Loyalty Modeling

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

Published by @gtmagents·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.

More on the bench

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Create Quarterly Business Reviews with account health scores, usage analytics, ROI analysis, expansion opportunities, and risk mitigation

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Escalation

Structure and package support escalations for engineering, product, or leadership with full context, reproduction steps, and business impact. Use when an issue needs to go beyond support, when writing an escalation brief, or when assessing whether an issue warrants escalation.

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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.”