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 TrustRadius reviews data fields are sent to Make?

All 25 output fields from the TrustRadius Reviews scraper are included: url, product_id, product_name, review_id, review_url, review_title, and more. The full payload is sent as structured JSON — no fields are filtered out.

Can I schedule recurring TrustRadius 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 TrustRadius reviews into Make?

Each reviews 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.

TrustRadiusMake

How to Scrape TrustRadius Reviews into Make

Extract reviews data from TrustRadius and pipe into Make automatically

Step-by-step guide

Choose the TrustRadius Reviews scraper

Navigate to the TrustRadius Reviews 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 TrustRadius input URLs

Paste the TrustRadius 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 25 fields like url, product_id, product_name, review_id. Route the data to any downstream app — CRMs, databases, email tools, and more.

Cost per record

1 credit

Output fields

25 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

Product Name

sample_product_name

Review Id

sample_review_id

Review Url

sample_review_url

Review Title

sample_review_title

Review Rating

1,000

Review Date

sample_review_date

Review Author

sample_review_author

Author Position

sample_author_position

Author Company Name

sample_author_company_name

Author Company Industry

sample_author_company_industry

Author Company Size

sample_author_company_size

Author Labels

sample_author_labels

Author Experience Years

1,000

Review Text

Incredible food and atmosphere. The tasting menu was a perfect blend of innovation and comfort.

Review Summary

sample_review_summary

Likelihood To Recommend

sample_likelihood_to_recommend

Pros Cons

sample_pros_cons

Product Rating

1,000

Product Reviews Count

1,000

Product Url

sample_product_url

Usability Rating

1,000

Return On Investment

sample_return_on_investment

Input Url

https://www.linkedin.com/in/sarahchen

Data Dictionary

25 fields returned per record

TrustRadius product review page URL (100.00% fill rate)

Unique product identifier from the URL (100.00% fill rate)

The name of the product being reviewed (100.00% fill rate)

Unique identifier for each review (100.00% fill rate)

URL of the full review (100.00% fill rate)

The title of the review (100.00% fill rate)

Star rating of the review (1-10) (100.00% fill rate)

Date when the review was submitted (99.97% fill rate)

Name of the reviewer (56.24% fill rate)

Position/role of the review author (56.02% fill rate)

Company where the author works (56.05% fill rate)

Industry of the author's company (63.36% fill rate)

Size category of the author's company (63.39% fill rate)

Reviewer type and verification status labels (99.96% fill rate)

Years of experience of the reviewer (62.14% fill rate)

Full text of the review (100.00% fill rate)

Summary of the review (95.32% fill rate)

Reviewer's likelihood to recommend the product (99.05% fill rate)

List of pros and cons mentioned (20.52% fill rate)

Overall product rating on TrustRadius (100.00% fill rate)

Total number of reviews for the product (100.00% fill rate)

TrustRadius product page URL (100.00% fill rate)

Usability sub-rating (0.23% fill rate)

Reviewer's assessment of ROI (0.41% 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 TrustRadius Reviews into Make

Ready to connect TrustRadius data to Make?

Start extracting trustradius reviews data and pipe into Make in minutes.

Go to TrustRadius Reviews scraper Browse all integration guides

No code requiredAuto-delivery to Make25 data fields