Data Collection
Price Scraping And Price Intelligence For Teams That Reprice Every Day
We build price scraping pipelines and keep them running after the first retailer redesign, which is usually where in-house scripts give up. What you get is competitive price intelligence your pricing team can act on, with every competitor price already matched to your SKU and refreshed daily in your database or dashboard.
Dmytro SerebrychCo-founder, udata
Updated
The engineering behind it is what we already run for Retail Shake, a French retail intelligence platform, and for LiveTrend, where our team maintains 370+ scrapers. Hourly rates are listed further down, and the code stays yours.
What Goes Into Price Intelligence Data
A competitor price on its own tells you very little. It matters when it’s the same product, in stock, with no coupon stacked on top. That’s why the price intelligence data we deliver carries context around each number.
Shelf And Promo Prices
The current price next to the strikethrough the retailer shows. Multi-buy deals and coupons land in separate fields you can filter on.
Stock And Availability
A cheap price on a sold-out product shouldn’t pull yours down, so availability travels with every price.
Product Matching
Your SKU linked to the same item on every competitor site, by GTIN or EAN where they exist and by title and attributes where they don’t. Honestly, this is most of the work.
Marketplace Sellers
Who holds the buy box and at what price. Third-party sellers undercutting your listings show up here too.
Delivery Costs
Shipping fees shift the real comparison more than most teams expect, especially on bulky goods.
Price History
Every change kept with a timestamp, so analysts can spot patterns and test pricing rules against last season.
Most clients start with ecommerce price scraping on their five to ten closest competitors and add marketplaces later, once they trust the price intelligence coming out of it. Scraping product prices without matching leaves someone on your team lining up products in Excel every week. We build matching in from the first sprint.
How Our Price Scraping Pipeline Runs
Five stages. The first two settle most of the cost.
Source Audit
We open every competitor site on your list and check how it renders and what bot protection it runs. We also read its terms. Each source gets an effort rating before you see an estimate.
Written Spec
Fields, refresh frequency, matching rules and acceptance checks go into a spec in Notion. About half of it is written before development starts. The rest fills in as sources come online.
Collection
Python and Scrapy cover most sources. A headless browser runs only where a site needs one, and each source has its own proxy budget. We scrape prices on the schedule your use case needs, daily for most retail and more often for marketplaces where it pays off.
Matching And Checks
New products go through matching. Every crawl is checked for missing rows and for impossible jumps, like a 90% drop overnight. Suspicious values are held back so they never reach your repricing rules.
Delivery
Straight into your PostgreSQL, as scheduled exports to a cloud bucket, through an API, or into a dashboard we build. Alerts go to Slack or email when a competitor crosses a threshold you set.
Why Web Scraping Price Monitoring Keeps Breaking
Retailers rebuild category pages without warning, and plenty of them tighten bot protection right before Black Friday. That makes web scraping price monitoring maintenance work as much as build work. On LiveTrend, three scraping engineers and one data engineer look after 370+ scrapers. Plan for that kind of ratio if your source list runs into the hundreds.
Price Monitoring Website, Scraper API, Or A Managed Pipeline?
Companies buy online price monitoring in a few different ways, and we aren’t the right fit for every one of them.
| Option | Fits when | Struggles when | Who fixes broken sources |
|---|---|---|---|
| Off-the-shelf price monitoring website (SaaS) | You track big retailers with standard catalogs | Your competitors are niche, regional or B2B portals the tool doesn’t cover | The vendor, on its schedule |
| Scraper API plus your engineers | You have a data team and only need proxies and rendering | Parsers break faster than your team can patch them | You |
| Managed pipeline from udata | Prices feed repricing, a product or daily reports | You need a single snapshot | Us |
If a SaaS tool already covers your competitors, buy it. It will cost less than custom work. We usually get the call when the tool misses half the sources, or when product price monitoring has to plug into an internal pricing engine the SaaS can’t talk to.
Price Intelligence Platforms We Work On
Two published projects, both from the French market.
