Web Scraping for Competitor Research and Market Intelligence: The Complete Playbook
The research use cases that move revenue, and the two approaches that cover every one of them. From scheduled scraping recipes for a few fixed sources to raw extraction structured by AI inside Second Brain.
Your competitors publish more about themselves than they realize. Prices, product changes, hiring, positioning, the language they use to sell, the documents they leave on their own servers. It’s all sitting on the public web. The companies that win the game aren’t the ones with secret information. They’re the ones who collect the public information systematically while everyone else checks a few tabs by hand and calls it research.
Web scraping is how you do that at scale. Done well, it turns scattered public data into a structured view you can actually act on. Done badly, it turns into a pile of broken templates you spend more time fixing than using.
This is the full picture: the use cases worth your time, and the two ways to deliver them depending on the job. We run both, and most of this article is about helping you pick the right one before you build anything.
The research use cases that matter most
Scraping earns its place when the manual version is too slow, too shallow, or too easy to skip. These are the jobs where it consistently pays off.
Competitor monitoring. Track pricing, product availability, new features, and messaging across the competitors you care about. The point isn’t a one-time snapshot. It’s noticing the day a rival changes a price, drops a product, or rewrites how they pitch themselves, while it still matters.
Lead generation and prospecting. Build targeted lists of businesses that fit your customer, then enrich them with contacts. For local leads, the Google Maps Scraper is the fastest route: one pass returns business name, address, phone, website, and reviews, so the list is half enriched before you start. For wider, non-local prospecting, the Google Search Scraper pulls relevant companies from across the web, then the Emails Scraper and Phone Number Scraper attach contact details from their sites. Smaller, sharper lists beat giant generic ones every time.
Market and niche research. Map a space you’re entering or expanding into. Who the players are, how they position, where the gaps sit. The Google Search Scraper is built for this wider view: it pulls relevant results from across the web, not just one local area, and the Get Page Content reads the pages it surfaces. Together they give you the lay of the land without a week of clicking.
Person and company research. Due diligence before a deal, partner vetting, sales research before a call. Pull what’s public about a person or a company from across the web so you walk in informed instead of guessing. I’ve written separately about why the first step here is never to ask AI to recall facts, it’s to collect the real data first, then let AI work on it.
Tender, RFP, and bid discovery. Bids and contract opportunities are scattered across portals, agency sites, and procurement pages, each formatted differently. Scraping the right keywords across the right sources turns a daily manual hunt into a feed.
Content and SEO research. See what competitors publish, which topics they cover, and where they don’t. The Page Content Extractor pulls the actual text so you can analyze structure, messaging, and keyword focus across dozens of pages at once.
Public files hiding in plain sight. The Files and Documents Finder uncovers PDFs, decks, brochures, and spec sheets hosted on a company’s own site. Sales material, pricing documents, and product detail that never made it onto a normal page.
Local business data. Beyond single leads, the Google Maps Scraper gives you a full local dataset: names, addresses, phones, websites, and reviews across an area. Useful for market sizing and seeing competitive density in a given location, not just building a contact list.
Every one of these comes down to the same two jobs underneath: collect the data, then structure it into something you can use. The mistake most people make is trying to do both at the moment of collection. That’s what makes scraping feel hard. Separate the two and the right approach becomes obvious.
The two questions that decide your approach
Before you build anything, answer these:
How many sources? A handful of fixed sites, or dozens to hundreds?
How stable and how often? A few predictable sites you’ll re-scrape on a schedule, or a wide, changing list you’ll touch once or a few times?
Few sources, repeated often, stable structure points to one approach. Many sources, broad and mixed points to the other. We’re good at both, and they’re genuinely different jobs.
Approach one: a few stable sources on a schedule. Build the recipe.
You track three or four competitor product pages and a couple of supplier catalogs, and you want fresh prices and stock every morning. The pages are already structured, and you’re going to hit the same URLs again and again.
This is where a custom scraping recipe is exactly right.
When the structure is stable and you’re returning daily or weekly, precise setup pays for itself fast. A recipe in Hexomatic targets the exact fields you want, handles pagination, walks every product, and delivers clean structured rows on a schedule. Price in its column, stock in its column, no cleanup, no babysitting. Build it once and it runs.
Two ways to get there. Build it yourself with the scraping recipe tool: point and click the fields, set the schedule, done. Or hand the brief to our Concierge Service and our team builds the recipe for you, including the awkward sites that fight back. The output is precise and repeatable, which is the whole point when the same numbers feed a decision every single day.
