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customer-insights skill

/plugins/customer-marketing/skills/customer-insights

This skill helps consolidate product usage, health, and sentiment signals into actionable insights for lifecycle programs and churn prevention.

npx playbooks add skill gtmagents/gtm-agents --skill customer-insights

Review the files below or copy the command above to add this skill to your agents.

Files (1)
SKILL.md
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---
name: customer-insights
description: Use when consolidating product usage, health, and sentiment signals for
  lifecycle programs.
---

# Customer Insights & Telemetry Skill

## When to Use
- Building segment-specific lifecycle journeys.
- Prioritizing accounts for adoption help, expansion offers, or advocacy invites.
- Diagnosing churn/retention risks and surfacing insights to CS + product.

## Framework
1. **Signals Stack** – product usage, engagement, sentiment, commercial, health composite.
2. **Data Plumbing** – define sources (warehouse, product analytics, CS tools) and refresh cadence.
3. **Normalization** – align account/user IDs, tag personas/verticals.
4. **Insight Delivery** – dashboards + alerts to lifecycle, CS, product teams.
5. **Closed Loop** – track outcomes (expansion booked, churn prevented, advocacy activated).

## Templates
- Health score schema (dimensions, weight, threshold, owner).
- Insight brief (observation, impact, recommended play, owner, due date).
- Data dictionary for lifecycle dashboards.

## Tips
- Keep manual notes from CSMs in sync with telemetry to avoid blind spots.
- Tag signals by persona/vertical for more precise plays.
- Automate distribution via Slack/email alerts tied to triggers.

---

Overview

This skill consolidates product usage, health, and sentiment signals into a single customer insights layer to power lifecycle programs. It standardizes telemetry across tools, computes health and risk scores, and delivers actionable briefs and alerts to GTM teams. The result is faster prioritization, clearer handoffs, and measurable closed-loop outcomes.

How this skill works

It ingests signals from product analytics, the data warehouse, CRM, and customer success tools, then normalizes account and user identifiers and tags by persona and vertical. The skill builds a signals stack (usage, engagement, sentiment, commercial, composite health), applies configurable weightings to produce health scores, and produces dashboards, alert rules, and insight briefs. Outputs are distributed to lifecycle, CS, and product teams and tracked for outcome metrics like expansion booked or churn avoided.

When to use it

  • Designing segment-specific lifecycle journeys and outreach playbooks.
  • Prioritizing accounts for adoption help, expansion campaigns, or advocacy invites.
  • Detecting early churn or retention risk and surfacing root causes.
  • Aligning product, CS, and revenue ops around a unified health signal.
  • Measuring impact of plays with closed-loop outcome tracking.

Best practices

  • Define your signals stack and refresh cadence before building dashboards.
  • Normalize account and user IDs and enforce a shared data dictionary.
  • Weight health dimensions explicitly and document thresholds and owners.
  • Keep CSM manual notes synced with telemetry to avoid blind spots.
  • Tag signals by persona and vertical to enable precise plays and measurement.
  • Automate alerts to Slack or email and include recommended next actions.

Example use cases

  • Create a health score dashboard for enterprise accounts and trigger CS outreach when composite drops below threshold.
  • Segment users by adoption signals to enroll high-value customers into expansion campaigns.
  • Surface sentiment declines from support tickets and usage dips to product for quick fixes.
  • Generate insight briefs for weekly GTM standups summarizing high-risk accounts and recommended plays.
  • Track outcomes of interventions (churn prevented, expansion booked) to refine weighting and thresholds.

FAQ

Which data sources should I prioritize for early value?

Start with product analytics, CRM activity, and recent support or NPS sentiment. Those deliver immediate signals for adoption and risk.

How do I choose weights for the health score?

Pick weights based on business impact and validate with historical outcomes. Iterate using A/B tests or back-testing against churn and expansion data.