Ace Hardware Product Reviews Scraper

Extract public Ace Hardware product reviews into clean JSON, CSV, Excel, XML, or RSS records.

Use an Ace product URL or item ID to collect review text, ratings, authors, dates, recommendation signals, helpfulness, media, syndication, and product-level rating statistics.

No Ace login, private API key, or browser automation is required.

What does Ace Hardware Product Reviews Scraper do?

Ace Hardware Product Reviews Scraper turns public customer feedback from Ace Hardware product pages into analysis-ready records.

It can:

• 🔗 Accept full Ace product URLs or numeric item IDs.
• 📄 Paginate through every available public review.
• 🎯 Filter by stars, recommendation, and submission date.
• ↕️ Sort by date, rating, or helpfulness.
• 🖼️ Capture review photos, videos, badges, and merchant responses.
• 📊 Add product metadata and aggregate rating distributions.
• 🕐 Support scheduled incremental monitoring with date filters.

The Actor uses Ace's public structured review feed rather than a heavy browser, which keeps runs fast and predictable.

Who is it for? Ace review monitoring teams

Brands and Ace vendors

• Monitor feedback on products sold through Ace.
• Find recurring defects and packaging complaints.
• Track recommendation rates after a product update.

Product-quality teams

• Review one- and two-star comments in a shared spreadsheet.
• Compare rating distributions across item IDs.
• Route new negative feedback into an issue tracker.

Retail and ecommerce analysts

• Build recurring product-review datasets.
• Compare public sentiment across product lines.
• Preserve timestamped review snapshots for trend analysis.

Agencies and data teams

• Export normalized Ace reviews for clients.
• Feed review text into sentiment or topic models.
• Automate collection through the API, schedules, or webhooks.

Why use this Ace Hardware review extractor?

• ✅ No login required — collect public reviews without an Ace account.
• ✅ HTTP-first extraction — no browser overhead for the core workflow.
• ✅ One row per review — easy to filter, join, deduplicate, and export.
• ✅ Product context included — name, brand, image, MPN, UPC, and aggregate statistics.
• ✅ Monitoring filters — pull only reviews submitted in a chosen period.
• ✅ Failure isolation — one unavailable item does not discard successful products.
• ✅ Apify integrations — use schedules, API calls, webhooks, Make, Zapier, and cloud storage.

What Ace Hardware review data can you extract?

Category	Fields
Product identity	Product ID, name, brand, URL, image, category
Product identifiers	Manufacturer part numbers and UPCs
Product summary	Average rating, total reviews, recommended/not-recommended counts
Rating distribution	Counts for one through five stars
Review content	Review ID, title, full text, rating, rating range
Reviewer	Nickname, author ID, location, locale
Timing	Submission time, last modification time, scrape time
Signals	Recommended, rating-only, featured, syndicated
Engagement	Helpful, unhelpful, inappropriate, and comment counts
Rich content	Badges, context, secondary ratings, photos, videos
Responses	Merchant responses and syndication source

Optional source fields are omitted when Ace does not provide them; numeric counters remain numbers and booleans remain booleans.

How much does it cost to scrape Ace Hardware reviews?

This Actor uses pay-per-event pricing: a small run-start charge plus one charge per review saved.

Plan tier	Current price per review
Free	$0.000039795
Starter / Bronze	$0.000034605
Scale / Silver	$0.000026992
Business / Gold	$0.000020763
Platinum	$0.000013842
Diamond	$0.000010000

A run also has a $0.005 start event.

Example at the Free tier:

• 10 reviews: about $0.0054.
• 100 reviews: about $0.0090.
• 1,000 reviews: about $0.0448.

Final cost depends on the number of matching reviews emitted, not the number of source pages checked.

How to scrape Ace Hardware product reviews

1. Open Ace Hardware Product Reviews Scraper on Apify.
2. Paste one or more Ace product URLs into Product URLs.
3. Alternatively, enter numeric values under Item IDs.
4. Set the maximum reviews per product.
5. Optionally choose rating, recommendation, and date filters.
6. Click Start.
7. Preview records in the Dataset tab.
8. Export the dataset as JSON, CSV, Excel, XML, or RSS.

Start with the prefilled Bosch drill URL to verify the workflow quickly.

