RunMacroCopying information off a website by hand — prices, listings, contacts, search results — is slow, mind-numbing, and quietly full of errors. The moment you need the same data from a hundred pages, or the same report every morning, it stops being a task and becomes a bottleneck. Web data extraction with RunMacro turns that bottleneck into a workflow: it opens each page, reads exactly the elements you care about, and writes the results to Excel or Google Sheets, all without a single line of code. This guide walks through how to build one that actually holds up in production.
Why automate extraction instead of copy-pasting?
Manual collection does not scale and it is not consistent. Two people copying the same table will format it two different ways, and a tired person at 5 p.m. will miss a row. A workflow does the same thing the same way every time, runs unattended, and can be scheduled to refresh your data overnight. The real win is not just speed — it is trustworthy, repeatable output you can build reports on.
Step 1 — Target elements the reliable way
The single biggest factor in whether a scraper survives is how it finds things on the page. Clicking fixed screen coordinates breaks the moment the layout moves. RunMacro's Smart HTML instead reads and clicks elements by their selector — the underlying structure of the page — so it keeps working after redesigns and across screen sizes. Pick the element once with the built-in picker and RunMacro remembers how to find it.
Two companions make this robust. Use Wait for element so the workflow pauses until the content has actually loaded rather than racing an empty page, and use IF element to branch when a field is missing — for example, skipping a product that has no price instead of crashing the run.
Step 2 — Run Chrome in the background
Extraction almost always runs in Background mode (Chrome/CDP). RunMacro drives Chrome through the DevTools Protocol, which means it reads the live DOM directly and never has to take over your physical mouse. You start the crawl and keep working. Navigate with Open URL, spread work across tabs using New Tab and Switch Tab, and call Reload when a page needs a refresh to load fresh results.
Step 3 — Handle pagination and dynamic content
Real sites rarely put everything on one page. The standard pattern is a loop: read every item on the current page, then either click the Next button or open the next page URL, and repeat until there are no more results. Use a label and a condition so the loop stops cleanly when the Next button disappears. For pages that load more items as you scroll, add a short wait after each scroll so new rows have time to appear before you read them — patience here is what separates a complete dataset from a half-empty one.
Step 4 — Export to Excel or Google Sheets
Once the values are in variables, write them out immediately rather than holding everything in memory. Append CSV Row builds a spreadsheet one row at a time, which also means a crash halfway through still leaves you the rows collected so far. If your team lives in the cloud, Google Sheets pushes each record straight into a shared sheet that updates in real time. Writing as you go, not at the end, is the habit that saves long runs.
A complete example workflow
Here is the shape of a production-ready extractor:
• Open URL to the first results page and Wait for element on the list container
• IF element check that results exist; if not, jump to the end
• For each item, use Smart HTML to read the fields (name, price, link) into variables
• Append CSV Row (or push to Google Sheets) with those variables
• Click Next with Smart HTML, add a short Delay, and loop back to the read step
• When Next is gone, the condition ends the loop and the file is complete
Keep it reliable and respectful
A scraper that runs once is easy; one that runs for months needs care. Add a small wait after every navigation so pages settle, and prefer selectors tied to meaningful structure over auto-generated class names that change on every deploy. Log progress — even a simple line per page — so when a run stops you know exactly where. Just as important, respect each site's terms of service and rate limits: space your requests out rather than hammering the server. Slower, human-paced requests are not only more polite, they are also far less likely to get your automation blocked.
Frequently asked questions
Do I need to know how to code?
No. You pick elements with a visual selector picker and assemble commands — no scripting required.
Will the scraper break when the website changes?
Much less than a coordinate-based one. Smart HTML targets elements by selector, so it survives most layout changes; if a site is redesigned heavily, you re-pick the affected element.
Can it export straight to Google Sheets?
Yes. Use the Google Sheets action to push each record into a shared sheet, or Append CSV Row for a local spreadsheet.
Is web scraping allowed?
Respect each site's terms of service and rate limits, and space out requests. Responsible, human-paced extraction is both more ethical and less likely to be blocked.
Where to go next
Start with a single page and a handful of fields, confirm the export looks right, then add the pagination loop. Once the pattern works, the same workflow scales to thousands of records with no extra effort. If you are new to building workflows at all, the getting started guide covers the basics first, and the Smart HTML docs go deeper on selectors.