Retail Shake · Retail intelligence, Franceengagementongoing
Retail Shake sells online price intelligence to retailers and brands: competitor prices and promotions, stock levels and product data, refreshed daily. Our engineers work on the platform itself, and three data scraping engineers keep collection running. Its customers choose which competitors to track, so the real test is adding a new retailer quickly without breaking the ones already live. Read the case study →
LiveTrend · Fashion intelligence, Franceproxy costs−67%
LiveTrend forecasts fashion trends from market data, prices and assortment included. We run its whole collection side, from 370+ scrapers to crawl monitoring and the proxy layer underneath. On that operation proxy costs dropped 67% and crawls got 15% faster. Coverage grew as well, by 56 brands in three months. Read the case study →
What Price Scraping Costs With Us
We bill engineering time rather than a fee per SKU. How many sources you track and how well they’re protected move the budget more than the size of your catalog.
| Role | Project work | Dedicated, long-term |
|---|---|---|
| Backend developer (Python, Scrapy) | $25–$35/h | $20/h |
| Data engineer | $25–$40/h | $25/h |
| Full-stack developer (dashboards, alerts) | $30–$40/h | $24/h |
| QA engineer | $20/h | $20/h |
| Project management | Included | Included |
For scale: one dedicated backend engineer at $20 an hour comes to roughly $3,400 a month full-time. Proxies and servers are a separate line, and on heavily protected marketplaces they can come close to the engineering bill.
What Moves The Budget
- Refresh frequency. Hourly marketplace prices cost far more to run than a daily crawl of brand sites.
- Matching difficulty. Products with EANs match almost on their own. Fashion and private-label goods need attribute rules and a person reviewing the doubtful pairs.
- Bot protection. A site behind an active bot check has to be rendered in a full browser, which costs more per page and breaks more often.
- Regions and stores. One retailer can show different prices by country, sometimes by postcode, and each variant is another crawl to run and check.
Who Buys Ecommerce Pricing Intelligence From Us
Online Retailers
Daily competitor prices feeding repricing rules or a category manager’s morning report. Online pricing intelligence pays back fastest here, since margin moves with every price change.
Brands And Manufacturers
Watching resellers for MAP breaches and unapproved discounts, with dated price history to bring into the conversation.
Intelligence SaaS
Companies that sell price data to their own customers, as Retail Shake does. You keep the product and the client relationship while our engineers run collection in the background.
Marketplace Sellers
Buy box tracking and an alert when another seller undercuts your listing. A competitors pricing intelligence dashboard is usually enough here, no full platform needed.
If prices are only one of the things you need from the web, our web scraping services page covers catalogs, reviews and other data we collect.
How Our Team Keeps A Monitoring Project On Budget
Pricing intelligence projects don’t end at launch. Sources change every month, so you need to see what the upkeep costs. We built our own tracker for that. It works like Jira, with your budget and scope sitting next to the tickets, so you see hours spent against the agreed amount without asking for a report.
The team posts a written status in Slack every day, and standups run daily or every other day. Sprints last one or two weeks and end with a demo plus a short document on what shipped and which sources changed.
We sign an NDA before the project starts if you want one, and client data is encrypted. The contract says the code and IP are yours. Either side can scale down or end the engagement with two weeks’ notice. If you’d rather have scraping engineers inside your own team, see dedicated developers.
Frequently Asked Questions
Engineering runs $25–$35 an hour for a backend developer on project work, or $20 in a long-term dedicated setup. A handful of unprotected competitor sites is a short project, while hundreds of sources refreshed daily need a monthly team, and proxies and servers sit on top of either. Our advice is to start with your ten most important competitors and expand once the matching holds up.
Public prices aren’t personal data, so GDPR rarely comes into it. Site terms still apply, and in the EU so do database rights. We review each source before collecting and flag the risky ones. If the stakes are high, check with your lawyer.
Daily covers most retail. Marketplaces move faster, and there we can refresh several times a day where the source allows it. Frequency is set per source because every extra run adds proxy cost.
Not quite. Monitoring is collecting prices and sending alerts. Pricing intelligence adds matching and history, which is what lets an analyst explain a competitor’s move. Most clients start with monitoring.
Yes, through an API or straight into your database, whichever your repricing engine reads. The code and IP stay yours under the contract if you ever want to bring the operation in-house.