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ScrapeLinkedInLinkedInprofiles by search term
Scrape LinkedIn profiles by search term. Extract Google Maps business listings and reviews. Collect Facebook pages, groups and posts. Scrape Instagram profiles, reels and comments. Extract TikTok posts and creator profiles. Collect YouTube channels and video data. Scrape X / Twitter profiles and posts. Extract Indeed job listings and salaries. Collect Yelp business reviews and ratings.
Home/Integrations/How to Scrape Google Shopping Search into Make
Google ShoppingMake

How to Scrape Google Shopping Search into Make

Extract search data from Google Shopping and pipe into Make automatically

Step-by-step guide

1

Choose the Google Shopping Search scraper

Navigate to the Google Shopping Search scraper in Scrapernode. Select "Fresh Scrape" for real-time data or "Quick Lookup" for pre-collected records. Each record costs 1 credit.

2

Set up your Make connection

Create a new Make scenario with a Webhook trigger module. Copy the webhook URL into Scrapernode's webhook settings. Use Make's built-in JSON parser to map scraper fields to downstream modules like "Create Spreadsheet Row" or "Send Email".

3

Provide your Google Shopping input URLs

Paste the Google Shopping URLs you want to scrape — one per line, or upload a CSV. Scrapernode accepts direct profile links, search result URLs, and content pages.

4

Launch the scraping job

Click "Start Extraction" to begin. Scrapernode handles proxy rotation, rate limiting, and anti-bot detection automatically. Jobs typically complete in under 60 seconds per batch.

5

Receive data in Make

When the job completes, Scrapernode sends the full results to your Make workflow. Each record includes 15 fields like url, product_id, title, final_price. Route the data to any downstream app — CRMs, databases, email tools, and more.

Cost per record

1 credit

Output fields

15 fields

Destination

Make

Sample Output

Preview the data you'll receive — 5 sample records

Record 1 of 5
Url
sample_url
Product Id
sample_product_id
Title
How We Scaled to 1M Users
Final Price
1,000
Initial Price
1,000
Currency
sample_currency
Rating
4.5
Reviews Count
457
Seller Name
sample_seller_name
Delivery
sample_delivery
Thumbnail
sample_thumbnail
Product Condition
sample_product_condition
Ad
Yes
Position
Software Engineer III
Input Url
https://www.linkedin.com/in/sarahchen

Data Dictionary

15 fields returned per record

The URL of the product listing page (100.00% fill rate)

Unique identifier for the product (100.00% fill rate)

The product title (78.58% fill rate)

The final price after discounts or promotions (80.17% fill rate)

The original price before discounts (23.54% fill rate)

The currency of the product price (100.00% fill rate)

The average customer rating of the product (44.34% fill rate)

The total number of reviews the product has (50.73% fill rate)

Name of the seller (91.44% fill rate)

Delivery information (75.32% fill rate)

Thumbnail image URL for the product (99.98% fill rate)

Condition of the product (new, used, etc.) (5.84% fill rate)

Whether the listing is an advertisement (100.00% fill rate)

Position of the product in search results (100.00% fill rate)

The URL that was entered when starting the scraping process (100.00% fill rate)

Frequently Asked Questions

Common questions about How to Scrape Google Shopping Search into Make

Ready to connect Google Shopping data to Make?

Start extracting google shopping search data and pipe into Make in minutes.

Go to Google Shopping Search scraperBrowse all integration guides
No code requiredAuto-delivery to Make15 data fields