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Marketplace pricing analyzer

Analyzes marketplace pricing across supply, demand, and margin dimensions to surface tradeoffs and deliver ranked optimization recommendations, take-rate analysis, price elasticity assessment, and a testing roadmap. Use…

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Purpose

Diagnose how a marketplace's current pricing structure balances supply liquidity, demand conversion, and unit margin — then deliver ranked recommendations, elasticity assumptions to validate, and a testing plan. Resolves the core two-sided tension: pricing that fills demand can starve supply, and pricing that rewards supply can price out demand.

When to Use

Invoke this skill when the user asks to:

  • Set or revise pricing for a marketplace, platform, or two-sided product
  • Analyze take rate, commission, or platform fee structure
  • Diagnose why booking volume, GMV, or contribution margin is missing targets
  • Evaluate a surge, dynamic, or peak-pricing change before rollout
  • Assess a supply-side subsidy, incentive, or payout change
  • Compare pricing tradeoffs across multiple ride/product/service types

Inputs Required

Before starting, collect:

  • Marketplace type: What is bought and sold, and who the two sides are (e.g., riders/drivers, buyers/sellers, guests/hosts)
  • Product/ride types: The distinct offerings being priced (e.g., standard vs. premium tiers, categories, service levels)
  • Current pricing structure: Base price/fees, take rate or commission %, any dynamic or surge mechanics, supply-side payout formula
  • Current performance: Relevant metrics — GMV, take rate realized, conversion, supply utilization, contribution margin, churn on either side
  • Business goal: Primary objective — grow GMV, expand margin, improve supply liquidity, increase conversion, or defend against a competitor
  • Market conditions: Demand seasonality, supply constraints, competitor pricing, geographic or category concentration
  • Constraints: Regulatory caps, contractual payout floors, brand/fairness limits, or margin floors

If elasticity data is unavailable, work from category benchmarks and flag every assumption as a hypothesis to test rather than a fact.

Instructions

Step 1: Map the Current Pricing Structure

Lay out the money flow before analyzing it:

  • Price to demand side: What the buyer pays, all-in (base + fees + surge)
  • Payout to supply side: What the seller/provider receives, net of platform cut
  • Platform take: The spread — expressed as both absolute value and take rate %
  • Per-transaction margin: Take minus variable costs (payment processing, support, incentives)

Confirm the take rate is calculated on the correct base (gross vs. net) — miscounted take rate is the most common source of flawed pricing conclusions.

Step 2: Diagnose the Binding Constraint

Every marketplace is limited by one side at a time. Identify which:

  • Supply-constrained (demand exceeds available supply): high utilization, frequent unmet demand, rising wait times → pricing power exists; risk is under-monetizing
  • Demand-constrained (supply exceeds demand): low utilization, idle supply, provider churn → pricing must protect conversion; risk is over-taxing demand
  • Balanced/liquid: both sides clear at current price → optimize margin carefully without disturbing equilibrium

State the constraint explicitly. Every downstream recommendation depends on it.

Step 3: Assess Price Elasticity on Both Sides

Estimate responsiveness for each side:

  • Demand elasticity: How much does volume drop if buyer price rises? Higher for discretionary/commodity categories, lower for urgent/differentiated ones
  • Supply elasticity: How much does available supply grow if payout rises? Higher when supply is part-time/flexible, lower when it requires dedicated investment
  • Cross-side effects: A price increase to demand that thins volume also reduces supply-side earnings and can trigger supply churn — model the second-order loss

For each product/ride type, assign a qualitative elasticity band (Low / Medium / High) with reasoning. Note which estimates need experimental validation.

Step 4: Evaluate Take-Rate Positioning

Pressure-test the current take rate:

  • Benchmark band: Ride-hailing/delivery 20–30%; goods marketplaces 5–15%; services marketplaces 10–20%; software/rental platforms 10–25% — position the current rate against its category
  • Take-rate ceiling signals: supply churn, disintermediation (parties transacting off-platform), competitor undercutting
  • Take-rate floor signals: strong margin headroom, low supply sensitivity, dominant position with switching costs
  • Structure vs. level: Sometimes the fix is where the fee sits (buyer-side vs. seller-side) or how it's framed, not the total percentage

Step 5: Analyze Dynamic / Surge Mechanics (if applicable)

If time- or demand-variable pricing is in play or under consideration:

