Accurately compute marketplace seller profit after platform fees

Many marketplace sellers—roughly 50 million globally—struggle to calculate true product-level profit because platforms layer on variable, opaque fees (commissions, referral, fulfillment, advertising, chargebacks and returns) and use inconsistent nomenclature across Amazon, eBay, Etsy and Shopify. Small brands and high-volume resellers (annual GMV from ~$50k to $50M) routinely spend hours reconciling CSVs, misprice items, and incur margin leakage on the order of 3–10% as a result. You could build a SaaS product that ingests payout files, invoices, ad spend and shipping costs, normalizes and classifies fee lines with ML/LLM parsers, and delivers per-SKU, per-channel profit and automated reconciliations with alerts and an API for ERPs and accounting tools. The economics are clear: with an assumed $100 ACV the addressable market implied by 50M potential users is $5.0B, and an initial beachhead of mid-size sellers and agencies (pricing $200–2,000/year) is realistic. Market forces make this timely—marketplaces have been adding roughly 2–4 new fee line items per year, multichannel selling is rising, and modern ML reduces manual reconciliation time by an estimated 70–90%. To stand out you’ll need >=95% fee-classification accuracy, deep native integrations for platform payout formats, an immutable audit trail for accountants, and a frictionless onboarding (e.g., import 12 months of statements in under 30 minutes); labeled datasets and integration breadth can become durable advantages. Be honest about the challenges: secure access to sensitive payout data, continuous maintenance as platforms change formats, and nontrivial customer acquisition cost across many small sellers will require significant engineering and trust-building spend in year one.