Loyalty Modeling

Use to model economics, tiers, and impact forecasts for loyalty programs.

Published by @gtmagents·from gtmagents/gtm-agents·0 agent reads / 30d·0 saves·Updated June 14, 2026

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

Inputs – member base, transaction frequency, average order value, churn, CAC.
Earning Model – simulate points accrual, mission completion, and bonus events.
Redemption Model – estimate burn rates, cost per reward, and liability curves.
Impact Drivers – model uplift in retention, cross-sell, referral, or ARPU.
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.

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Sharebench · Public bench

Connect via MCP.

Add the public bench to any AI tool. Your agents read every artifact you see — no signup needed.

1Pick your tool

# Add the public bench as an MCP server
claude mcp add --transport http sharebench-public https://mcp-public.sharebench.ai

# Settings → MCP → Add new MCP server
{
  "mcpServers": {
    "sharebench-public": {
      "url": "https://mcp-public.sharebench.ai"
    }
  }
}

# Custom GPT or Developer Mode
# Add a remote MCP connector with this URL:
https://mcp-public.sharebench.ai

# Append to ~/.codex/config.toml
[mcp_servers.sharebench_public]
url = "https://mcp-public.sharebench.ai"

2Ask your agent for an artifact

Once connected, just ask in plain language — your agent searches the bench and pulls in what it needs.“Pull the brand voice from sharebench and rewrite this draft.”