  • Trigger accuracy: Does surge activate on real supply/demand imbalance or on noisy signals?
  • Supply responsiveness: Does higher surge actually pull more supply into the market, or just extract more from inelastic demand?
  • Fairness/perception risk: Where does surge cross into backlash territory — cap it before that point
  • Margin capture: Who keeps the surge premium (platform vs. supply)? Splitting it can preserve supply loyalty

Step 6: Model the Tradeoffs Across Dimensions

For the primary business goal, spell out what each pricing lever gives and costs:

  • Raise buyer price → +margin/unit, −conversion, −supply earnings, −liquidity
  • Raise supply payout → +supply liquidity, +retention, −margin/unit
  • Raise take rate → +margin/unit, risk of supply churn/disintermediation
  • Lower price to grow volume → +GMV, +liquidity, −margin/unit (justified only if elasticity clears the margin loss)

Quantify directionally where inputs allow (e.g., "a 10% take-rate increase adds ~X margin but risks Y% supply churn based on the assigned elasticity").

Step 7: Rank Optimization Recommendations

Deliver the top 6–8 recommendations ordered by expected impact on the stated goal. For each:

  1. The move: The specific pricing change
  2. Why now: How it aligns with the binding constraint and goal
  3. Expected effect: Directional impact on supply, demand, and margin
  4. Primary risk: The most likely way it backfires
  5. Confidence: High / Medium / Low, based on data quality

Step 8: Build the Testing Roadmap

Sequence how to validate before full rollout:

  • What to test first: The highest-impact, highest-uncertainty change
  • Test design: Geo split, cohort holdout, or time-based A/B — with the control held constant
  • Success metric: The specific outcome and the tradeoff metric to watch (e.g., optimize contribution margin without dropping supply utilization below threshold)
  • Guardrails: The supply-churn or conversion floor that halts the test
  • Read window: How long to run before deciding, accounting for supply behavior lag

Output Format

  • Structure: 8 sections following the diagnostic sequence; tradeoff analysis and recommendations in ranked/structured form
  • Length: 1,000–1,400 words
  • Format: Structured document — prose for diagnosis, tables or ranked bullets for tradeoffs and recommendations, week/phase sequence for the testing roadmap
  • Sections: Pricing structure map → Binding constraint → Elasticity assessment → Take-rate positioning → Dynamic pricing analysis → Tradeoff model → Ranked recommendations → Testing roadmap
  • Tone: Pricing strategist — analytical, tradeoff-explicit, no false precision; state assumptions rather than hide them

Quality Checklist

Before delivering output, verify:

  • [ ] The binding constraint (supply-, demand-constrained, or balanced) is named explicitly and drives the recommendations
  • [ ] Take rate is benchmarked against the correct category band, calculated on the stated base
  • [ ] Every elasticity estimate carries a Low/Medium/High band with reasoning, and untested assumptions are flagged
  • [ ] Each recommendation shows directional impact across all three dimensions — supply, demand, and margin
  • [ ] The top recommendations are ranked by expected impact on the stated goal, not listed arbitrarily
  • [ ] The testing roadmap includes a guardrail metric and a floor that halts the test

Notes

  • The most common marketplace pricing error is optimizing one side in isolation — a change that looks margin-accretive per transaction can shrink liquidity and reduce total GMV
  • When supply is the constraint, raising payout usually beats lowering buyer price; when demand is the constraint, the reverse holds
  • Disintermediation risk rises sharply with take rate — if buyers and sellers can easily transact off-platform, the effective take-rate ceiling is lower than benchmarks suggest
  • Never recommend a large simultaneous change to both sides; isolate variables so the test result is readable

How to use this skill

This file is a Claude Code skill in Anthropic's standard format — markdown with YAML frontmatter at the top.

Installing in Claude Code:

  • ~/.claude/skills/<folder-name>/SKILL.md — auto-loads in every session anywhere on your machine (recommended for skills you'll use across multiple projects)
  • <your-project>/.claude/skills/<folder-name>/SKILL.md — auto-loads only when working in that project (recommended for project-specific skills)

Restart Claude Code or open a new session to register.

Using in Claude.ai, ChatGPT, Gemini, or other AI tools:

  1. Drag this file into a new chat, or copy and paste its contents
  2. Send your task or request as your next message — the AI will apply the skill where it fits

For Claude Code skill details: https://docs.anthropic.com/en/docs/claude-code/skills