Case Study — Automotive

Aftermarket Parts Pricing From Major Marketplaces

German Tier-1 automotive supplier • E-commerce pricing data

The Challenge

Our client needed pricing data from major e-commerce marketplaces—eBay, Amazon, and specialized aftermarket platforms—to inform their pricing strategy for replacement parts.

Their internal data engineering team had spent six months attempting to build web scrapers in-house. The project had stalled: modern anti-bot measures from Cloudflare, DataDome, and platform-specific protections proved too sophisticated to bypass reliably. The team was burning resources with nothing to show for it.

Our Approach

We recommended abandoning the build approach in favor of a buy strategy. Our engagement focused on:

• Rapid market scan to identify providers with proven capabilities on the target platforms
• Technical validation through structured proof-of-concept with two finalist vendors
• Commercial negotiation to secure enterprise-grade terms and pricing

The key insight was that specialized providers had already solved the anti-bot challenge at scale—investing further in an internal solution made no economic sense.

The Outcome

Within eight weeks of engagement, the client had a production-ready data feed covering over two million SKUs per week. The selected vendor offered not just raw data but also standardized product matching and category normalization.

The engagement illustrated a common pattern: enterprises often underestimate the complexity of web data collection and overestimate the value of building in-house. A rigorous buy-vs-build analysis, informed by market knowledge, typically favors specialized providers for non-core data needs.