Input parameters

Parameter	Type	Default	Purpose
productUrls	string array	sample URL in prefill	Ace Hardware product page URLs
productIds	string array	[]	Numeric Ace item IDs
maxReviewsPerProduct	integer	100	Maximum matching rows per product
sort	string	newest	Date, rating, or helpfulness order
ratings	string array	[]	Selected ratings from 1 to 5
recommended	string	all	All, recommended, or not recommended
submittedAfter	string	empty	Inclusive ISO date lower bound
submittedBefore	string	empty	Inclusive ISO date upper bound
includeRatingsOnly	boolean	true	Keep reviews without written text
maxRequestRetries	integer	3	Bounded retries for temporary errors

Provide at least one valid Ace URL or item ID.

Invalid dates and invalid rating values fail closed instead of silently widening the query.

Input examples

Collect the 100 newest reviews:

{
  "productIds": ["2398303"],
  "maxReviewsPerProduct": 100,
  "sort": "newest"
}

Collect negative reviews from a product URL:

{
  "productUrls": [
    "https://www.acehardware.com/departments/tools/power-tools/corded-drills/2398303"
  ],
  "ratings": ["1", "2"],
  "recommended": "notRecommended",
  "maxReviewsPerProduct": 250
}

Monitor feedback submitted during 2026:

{
  "productIds": ["2398303"],
  "submittedAfter": "2026-01-01",
  "submittedBefore": "2026-12-31",
  "sort": "newest"
}

Output example

Each row represents one review:

{
  "productId": "2398303",
  "productName": "Bosch 7 amps 1/2 in. Corded Hammer Drill",
  "productBrand": "Bosch",
  "productUrl": "https://www.acehardware.com/p/2398303",
  "averageRating": 3.2972972972972974,
  "totalReviewCount": 74,
  "rating1Count": 22,
  "rating5Count": 32,
  "reviewId": "300170121",
  "rating": 1,
  "ratingRange": 5,
  "reviewTitle": "Fails to perform its base task.",
  "nickname": "Ashlela",
  "submissionTime": "2026-04-18T17:58:10.000+00:00",
  "isRecommended": false,
  "helpfulVotes": 0,
  "unhelpfulVotes": 0,
  "photoUrls": ["https://photos-us.bazaarvoice.com/photo/example"],
  "isSyndicated": true,
  "syndicationSource": "boschtools.com",
  "contentLocale": "en_US",
  "scrapedAt": "2026-07-16T00:00:00.000Z"
}

Exact optional fields vary because reviewers do not always add text, location, media, or recommendation status.

Tips for reliable Ace review extraction

• Start with one item and a low limit before submitting a large product list.
• Use item IDs when you already have a catalog export; they avoid URL formatting issues.
• Use sort: newest with submittedAfter for recurring monitoring.
• Remember that the maximum applies to matching reviews after filters.
• Use one- and two-star filters to build a product-quality alert stream.
• Deduplicate historical exports by reviewId.
• Schedule runs at a cadence appropriate for each product's review volume.
• Keep rating-only records when calculating rating distributions.

Scheduling review monitoring

Apify schedules can run this Actor hourly, daily, weekly, or on a custom cron expression.

A practical workflow is:

1. Store tracked item IDs in task input.
2. Set sort to newest.
3. Set submittedAfter to the start of your monitoring window.
4. Run the task daily or weekly.
5. Deduplicate rows downstream by reviewId.
6. Trigger alerts for low ratings or isRecommended: false.

For a rolling integration, update the date input through the API before each run.

Integrations

Ace reviews → Google Sheets

Append newly submitted reviews to a quality dashboard and build pivot tables by product, star rating, or recommendation.

Ace reviews → Slack or Discord

Send alerts whenever a scheduled run returns a one-star review or a not-recommended product experience.

Ace reviews → Make or Zapier

Route records to Airtable, Notion, HubSpot, or an issue tracker without writing code.

Ace reviews → data warehouse

Use a webhook after each run, fetch the default dataset, and load rows into BigQuery, Snowflake, or PostgreSQL.

Ace reviews → AI analysis

Send reviewText into a topic, sentiment, complaint-classification, or product-defect workflow.

