--- name: marketplace-pricing-analyzer description: "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 when setting or revising pricing on a two-sided marketplace, diagnosing why volume or margin is off target, or evaluating a take-rate, surge, or subsidy change." argument-hint: "[marketplace-type] [pricing-goal]" allowed-tools: ["Read"] --- # Marketplace Pricing Analyzer ## 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](https://docs.anthropic.com/en/docs/claude-code/skills) — markdown with YAML frontmatter at the top. **Installing in Claude Code:** - `~/.claude/skills//SKILL.md` — auto-loads in every session anywhere on your machine (recommended for skills you'll use across multiple projects) - `/.claude/skills//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. 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