The Scenario
Your recruiting agency delivers a talent market analysis to clients at the start of every engagement. The analysis includes a breakdown of active job postings — titles, salary ranges, remote status, which companies are hiring aggressively.
The analyst who built the last one left. You found their methodology in a shared doc: they manually visited sixty job board URLs every month and copied the data into a workbook. It took them about six hours.
The client presentation is in nine days. You have a list of sixty job posting URLs in column A of an Excel workbook and no one on the team has six hours to spend doing data entry.
The bad version:
- Open each job URL, copy the job title, switch to the workbook, paste it, switch back, copy the company name, switch to the workbook, paste it — fifty-eight more to go
- Discover that salary information is in three different formats across the sixty postings: some say "120,000 - 145,000," some say "up to 130k," some say "competitive" — and they all need to land in the same column
- Notice at row 40 that four of the postings have been taken down since the URL list was compiled, so those rows are just blank
The analysis needs sixty rows of clean, structured data. The client hired you to interpret the market, not to transcribe it.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that lives inside your Excel workbook. It reads the URL list, understands the data structure you need, and through its built-in ScrapingAnt integration it scrapes each job posting with AI extraction — pulling structured fields and writing them into your workbook in one pass.
Open the SheetXAI sidebar and ask:
Scrape all 60 job URLs in column A using ScrapingAnt and extract Job Title, Company, Location, Salary, and Remote flag into the next five columns of my Excel sheet
SheetXAI processes all sixty URLs through ScrapingAnt, extracts the five fields using AI-powered parsing, and writes each row. Salary ranges land in column E in whatever format the page used. Remote status lands in column F as a yes/no or as the exact phrase used on the posting.
What You Get
- Column B: job title as listed on the posting
- Column C: company name
- Column D: location (city, state, or "Remote" where specified)
- Column E: salary range in the format used by the posting
- Column F: remote status extracted from the job description
Rows where a posting has been removed get "Posting unavailable" in column B rather than a silent blank row.
What If the Data Is Not Quite Ready
Salary formats are inconsistent — you need a normalized annual figure in a separate column
After filling columns B through F, add a column G where each salary range is converted to an estimated annual midpoint in USD — treat "up to 130k" as 130000 and "120k - 145k" as 132500; leave "Competitive" as blank
Some job boards require a login to view full posting details
For each URL in column A, use ScrapingAnt headless Chrome rendering and attempt to extract the job posting fields; if the page returns a login wall, write "Login required" in column B and skip the remaining columns for that row
The URL list has jobs from three different boards — flag the source in a new column
For each URL in column A, extract the domain name and write it into column G as the source board label before filling the job data columns
Full market analysis pull in one prompt: scrape, extract, normalize, and flag
Scrape all 60 job URLs in column A with ScrapingAnt AI extraction; write job title into column B, company into column C, location into column D, salary range into column E, remote status into column F; in column G normalize salary to an estimated annual USD midpoint; in column H flag any posting older than 30 days as "Stale" using the posting date if available
One prompt replaces six hours of manual work and three cleanup passes.
Try It
If your job posting list is sitting in an Excel workbook waiting to become a market analysis — Get the 7-day free trial of SheetXAI and ask it to extract all five fields from every row using ScrapingAnt. Related: how to build a SaaS pricing comparison table, or back to the hub for the full ScrapingAnt use case list.