The Scenario
Your competitive pricing review is every Thursday. The workbook has 50 competitor product URLs in column A, and your job is to have current prices in column B and product names in column C before the 2 PM sync.
Last week you started at noon. You opened each URL, read off the price, typed it in. By 1:45 you had finished row 31, guessed at two prices where the page was loading slow, and skipped three where the pricing was buried behind a modal. The report went to the review half-populated.
The bad version:
- Open each URL in a browser tab, wait for JavaScript to render the page fully, then manually read the price — except four of the fifty pages block your IP after the third visit
- Copy the price figure, switch back to the workbook, paste it in, reformat the currency so it matches the existing column — repeat fifty times
- Realize at row 38 that one competitor changed their pricing structure since last week, so the column you have been filling is now comparing annual to monthly prices
Nobody hired you to transcribe numbers from web pages for two hours every Thursday. The actual analysis — the competitive positioning question — takes twenty minutes if you have clean data to work with. The data collection is what is eating your afternoon.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that lives inside your Excel workbook. It reads your data, understands your column layout, and through its built-in ScrapingAnt integration it scrapes each URL with headless Chrome rendering — which means JavaScript-rendered prices get captured, not just the initial HTML.
Open the SheetXAI sidebar and ask:
For each URL in column A (50 rows), use ScrapingAnt's AI extraction with the prompt "Extract product name and price" and write the extracted product name into column B and price into column C — use headless Chrome rendering for JavaScript-loaded prices
SheetXAI sends each URL through ScrapingAnt's headless Chrome renderer, applies the extraction prompt, and writes results back row by row. Column B fills with product names. Column C fills with prices. Rows where extraction fails get flagged with a note rather than silently left blank.
What You Get
- Column B filled with extracted product names, one per row, matching the URL in column A
- Column C filled with the scraped price figure as it appeared on the rendered page
- Rows where ScrapingAnt could not extract a price flagged with "Extraction failed — verify manually"
- No browser tabs open, no copy-paste, no reformatting loop
What If the Data Is Not Quite Ready
Some prices only appear after a currency selector loads
For each URL in column A, use ScrapingAnt headless Chrome with a 3-second wait before extraction, then extract the price shown in the default currency and write it into column C
Product names are inconsistent — some have SKU codes appended
Scrape all 50 URLs in column A with ScrapingAnt AI extraction, write the raw product name into column B, then clean column B so any trailing SKU codes in parentheses are stripped before saving
Competitors have separate regional pricing pages — column D has the UK variant URL
For each row where column D is not empty, use ScrapingAnt to scrape the URL in column D and write the GBP price into column E; use the column A URL as fallback and write USD price into column C
Full competitive snapshot: scrape, normalize, and flag outliers in one pass
Scrape all 50 product URLs in column A with ScrapingAnt headless Chrome, write product name into column B and price into column C, convert any non-USD prices using today's exchange rate into column D, then in column E flag any product where our price (column F) is more than 15% higher than theirs
The underlying pattern: combine the scrape, the cleanup, and the analysis into one ask.
Try It
Open your competitive pricing workbook — the one with URLs in column A and empty columns waiting for data — and Get the 7-day free trial of SheetXAI. Ask it to scrape those fifty pages with ScrapingAnt and fill in names and prices. Then check out the spoke on building a full SaaS pricing comparison table, or back to the hub overview for all ScrapingAnt use cases.