An electronics retail group needed price intelligence software data covering part numbers, quantity based pricing, stock levels, and lead times, delivered monthly at a scale reaching 250 million SKUs a year.
Client Overview
A large electronics retail group needed a clear, continuous view of how its products were priced across the market, down to the level of individual part numbers and order quantities. Electronics pricing rarely works on a single sticker price, since bulk and quantity based pricing is standard across the category, and the group needed data structured to match that reality rather than a simplified single price point per SKU.
The scale of the ask was significant from the start. The group expected its tracked catalog to reach roughly 250 million SKUs within a year, a volume that ruled out treating this as a side project for an internal team. Price intelligence software built for this had to be paired with a data operation that could actually deliver at that scale, which is why the group brought in PromptCloud as a managed partner rather than continuing to run scraping in house.
Client Requirements
The brief PromptCloud received was specific about both the data and how it needed to arrive:
- Product part number, quantity, and quantity based pricing for every tracked SKU
- Stock levels and lead times alongside pricing, not pricing alone
- A monthly crawl frequency across a competitor site list the client provided
- Clean, structured data delivered straight to the client’s database, ready to query
- No manual processing step required on the client’s side
Challenges
Competitor sites in the electronics category rarely present quantity based pricing in a simple, uniform way. Price breaks by order quantity, part number variations, and stock or lead time indicators are often scattered across a page in formats that differ from one retailer to the next, which made a single generic template unworkable across the client’s full competitor list.
The bigger constraint was scale. No off the shelf price intelligence software was built to handle a catalog approaching 250 million SKUs, and reaching that volume meant the crawl infrastructure had to handle load that would strain a smaller, internally built setup. The client needed to trust that data would keep arriving cleanly every month without their own team stepping in to fix broken scrapers or reformat inconsistent output.
Solutions
PromptCloud built the project around its site crawling service, since the client’s competitor list spanned sites with different structures, and matched delivery to the client’s own data schema from day one, the kind of price intelligence software data the client needed without a translation step at the end.
Crawlers Built for Quantity Based Pricing
Rather than capturing a single price per part number, the crawlers were built to pull the full pricing structure competitors displayed, price by quantity, alongside stock levels and lead times for each listing. This matched how the client’s own pricing strategy actually worked, since a part quoted at one price for a single unit and a different price at bulk quantities needed to be represented as such, not flattened into one number. Getting this structure right up front meant the data was usable for retail price monitoring without extra cleanup on the client’s end.
A Data Template Matched to the Client’s Schema
Before any crawling began, PromptCloud built a data template based on the schema the client’s own database already used. Every field extracted, from part number to lead time, mapped directly onto that structure, so data could be uploaded straight into the client’s systems without a translation step in between. This is the difference between data that is technically available and data that is actually ready to use, and it is what let the client’s analysts start querying datasets instead of preparing them.
Setup in Two Days, Delivery on a Monthly Cycle
PromptCloud’s team had the custom crawlers live within two days of the project starting, and the first delivery landed roughly 200,000 records, an early signal of the volume the setup could handle as the tracked catalog grew toward the client’s 250 million SKU target. From there, data moved to a monthly cycle, delivered in CSV format straight to the client’s Dropbox account, with no manual handoff required on either side.
Monitoring at a Scale Worth Treating as Alternative Data
Automated monitoring watched the full source list for site changes, catching layout shifts before they could break a crawler or introduce gaps in a monthly delivery. At the volume this client was working toward, structured pricing, stock, and lead time data starts to look less like a single company’s internal report and more like the kind of dataset covered in PromptCloud’s alternative data report, the sort of continuously refreshed, structured web data that businesses increasingly treat as a strategic asset rather than a one-off pull.
Price Intelligence Data, Before and After PromptCloud
| Area | Before | After |
| Pricing structure | Single price point per part number | Full quantity based pricing captured |
| Data readiness | Needed in-house cleanup before use | Delivered ready to query, schema matched |
| Setup time | Unknown, dependent on internal team bandwidth | Live in 2 working days |
| Site monitoring | Manual fixes after something broke | Automated, fixed before gaps appear |
Benefits to the Client
Retail price monitoring moved from an internal side project to a dependable monthly feed almost immediately, with the first delivery landing about 200,000 records within days of setup. Zero data processing effort was needed on the client’s side, since every field arrived matched to their own schema and ready to load straight into their database.
Scalable infrastructure kept costs down as the tracked catalog grew toward 250 million SKUs a year, a volume that would have required real infrastructure investment to support internally. The client’s analysts spent their time querying finished datasets and running pricing analysis, not preparing data or maintaining crawlers, and 100 percent API availability meant the feed simply kept working month after month without the client’s team needing to check on it.
Price Intelligence Software Built to Handle Real Scale
Tracking 250 million SKUs a year is not a job for a scraper stitched together to handle one or two competitor sites. Price intelligence software at that volume needs quantity based pricing, stock levels, and lead times captured accurately across every source, delivered on a schedule the business can actually plan around.
What made this work was not just crawling more pages, it was matching the data to the client’s own schema from the start and keeping the pipeline running without manual intervention as volume grew. That is what turned a monthly competitor data pull into infrastructure the client’s pricing strategy could rely on.
