---
name: gtmagents-member-insights
description: "Use to analyze loyalty member behavior, segmentation, and experiment results."
license: "Apache-2.0"
metadata:
  decimalai-skill-id: "fb1cbba4-47f4-4b72-a941-76c0587eb973"
  decimalai-version-id: "b30c1a63-5f1b-43a9-9bc0-2773c79be54c"
  decimalai-version: "1"
  decimalai-body-sha256: "33272b8980dd8e930d16cc511991eb8d900400da2a3679744c350db83c505142"
  decimalai-name: "gtmagents/member-insights"
  decimalai-format: "1"
  decimalai-invocation: "model"
  decimalai-category: "marketing"
  decimalai-source: "https://app.decimal.ai/s/gtmagents-member-insights@1/SKILL.md"
---
# Member Insights Skill

## When to Use
- Monitoring program health across tiers or regions.
- Designing personalization campaigns based on loyalty data.
- Reporting experiment outcomes to stakeholders.

## Framework
1. **Data Sources** – transaction systems, product telemetry, MAP/CRM, support, survey tools.
2. **Segmentation** – tier, lifecycle stage, engagement score, risk/comeback cohorts.
3. **Metrics** – enrollment funnel, active members, point velocity, redemption, incremental revenue.
4. **Experimentation** – define guardrail metrics, success criteria, and monitoring cadence.
5. **Insight Distribution** – dashboards, alerts, and story-driven memos for GTM teams.

## Templates
- KPI scorecard (metric → target → actual → variance → owner).
- Segment heatmap (cohort → engagement → action recommendation).
- Experiment readout template (hypothesis, lift, guardrails, next steps).

## Tips
- Blend quantitative metrics with VOC snippets for context.
- Tag insights with urgency so ops/marketing can prioritize quickly.
- Pair with `monitor-loyalty` command to ensure consistent reporting structure.

---