The Scenario
You run pricing intelligence for a retail brand. Column A of your Google Sheet has 20 SKU names. Your category manager asked for a Google Shopping competitive report before end of week — for each SKU, they want to see who's selling it and at what price, with the merchant name and product URL for the top 5 Shopping results.
You've done this manually before. It takes about 40 minutes to run 20 searches, record the results, and format the output. You have about 15 minutes before a different deadline hits.
The bad version:
- Search Google Shopping for SKU one, open Search API or the Shopping tab, copy the top 5 merchant names, prices, and URLs into the right row
- Repeat 19 times while making sure you haven't drifted a row due to a result set with fewer than 5 listings
- Reconcile the price formats because some come back as "$24.99" and others as "24.99" and your category manager wants them consistent
The category manager is presenting this to the merchandising team. A partially filled sheet with inconsistent price formats does not inspire confidence in the research.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent inside your Google Sheet. It reads your SKU list, understands what you're building, and through its built-in Search API integration it queries Google Shopping for each product name and writes the merchant data back in a consistent structure.
For each product name in column A, search Google Shopping using Search API and write the top 5 results — merchant name, price, and product URL — into columns B, C, D for result 1, E, F, G for result 2, and so on through column P.
What You Get
- Top-5 Google Shopping results for all 20 SKUs written across named columns, with merchant name, price, and URL in consistent groupings
- Prices written as returned by Search API so no manual normalization is needed before sorting
- Any SKU returning fewer than 5 Shopping results reflected accurately rather than padded with blank cells
What If the Data Is Not Quite Ready
You only want the single lowest-priced result per SKU rather than the full top 5
For each product name in column A, search Google Shopping using Search API, retrieve the top 10 results, and write only the merchant name and price of the lowest-priced listing into columns B and C.
Your SKU names in column A include internal codes that don't search well and you have consumer-facing names in column B
For each row, use the consumer product name in column B rather than the internal SKU code in column A to search Google Shopping using Search API — write the top 5 merchant names, prices, and URLs into columns C through R.
You want to pull Shopping results and immediately flag any merchant that's pricing more than 20% below your brand's MAP price listed in column B
For each product in column A, search Google Shopping using Search API and write the top 5 merchant names and prices into columns C through L, then compare each price to the MAP price in column B and mark column M with "MAP violation" if any of the top-5 prices are more than 20% below the MAP value.
You want to pull Shopping data, calculate the median market price per SKU, and produce a summary tab with SKUs ranked by how far off MAP the median sits
For each product in column A, search Google Shopping using Search API and retrieve the top 10 results with merchant name and price — write them into grouped rows below each SKU header, calculate the median price across all 10 results and write it in column B next to the SKU header, then create a new tab called "MAP Summary" that lists each SKU with its MAP price from column B of the main sheet, the median Shopping price, and a delta column sorted from largest MAP deviation to smallest.
Letting SheetXAI run both the data collection and the pricing analysis in one prompt cuts out two separate processing steps.
Try It
Get the 7-day free trial of SheetXAI and open your SKU pricing sheet, then ask it to pull Google Shopping competitor results for each product name using Search API. If you're also tracking Amazon pricing for the same SKUs, the Amazon product research spoke covers that workflow.