Valeria LedezmaIn 2026, multi location brands and SaaS SEO providers are under pressure to deliver faster insights from customer feedback. It is no longer enough to show “recent reviews.” Customers expect full historical context, sentiment trends, and location comparisons across regions. That requires bulk retrieval. The challenge is that review platforms are not designed for your reporting cadence. They throttle, they change layouts, and they vary by publisher.
The practical solution is to fetch reviews in bulk through a single request workflow that can retrieve a large volume for a location or profile. With the Local Data Exchange Business Reviews API, this is achieved through one job request payload that triggers retrieval across one or more publishers, with options designed for large review volumes such as lazy mode and full page retrieval.
What “one API call” means in this system
The Publisher Reviews API is job based. You submit a JSON formatted payload that includes a job key and a data object containing your API key, location identifiers, and publisher configuration .
The documentation describes the request structure and notes that requests are placed on SQS queues for production and staging . In other words, the “one call” is one job request that kicks off a potentially large retrieval process. This is the right model for fetching 1000 reviews because long running retrieval is handled asynchronously, not by holding open a synchronous HTTP connection.
The bulk retrieval settings that matter
To fetch a large number of reviews, there are three configuration ideas you should implement in your request pattern.
1) Use lazy for large retrievals
The docs describe lazy as a boolean that increases the timeout limit for a request, and it should be set to true when your request needs to scrape a large number of reviews .
If your goal is 1000 reviews, default to lazy: true. This is a simple operational switch that aligns directly to bulk retrieval.
2) Ensure full page retrieval is enabled
Publisher sites often paginate reviews. The docs define first_page_only and explain that when it is false, the system will retrieve reviews from all pages, and when true it only returns the first page of the latest reviews .
For 1000 review retrieval, set first_page_only: false. That instructs the retrieval process to go beyond the first page.
3) Start with an intentional backfill, then move to incremental
Bulk retrieval is most valuable at onboarding. After you have historical reviews, you should switch to incremental sync to keep data fresh without re-downloading everything.
The docs describe last_review_hashes as the mechanism to prevent returning all found results .
A proven pattern:
- First backfill request: omit
last_review_hashesor provide an empty array for each publisher. - Store the most recent review hashes returned.
- Next sync: send those hashes to retrieve only net new reviews.
This is how you keep your system performant while still being able to fetch large volumes of data when needed.
A reference payload pattern for bulk review retrieval
Below is a simplified pattern based on the documented job structure, showing the keys that matter for a bulk pull. Adapt publisher keys and profile URLs per your tenant.
{
"job": "App\\Jobs\\RequestReviews",
"data": {
"api_key": "YOUR_API_KEY",
"foreign_key": "location-12345",
"lazy": true,
"business": {
"id": "location-12345",
"name": "Example Brand",
"address": {
"street": "123 Main St",
"city": "San Francisco",
"state": "CA",
"zip": "94102",
"country": "USA"
}
},
"publishers": {
"google.com": {
"profile_key": "https://publisher.com/business/profile",
"first_page_only": false,
"last_review_hashes": []
}
}
}
}
The documentation provides the same overall structure, including job, api_key, foreign_key, lazy, and the publishers object with profile_key, last_review_hashes, and first_page_only .
Design for large retrieval outcomes and partial success
Bulk retrieval can fail in ways that look like success if you do not track outcomes.
The docs list status codes that become relevant at 1000 review scale:
429for too many requests bans due to retries504for timeouts530partial success where some reviews are present but not all532internal status indicating the task is part of a group attempting to retrieve a large number of reviews
Your platform should treat 530 and 532 as operational signals:
- For
532, expect multi step completion and reconcile when all tasks finish. - For
530, present a UI warning and schedule a follow up job to attempt completion.
This is how you prevent dashboards from undercounting without explanation.
Decode and index text correctly for SEO analytics
At bulk scale, the value is not only displaying review cards. It is enabling analysis.
The docs explicitly note that text and author_name are base64 encoded . Decode before indexing into your search engine or analytics pipeline.
Once decoded and stored, you can support 2026 SEO workflows:
- Detect location specific service keywords customers use
- Track topic shifts by quarter
- Identify “conversion friction” terms like wait time, billing, or cleanliness
- Feed insights into local landing page content updates and FAQs
2026 trend: bulk review retrieval as a foundation for AI workflows
In 2026, review automation is increasingly AI assisted, but AI only works when the dataset is complete. Fetching 1000 reviews is not a vanity metric. It is what makes:
- Reliable sentiment baselines possible
- Better anomaly detection possible
- More accurate topic clustering possible
Bulk retrieval also enables better benchmarking across locations, which is a core need for franchises and enterprise brands.
Putting it all together with Local Data Exchange
To fetch 1000 business reviews in one job request, use:
- A single RequestReviews payload
lazy: truefor large volume timeoutsfirst_page_only: falseto pull all pages- A backfill then incremental pattern using
last_review_hashes - Strong handling of
530and532statuses for large retrieval completion
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