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member-insights skill

/plugins/loyalty-lifecycle-orchestration/skills/member-insights

This skill analyzes loyalty member behavior and experiments to inform segmentation, personalization, and impact reporting for GTM teams.

npx playbooks add skill gtmagents/gtm-agents --skill member-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: member-insights
description: Use to analyze loyalty member behavior, segmentation, and experiment
  results.
---

# 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.

---

Overview

This skill helps analyze loyalty member behavior, segmentation, and experiment results to drive actionable GTM decisions. It packages a production-ready framework for sourcing data, defining segments, tracking key metrics, and producing clear experiment readouts. Use it to surface prioritized recommendations, build stakeholder-ready visuals, and standardize reporting across teams.

How this skill works

The skill ingests signals from transaction systems, product telemetry, MAP/CRM, support, and survey tools to create a unified view of member activity. It applies segmentation logic (tiers, lifecycle stage, engagement score, risk/comeback cohorts) and computes core metrics like enrollment funnel, active members, point velocity, redemption, and incremental revenue. Experimentation support adds guardrail metrics, success criteria, and monitoring cadence, and generates templates for scorecards, heatmaps, and readouts for distribution.

When to use it

  • Monitoring program health across tiers, regions, or channels
  • Designing personalization or re-engagement campaigns using loyalty data
  • Evaluating and reporting experiment outcomes to stakeholders
  • Prioritizing ops and marketing actions based on member risk or opportunity cohorts
  • Standardizing KPI reporting and alerting for cross-functional GTM teams

Best practices

  • Combine quantitative metrics with voice-of-customer snippets to add qualitative context
  • Tag insights with urgency and owner to speed operational responses
  • Use consistent KPI scorecards (metric → target → actual → variance → owner) for clarity
  • Define guardrail metrics and monitoring cadence before launching experiments
  • Produce story-driven memos and dashboards to make insights consumable for GTM teams

Example use cases

  • Weekly scorecards showing enrollment funnel and tier migration with owners and variance
  • Segment heatmap highlighting high-value but at-risk members with recommended actions
  • Experiment readout summarizing hypothesis, lift, guardrails, and next steps for an A/B test
  • Personalization campaign targeting based on engagement score and redemption velocity
  • Operational alerts when point velocity or redemption deviates from expected benchmarks

FAQ

What data sources does the skill expect?

Transaction systems, product telemetry, MAP/CRM, support, and survey tools. The skill is designed to work with combined signals from these sources to produce reliable member insights.

How are segments defined?

Segments are flexible but typically include tier, lifecycle stage, engagement score, and risk/comeback cohorts. Definitions should be aligned to business rules and updated as program behavior evolves.