Elasticity Lab

Use to run pricing elasticity experiments with consistent assumptions and guardrails.

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

Elasticity Lab Skill

When to Use

Modeling price increases/decreases across segments.
Evaluating new pricing metrics (consumption, seats, feature gates).
Stress testing monetization experiments before council reviews.

Framework

Data Inputs – define baseline metrics (ASP, win rate, churn, attach rate) with data sources.
Elasticity Curves – map expected response bands (conservative/moderate/aggressive).
Scenario Builder – plug in price/metric changes and auto-calc ARR, margin, churn impact.
Risk Flags – highlight cohorts sensitive to change (industry, tenure, product mix).
Experiment Plan – propose pilot cohorts, measurement windows, and success criteria.

Templates

Elasticity workbook (inputs, assumptions, scenarios, summary).
Pilot plan checklist with KPI thresholds.
Risk matrix by cohort.

Tips

Keep historical data windows consistent to avoid noisy comparisons.
Document assumptions + version history for auditability.
Pair with pricing-governance to log council approvals.

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Sharebench · Public bench

Connect via MCP.

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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

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