations are AI-generated suggestions for improving your territory - accounts to add, accounts to remove, or changes that would strengthen overall coverage. Together, they help you move from “here’s my territory” to “here’s the best version of my territory.”

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Common Workflows

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Running a What-If Scenario

1

Open Scenarios

Navigate to the Scenarios section for your territory.
2

Select a parameter to change

Choose what to adjust - scoring model, weights, account list, segmentation criteria.
3

Apply as a scenario

Apply the change as a scenario (not permanently).
4

Review projected impact

How do scores shift? Which accounts move up or down? Does the High/Medium/Low distribution improve?
5

Decide

If the scenario looks good, apply it permanently. If not, discard and try another.

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Reviewing AI Recommendations

1

Open Recommendations

Navigate to the Recommendations section.
2

Review AI suggestions

PG:AI analyses your territory and suggests improvements based on data patterns.
3

Understand recommendation types

Common types include:
  • Add accounts - companies that match your territory criteria but aren’t included
  • Remove accounts - companies that score consistently low and may not be a good fit
  • Rebalance - accounts that would perform better in a different territory or segment
  • Model adjustments - suggestions for tweaking scoring weights based on conversion patterns
4

Act on each recommendation

Accept, reject, or investigate further.

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Comparing Scenarios Side-by-Side

1

Create Scenario A

Apply one set of changes.
2

Create Scenario B

Apply a different set of changes.
3

Compare key metrics

Compare average score, score distribution, number of High accounts, coverage gaps.
4

Choose the best approach

Select the scenario that best aligns with your goals.

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Pre-QBR Territory Optimisation

1

Run a scenario

Create a scenario with your current scoring model.
2

Check recommendations

Review AI recommendations - are there obvious improvements?
3

Apply improvements

Accept any recommendations that make sense.
4

Present with confidence

Present the optimised territory with data backing your decisions.

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Understanding Recommendation Confidence

Recommendations may include a confidence level or strength indicator:
Always check the reasoning behind a recommendation. Understanding why PG:AI made the suggestion is more valuable than just following it.

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Tips and Best Practices

Use scenarios before major changes - before restructuring territories or changing scoring models, run a scenario first to see the projected impact.
Don’t accept all recommendations blindly - AI sees patterns in data; you have context it doesn’t (relationship history, pending deals, strategic accounts).
  • Iterate - the best territories come from multiple rounds of scenario testing and refinement.
  • Document decisions - when you apply a scenario or act on a recommendation, note why. This helps when reviewing performance later.
  • Review recommendations monthly - as data changes (new enrichment, new job postings, score updates), new recommendations will surface.

Scoring Models

Scoring model outputs are a primary input to AI recommendations.

Territory Management

Scenarios are built from and save back to live territories.

Territory Analytics

Scenario outcomes can be compared using analytics.

Strategic Insights Scoring

Insight scores feed the recommendations engine.