API usage with the Apify API

Node.js with apify-client:

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('automation-lab/ace-hardware-product-reviews-scraper').call({
  productIds: ['2398303'],
  maxReviewsPerProduct: 100,
  sort: 'newest'
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

Python with apify-client:

import os
from apify_client import ApifyClient

client = ApifyClient(os.environ['APIFY_TOKEN'])
run = client.actor('automation-lab/ace-hardware-product-reviews-scraper').call(run_input={
    'productIds': ['2398303'],
    'maxReviewsPerProduct': 100,
    'sort': 'newest',
})
items = client.dataset(run['defaultDatasetId']).list_items().items
print(items)

cURL:

curl -X POST \
  "https://api.apify.com/v2/acts/automation-lab~ace-hardware-product-reviews-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"productIds":["2398303"],"maxReviewsPerProduct":100,"sort":"newest"}'

Use with AI agents via MCP

This Actor can be used by AI assistants through Apify's Model Context Protocol integration.

Claude Code:

$claude mcp add --transport http apify "https://mcp.apify.com?tools=automation-lab/ace-hardware-product-reviews-scraper"

Claude Desktop, Cursor, or VS Code MCP configuration:

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com?tools=automation-lab/ace-hardware-product-reviews-scraper"
    }
  }
}

Example prompts:

• “Use automation-lab/ace-hardware-product-reviews-scraper to collect the newest 100 reviews for Ace item 2398303.”
• “Extract one- and two-star Ace reviews from these product URLs and summarize recurring product defects.”
• “Monitor not-recommended Ace Hardware reviews submitted since the start of this month.”

OAuth connects the MCP server to your Apify account on first use.

Export formats and storage

The default dataset supports:

• JSON for applications and APIs.
• CSV or Excel for analysts and spreadsheet users.
• XML and RSS for compatible integration tools.
• JSONL for streaming and machine-learning pipelines.

Apify datasets can be retained, named, downloaded, or fetched through API endpoints.

The RUN_SUMMARY key-value-store record reports processed products, failed products, saved reviews, and completion time.

Legality: is it legal to scrape Ace Hardware reviews?

This Actor extracts public product-review content that is visible without a login.

You are responsible for using the data lawfully and ethically, respecting applicable laws, Ace Hardware's terms, copyright, privacy rights, and data-protection obligations.

Avoid republishing personal data unnecessarily, do not use review data for harassment or discrimination, and retain only the fields needed for your legitimate purpose.

This documentation is not legal advice. Consult qualified counsel when your use case involves regulated decisions or personal-data processing.

Troubleshooting

Why did the run return no reviews?

Confirm the item ID belongs to an Ace Hardware product and that public reviews exist. Also check whether rating, recommendation, or date filters exclude all source records.

Why was a product URL skipped?

Only acehardware.com product URLs with a numeric item ID are accepted. Search/category URLs and links from other domains are rejected to preserve product scope.

Why are some optional fields absent?

Ace reviewers do not always provide text, location, recommendation, media, context answers, or secondary ratings. Missing optional source values are omitted rather than invented.

How fast is the scraper?

The structured API retrieves up to 100 source reviews per request. Most single-product runs finish in seconds, with duration increasing for large product lists and deep histories.

FAQ

Can I scrape multiple Ace products in one run?

Yes. Add multiple URLs, item IDs, or both. Duplicate item IDs are processed once.

Can I fetch all reviews?

Yes. Set maxReviewsPerProduct high enough for the product's review count, up to 5,000.

Can I extract only negative reviews?

Yes. Set ratings to ["1", "2"], and optionally set recommended to notRecommended.

Does this use an official Ace API?

It uses the public structured review service loaded by Ace product pages. It does not require a private Ace account or user-supplied API credential.

How are duplicates handled?

The Actor deduplicates emitted rows by the public reviewId during each run. Use the same field to deduplicate across scheduled runs.

Can I use the free Apify plan?

Yes. Small runs fit comfortably within typical free-platform credits; exact capacity depends on current Apify plan pricing and your other usage.

Related ecommerce scrapers

Explore other automation-lab tools:

• Walmart Reviews Scraper
• Target Product Reviews Scraper
• Lowe's Product Reviews Scraper
• Best Buy Reviews Scraper
• PetSmart Product Reviews Scraper
• Ulta Product Reviews Scraper
• Amazon Reviews Scraper

Use consistent identifiers and timestamps to combine these retailer-specific review datasets in a shared analytics pipeline.

Data-quality notes

Ratings and counts come directly from the public Ace review service at run time.

Syndicated reviews may have an original source different from Ace; check isSyndicated and syndicationSource when attribution matters.

Aggregate rating statistics describe the complete product review set at scrape time, even when the run emits only a filtered subset.

The Actor never fabricates missing reviewer fields, recommendation values, or media URLs.