Manual competitor pricing costs hours — automated web scraping + BI

Category managers, repricers and pricing teams at online retailers and brands routinely spend hours or days reconciling competitor prices across marketplaces and direct sites, relying on spreadsheets and ad‑hoc scraping that causes missed promotions and margin leakage. This problem is most acute for mid‑market sellers with 10,000–100,000 SKUs and limited engineering resources who cannot maintain robust, reliable feeds at scale. You could build a managed SaaS that pairs scalable web scraping (headless browsers + managed proxy pools) with ML/OCR price extraction, SKU resolution and a BI layer offering dashboards, alerts, APIs and a dynamic pricing engine. Price it around the market average ($4,000 ACV) with optional onboarding and customization services, data‑quality SLAs and prebuilt ERP/PIM connectors to enable non‑engineering teams to onboard in weeks rather than months. The timing is favorable: an $8.0B addressable market (2,000,000 retailers × $4,000 ACV), broad e‑commerce proliferation, commoditized scraping tooling and recent ML/OCR gains that materially improve coverage and accuracy. Independent scores also look strong (Market Score 88/100, Revenue Potential 92/100), but this is a competitive space with established incumbents and room for focused entrants. To stand out, focus on measurable accuracy and coverage improvements (e.g., higher image‑price extraction rates, robust SKU matching), transparent SLAs and a managed operations offering that removes scraping ops from customers’ plates. Be honest about the hard parts: anti‑bot defenses, legal/regulatory risk and the operational cost of scale mean you must invest in IP rotation, fallback capture methods and a disciplined go‑to‑market that proves unit economics on a mid‑market cohort before expanding.