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loyalty-modeling skill

/plugins/loyalty-lifecycle-orchestration/skills/loyalty-modeling

This skill helps design and stress-test loyalty programs by modeling tiers, economics, and ROI scenarios for informed decision-making.

npx playbooks add skill gtmagents/gtm-agents --skill loyalty-modeling

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: loyalty-modeling
description: Use to model economics, tiers, and impact forecasts for loyalty programs.
---

# Loyalty Modeling Toolkit Skill

## When to Use
- Designing new loyalty tiers or revamping benefits.
- Building business cases for loyalty investments.
- Stress-testing point liability, breakage, and ROI.

## Framework
1. **Inputs** – member base, transaction frequency, average order value, churn, CAC.
2. **Earning Model** – simulate points accrual, mission completion, and bonus events.
3. **Redemption Model** – estimate burn rates, cost per reward, and liability curves.
4. **Impact Drivers** – model uplift in retention, cross-sell, referral, or ARPU.
5. **Scenario Planning** – compare base vs optimized vs aggressive variants.

## Templates
- Driver sheet (input assumptions, tier thresholds, rewards cost).
- Sensitivity table (goal → driver → delta → impact).
- Executive summary page for approvals.

## Tips
- Keep calculators modular so benefits can be toggled quickly.
- Align with finance on liability recognition + accounting treatment.
- Pair with `plan-loyalty` command to ensure blueprint is financially sound.

---

Overview

This skill models the economics, tiers, and impact forecasts for loyalty programs. It provides a modular toolkit to simulate earning and redemption, project liability curves, and quantify business outcomes like retention uplift and ROI. The skill is designed for cross-functional teams—marketing, finance, and revenue ops—to build data-driven loyalty business cases.

How this skill works

You provide core inputs (member base, transaction frequency, AOV, churn, CAC) and the skill runs an earning model to simulate points accrual, missions, and bonus events. A redemption model projects burn rates, cost-per-reward, and liability over time. Impact drivers layer on expected changes in retention, cross-sell, referrals, or ARPU, and scenario planning compares base, optimized, and aggressive variants.

When to use it

  • Designing new loyalty tiers or adjusting benefits and thresholds
  • Building an investment case and ROI forecast for a loyalty launch or update
  • Stress-testing point liability, breakage assumptions, and accounting impacts
  • Running sensitivity analysis to prioritize benefit levers and budget
  • Validating outcomes with finance before executive approvals

Best practices

  • Keep calculators modular so individual benefits and thresholds can be toggled quickly
  • Align assumptions with finance for liability recognition and accounting treatment
  • Use driver-based sensitivity tables to map goals to levers and expected delta
  • Maintain a clear executive summary page that highlights trade-offs and break-even points
  • Version scenarios (base, optimized, aggressive) and document key assumptions for each

Example use cases

  • Estimate the impact of adding a mid-tier with exclusive free shipping on retention and cost
  • Compare cost and liability outcomes of flat points vs. mission-based earning structures
  • Quantify breakage assumptions to understand timing and size of point liability on the balance sheet
  • Prioritize reward catalog changes by simulating redemption cost per active member
  • Test how CAC changes when offering targeted acquisition bonuses to specific cohorts

FAQ

How do I handle breakage assumptions?

Model multiple breakage rates and run sensitivity tables; align expected breakage with historical redemption patterns and finance recognition rules.

Can I isolate the impact of a single driver (e.g., retention uplift)?

Yes. Toggle other levers off and run a scenario that only adjusts the retention uplift to measure its marginal effect on revenue and ROI.