Taking a cut (Marketplaces)

Definition (short). You connect two (or more) sides of a transaction (buyers–sellers, riders–drivers, hosts–guests) and charge a commission or fee. You rarely own the underlying good/service; your economics are driven by GMV volume × take rate, minus operating costs.

Recent example. Airbnb’s effective take rate was ~ 13 – 15 % on $60 B+ GBV in 2023. Uber takes ~ 20 – 28 % of each fare after incentives. Amazon Marketplace charges 8 – 15 % category commissions; eBay averages ~ 10 %.

Historical example. Sotheby’s (1744) and Christie’s (1766) have always taken auction commissions. Stockbrokers since the 17th century charged per trade, and newspaper classifieds were proto-marketplaces taking small fees to connect local buyers and sellers.

KPI Definitions

Net Commission Profit Growth % (NCPG). Pseudo: ((Commission_Rev - Operating_Costs)_t - (Commission_Rev - Operating_Costs)_{t-1}) / (Commission_Rev - Operating_Costs)_{t-1} * 100. Why it matters: GMV without monetization, or take rate without efficiency, equals vanity. NCPG unifies the three real levers: volume (GMV), monetization (take rate), and cost discipline. Benchmark: Mature marketplaces target double-digit profit growth.

Gross Merchandise / Transaction Value (GMV). Pseudo: Σ transaction_amounts. Why it matters: Commission dollars usually scale linearly with GMV. Investors use GMV to size the marketplace even before profits. Benchmark: Airbnb GBV $63 B (2022).

Take Rate % (TR). Pseudo: TR = Commission_Revenue / GMV * 100. Why it matters: It shows value capture and pricing power; raising TR without hurting liquidity is gold. Benchmark: Product marketplaces 5 – 15 %; service marketplaces 15 – 30 %.

Operating Cost % of Commission Rev (OPEX). Pseudo: OPEX / Commission_Revenue * 100. Why it matters: High opex eats into take rate gains; scale should drive this down. Benchmark: Post-scale marketplaces target < 50 % opex/commission rev.

Number of Transactions (NT). Pseudo: COUNT(txn_id). Why it matters: Frequency matters for habit and network effects; many small orders can equal one large one in GMV, but frequency builds stickiness. Benchmark: DoorDash did 512 million orders in Q1 2023.

Average Transaction Value $ (ATV). Pseudo: ATV = GMV / NT. Why it matters: Shifts in product mix or user behavior drive ATV; high ATV can boost revenue per txn but often comes with lower frequency. Benchmark: E-commerce AOV $50 – $100.

Other Fees % of GMV (OTHERF). Pseudo: Other_Fee_Revenue / GMV * 100. Why it matters: Diversifies income and effectively raises take rate without raising headline commission. Benchmark: Etsy charges listing + promoted listings fees adding a few extra %.

Mix of Commission vs Subscriptions % (MIXT). Pseudo: Subscription_Revenue / Total_Revenue * 100. Why it matters: Subscriptions stabilize revenue and raise LTV; but too much may deter small sellers. Benchmark: Some B2B marketplaces get > 20 % of revenue from subscriptions.

Active Buyers (ACTB). Pseudo: COUNT(DISTINCT buyer_id WHERE txn>0). Why it matters: Demand-side depth; more buyers improve liquidity, justify higher TR. Benchmark: Etsy had 95.1 M active buyers (2022).

Active Sellers (ACTS). Pseudo: COUNT(DISTINCT seller_id WHERE txn>0). Why it matters: Supply depth; too few sellers causes stockouts/price spikes; too many with no sales drives churn. Benchmark: Etsy reported 7.5 M active sellers (2022).

Buyer Conversion Rate % (CONV). Pseudo: Purchases / Qualified_Sessions * 100. Why it matters: It signals liquidity and UX quality; higher conversion usually lifts both GMV and TR. Benchmark: General e-commerce sees 2 – 3 % conversion rates.

Purchase Frequency / Buyer (LFQ). Pseudo: NT / Active_Buyers. Why it matters: Frequency drives lifetime value and defensibility. Benchmark: Top “habit” marketplaces push > 5 – 10 orders per buyer per year.

Supply Health / Listings Liquidity % (SUPH). Pseudo: Listings_sold / Listings_posted_in_window * 100. Why it matters: If sellers don’t sell, they churn; liquidity is the soul of a marketplace. Benchmark: Healthy resale marketplaces target > 50 % sell-through over 60 – 90 days.

Disintermediation / Leakage Rate % (RISK) (aux). Pseudo: Off_platform_transactions_est / Total_matches_est * 100. Why it matters: High leakage erodes take rate and weakens the moat. Benchmark: Service marketplaces often battle > 20 % leakage early on.