The honest trade: a recipe is tied to that site’s layout, so a redesign means a fix. For a handful of sources that makes sense. Maintaining few recipes (templates) is trivial. It stops being trivial the moment the list grows, which is exactly when the second approach takes over.
Approach two: many mixed sources at scale. Grab the raw page, structure it in Second Brain.
Now you want forty competitors in a niche, a couple hundred local sites, or a long list where every site is built differently and some are product pages too. A custom recipe for each is a non-starter. You’d spend more time engineering and re-fixing templates than using anything they produce.
Here you don’t want a scraper that understands each site. You want one that reads any of them.
Get Page Content, the Page Content Extractor automation, pulls the readable text off any URL. No selectors, no per-site setup. Give it a link, get the text. It works on a pricing page, a service page, a directory listing, an article, because you’re not asking it to understand the layout, you’re asking it to grab the words. Google Search Scraper does the same one level up: a query in, the top results out, ready to feed the extractor. For a large site, the Website Crawler finds the pages and the extractor pulls each one.
One simple step replaces dozens of fragile ones. But raw text isn’t a spreadsheet yet, and that’s the part people get stuck on. The structuring still has to happen. It just happens later, by AI, inside Second Brain.
Where the raw path gets its structure
Connect Get Page Content to Second Brain and the extracted text lands in your local database as scraped pages, tied to the workflow that pulled it. Your data sits on your machine, not on someone’s cloud. Then you ask Claude to turn it into what you need: company name, positioning, pricing signals, key claims, contact details, whatever the job calls for, written into clean columns.
The structure is applied after collection, by AI, to text it can actually read. You get recipe-quality output without writing a recipe for every site. And because text extraction doesn’t care about layout, a redesign on any of those forty sites breaks nothing. The words are still the words. Rerun the same two steps and your sheet updates. This is also how you keep a research dataset current: schedule the collection, and Second Brain stays fresh without you reimporting by hand.
This is the real connection between Hexomatic and Second Brain. Hexomatic collects across many sources. Second Brain holds it locally and structures it at scale. AI sits in the middle doing the one thing it’s reliably good at.
Why not skip Second Brain and paste it all into Claude directly?
Because that’s where AI quietly falls apart, and it’s worth being exact about why. I covered this in one of my previous articles, but the short version: AI is excellent at structuring a small, focused chunk of text and unreliable the moment the input gets big. Every model has a context window, a hard limit on how much it holds at once. Dump fifty pages of raw scraped text into one prompt and it starts dropping records and blending one source into another. The capability is there. The capacity isn’t.
That gap is the entire reason Second Brain exists. The data lives in a local database, and Claude is fed the right slice at the right time instead of everything at once. You get the structuring quality of a small input applied across a large dataset, because the database does the holding and the feeding.
One discipline that protects every dataset
When you ask Claude to structure scraped text, tell it explicitly to use only what’s in the data. If a phone number isn’t on the page, the field stays empty. It does not invent a plausible one. Structure what’s there, leave blanks as blanks. A dataset that looks complete but is quietly wrong in ten places is worse than one with honest gaps, especially when you’re about to make a competitive decision on it.
Mapping use cases to approach
Most price monitoring and product tracking on a fixed set of competitors fits the scheduled recipe: stable sources, repeated often, structured fields you want clean and on time.
Most market mapping, lead research, person and company research, tender discovery, content analysis, and document hunting fits the raw path: many sources, mixed and changing, where the value is breadth and where AI structuring in Second Brain beats building and maintaining a template per site.
Plenty of real projects use both. A scheduled recipe watching your core competitors, and a raw-plus-Second-Brain sweep whenever you research a new market or batch of prospects. The skill isn’t loyalty to one method. It’s reading the job correctly before you start.
Get the right setup for your job
Self-serve. Start in Hexomatic. For a wide research sweep, run Google Search Scraper and Get Page Content across your URLs, pipe the results into Second Brain, and ask Claude to structure them. For a few fixed competitors, build a scraping recipe with the visual scraper and put it on a schedule.
Concierge. Don’t want to build the recipe or wire up the pipeline yourself? Send us the brief through our Concierge Service. We’ll set up either path for your exact use case, the scheduled recipe or the raw-to-Second-Brain flow, and hand you working output.
Enterprise. Running this across many sources on a schedule, feeding internal systems and a structured local knowledge base your team queries with AI? Book a call and we’ll architect the full pipeline around your data, from collection to a research database your whole team can ask questions of.
