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Documentation Index

Fetch the complete documentation index at: https://hc.pillargtm.com/llms.txt

Use this file to discover all available pages before exploring further.

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Playbook Evolution

The Playbook Evolution engine analyzes historical play execution data to measure which play templates work best for which account profiles, then generates data-driven recommendations for future plays. It extends the Plays system with a closed-loop feedback mechanism: plays are executed, outcomes are recorded, effectiveness is computed, and better plays are recommended.
Playbook Evolution requires Mandatory Play Outcomes to be enabled. Without outcome data, the engine cannot compute effectiveness metrics.

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How It Works

Plays + Outcomes + Account Context
  → Effectiveness analysis (win rate by template x segment x tier)
  → Segment affinity scoring (which plays work best for which profiles)
  → Recommendation generation (best play for this account + signal)
  → Ranked recommendations (confidence x expected win rate)

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Play Effectiveness

The engine groups completed plays by template, segment, and tier, then computes:
MetricDescription
Total RunsNumber of times this play template was executed for this segment/tier
Successful RunsRuns with positive outcomes (Renewed, Won, Expanded, Saved, Upsold)
Win RateSuccessful runs / total runs
Avg Time to CompleteAverage days from play start to completion
Avg ARR ImpactAverage ARR of accounts where this play was executed
Avg Health DeltaEstimated health score change from play execution
Confidence LevelBased on sample size: high, medium, or low

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Positive Outcomes

The following play outcomes are classified as positive:
  • RENEWED
  • WON
  • EXPANDED
  • SAVED
  • UPSOLD
All other outcomes (CHURNED, LOST, etc.) are classified as negative.

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Segment Affinity

For each play template and segment combination, the engine computes an affinity score (0-1) using a weighted average of win rates, giving more influence to larger sample sizes and penalizing low-confidence data.

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Play Recommendations

For each account with active signals, the engine recommends play templates based on three weighted components:
  • Sample Confidence — How much data exists for this play template
  • Win Rate — Historical success rate for matching segment and tier
  • Segment Affinity — How well this play performs for the account’s profile
Low-performing templates are excluded from recommendations. Each recommendation includes the most relevant triggering signal, a human-readable reason, and a priority level (1-5).

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Priority Computation

Priority (1 = highest, 5 = lowest) is computed based on historical win rate, confidence level, and triggering signal severity. High win rates and high confidence increase priority; low confidence and informational signals decrease it.
Exact recommendation weights, priority computation parameters, and exclusion thresholds are available in the PILLAR Implementation Guide provided to active customers.

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Data Model

PILLAR stores play effectiveness metrics (win rates, time to complete, ARR impact, and health deltas per play template and account profile) and generated play recommendations (per-account recommendations with confidence scores, expected outcomes, and priority levels).
Detailed data model schemas are available in the PILLAR Implementation Guide provided to active customers.

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KPIs

The API returns summary KPIs alongside effectiveness data:
KPIDescription
Total TemplatesDistinct play templates with effectiveness data
Avg Win RateOverall win rate across all templates
Top Performing PlayTemplate with the highest win rate
Top Win RateWin rate of the top performing template
Plays Needing ReviewTemplates flagged as underperforming based on configurable thresholds

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API Endpoints

GET  /api/plays/effectiveness
POST /api/plays/effectiveness
See the Playbook API reference for full endpoint documentation.

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Access

Available to: CRO/CEO, VP Sales, VP CS, RevOps