The Scenario
It's Monday morning and the pricing review is Thursday. You have 200 competitor product URLs sitting in column A of your Excel workbook — products that overlap with your own catalog. Last quarter, someone spent two days pulling prices by hand. Before that, someone wrote a Python script that worked until the target sites changed their HTML. Now the script is broken and the person who wrote it is at a different company.
The bad version:
- Open each URL manually, find the price, type it into column B, find the stock status, type it into column C, move to the next row.
- Get to row 40 and realize three of the pages have changed their layout — the price is in a different element. Start over on those rows.
- Finish at 11 PM Thursday, twenty minutes before the review deck is due.
Two hundred rows of manual price lookup is not an analysis task. It's data collection, and data collection at that scale has a way of swallowing the entire week that was supposed to go toward the actual analysis.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that lives inside your Excel workbook. It reads the workbook, understands your URL column, and through its built-in ScrapingBee integration it can scrape all 200 product pages and write extracted fields back — without you configuring a single selector.
For each URL in column A (200 rows), use ScrapingBee data extraction with CSS selectors to pull the product price and availability status, writing price into column B and availability into column C — flag rows where extraction returned nothing with "SCRAPE_FAILED" in column D.
What You Get
- Column B filled with the scraped price for each product URL in column A — raw value as it appears on the page.
- Column C filled with the availability status: "In Stock," "Out of Stock," or the page's equivalent.
- Column D left blank for successful rows; "SCRAPE_FAILED" written for any URL that returned no data — so you know exactly which rows need follow-up.
- The full 200 rows processed in one pass, not row by row.
What If the Data Is Not Quite Ready
The prices come back in different formats — some with currency symbols, some without, some with commas as decimal separators
For each URL in column A, use ScrapingBee to extract the product price into column B, then normalize the value in column E — strip currency symbols, replace comma decimals with periods, and output a clean numeric value suitable for sorting.
Some URLs in column A are 404s or redirects — the scrape returns nothing and flags the row, but you need to know why
For each URL in column A flagged with "SCRAPE_FAILED" in column D, use ScrapingBee to attempt the request again and write the HTTP status code into column E — distinguish between 404s, 403s, and timeouts so I know which failures are dead links versus access denials.
You need to match scraped prices against your own catalog prices in column F to flag competitive gaps
After scraping prices into column B using ScrapingBee, compare each scraped price against the corresponding value in column F (our price) — write "UNDERCUT" into column G if the competitor price is lower, "PARITY" if within 5%, and "PREMIUM" if our price is lower.
Kill chain: scrape prices, normalize, match against catalog, and flag anything where a competitor is more than 10% cheaper
For each URL in column A, use ScrapingBee to extract the product price, normalize it to a clean numeric value, compare against our price in column F, and write a status into column G — flag any row where the competitor is more than 10% cheaper with "ACTION REQUIRED" so I have a ready-to-filter list for the pricing review.
When the numbers and the decision are both in the same prompt, you don't need two passes to get a usable output.
Try It
Get the 7-day free trial of SheetXAI and open any Excel workbook with competitor product URLs in column A, then ask it to scrape prices and stock status using ScrapingBee. See also: Extract Job Listing Data From JavaScript Pages Into an Excel workbook and the full ScrapingBee integration overview.