How do I connect Make to Scrapernode?

Use Make's webhook trigger to receive data from Scrapernode. Copy the webhook URL from Make and paste it into Scrapernode's webhook settings for the scraper. Each completed job sends results automatically.

What Google Shopping products data fields are sent to Make?

All 17 output fields from the Google Shopping Products scraper are included: url, product_id, title, product_description, rating, reviews_count, and more. The full payload is sent as structured JSON — no fields are filtered out.

Can I schedule recurring Google Shopping scrapes to Make?

Yes. Use Make's scheduling features to trigger Scrapernode scrapes on a recurring basis via the REST API. You can also set up Make to process results as they arrive via webhooks.

How much does it cost to scrape Google Shopping products into Make?

Each products record costs 1 credit. Credits start at $9 for 3,000 credits (Starter plan). The Make integration itself is included free — no additional charges for data delivery.

What happens if the Make delivery fails?

Your scraped data is always stored in Scrapernode regardless of the integration status. If delivery to Make fails, you can re-trigger the sync or download results manually as CSV or JSON from the job details page.

Google ShoppingMake

How to Scrape Google Shopping Products into Make

Extract products data from Google Shopping and pipe into Make automatically

Step-by-step guide

Choose the Google Shopping Products scraper

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

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".

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.

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.

Receive data in Make

When the job completes, Scrapernode sends the full results to your Make workflow. Each record includes 17 fields like url, product_id, title, product_description. Route the data to any downstream app — CRMs, databases, email tools, and more.

Cost per record

1 credit

Output fields

17 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

Product Description

sample_product_description

Rating

4.5

Reviews Count

457

Images

sample_images

Variations

sample_variations

Initial Price

sample_initial_price

Final Price

sample_final_price

Currency

sample_currency

Product Condition

sample_product_condition

Sellers

sample_sellers

Product Specifications

sample_product_specifications

Product Reviews

sample_product_reviews

Data Dictionary

17 fields returned per record

The URL or link to the product (100.00% fill rate)

Unique identifier for the product (85.04% fill rate)

Title or name of the product (98.67% fill rate)

Description of the product (53.02% fill rate)

Average rating of the product (71.21% fill rate)

Number of ratings or reviews for the product (38.59% fill rate)

Images of the product (98.65% fill rate)

Product variations (37.48% fill rate)

Sub-fields

nameTextVariation name

valueTextVariation value

Original price before discounts (14.09% fill rate)

Final price after discounts (97.37% fill rate)

Currency of the price (97.37% fill rate)

Condition of the product (new, used, etc.) (49.99% fill rate)

List of sellers offering the product (92.36% fill rate)

Sub-fields

nameTextSeller name

linkTextLink to seller

priceTextPrice from this seller

ratingNumberSeller rating

reviewsNumberNumber of seller reviews

conditionTextProduct condition from this seller

Technical specifications of the product (75.42% fill rate)

Sub-fields

specification_nameTextName of the specification

specification_valueTextValue of the specification

Customer reviews for the product (52.11% fill rate)

Sub-fields

nameTextReviewer name

ratingNumberReview rating

dateTextReview date

textTextReview text

Product categories (98.65% fill rate)

Sub-fields

nameTextCategory name

urlTextCategory URL

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 Products into Make

Ready to connect Google Shopping data to Make?

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

Go to Google Shopping Products scraper Browse all integration guides

No code requiredAuto-delivery to Make17 data fields