Customer Reference Tracker

SkillDev tools

Manages customer reference calls, NPS analysis, and churn pattern detection

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the Customer Reference Tracker skill

What this skill tells your AI

The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/domains/business/venture-capital/skills/customer-reference-tracker/SKILL.md and read by ahel’s review.

Overview

The Customer Reference Tracker skill manages the customer reference check process during due diligence. It coordinates reference calls, analyzes customer satisfaction patterns, and identifies churn risks through systematic customer feedback collection.

Capabilities

Reference Call Management

  • Track reference requests and scheduling
  • Maintain reference call question templates
  • Record and summarize reference call notes
  • Manage reference fatigue and rotation

Customer Satisfaction Analysis

  • Aggregate NPS and satisfaction data
  • Analyze satisfaction trends over time
  • Segment satisfaction by customer type
  • Benchmark against industry standards

Churn Pattern Detection

  • Identify early warning indicators
  • Analyze churned customer characteristics
  • Track save rates and win-back patterns
  • Model churn risk factors

Customer Success Assessment

  • Evaluate customer success operations
  • Assess expansion and upsell patterns
  • Analyze customer health scoring
  • Review support ticket patterns

Usage

Coordinate Reference Calls

Input: Customer list, reference requirements
Process: Request references, schedule calls, track completion
Output: Reference call schedule, status tracking

Summarize Reference Findings

Input: Reference call notes, interview data
Process: Synthesize feedback, identify patterns
Output: Reference summary report, key themes

Analyze Customer Health

Input: Customer data, satisfaction metrics
Process: Aggregate and analyze customer health
Output: Customer health assessment, risk flags

Detect Churn Patterns

Input: Historical churn data, customer characteristics
Process: Pattern analysis, risk modeling
Output: Churn risk assessment, leading indicators

Reference Call Framework

CategorySample Questions
Problem/SolutionWhat problem does the product solve? Alternatives considered?
ImplementationHow was the implementation process? Time to value?
Value DeliveredWhat results have you achieved? ROI?
RelationshipHow responsive is the team? Would you recommend?
FuturePlans to expand usage? Concerns about the relationship?

Integration Points

  • Commercial Due Diligence: Feed customer insights into DD
  • Cohort Analyzer: Connect reference feedback to cohort data
  • Financial Due Diligence: Validate revenue quality
  • DD Coordinator (Agent): Coordinate with overall DD process

Best Practices

  1. Request diverse references (not just hand-picked logos)
  2. Include churned customer references when possible
  3. Use consistent question frameworks for comparability
  4. Triangulate reference feedback with quantitative data
  5. Respect customer time and reference fatigue limits

Signals

GitHub stars
2k
Forks
112
Last commit
Sep 2026
Advanced
Item type
skill
Key
customer-reference-tracker
Source
github.com/a5c-ai/babysitter