Argos Product Reviews Scraper | Extract Customer Ratings & Feedback (UK)

Scrape Argos reviews, ratings, photos, and seller responses from any Argos UK product page — clean, analysis-ready data for sentiment analysis, competitor research, and e-commerce analytics.

Paste a product URL → choose Max Mode (maximize unique coverage) or filters → get structured results you can export as JSON, CSV, or Excel.

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60-second overview

Goal	What to set
Max coverage	maxMode = true
Quick sample (e.g. latest 30)	maxMode = false, sort newest, maxreviews = 30
Only 1★ complaints	maxMode = false, starRatings = ["1"]
Broad coverage without Max Mode	Select all sorts × star ratings 1–5 (see below)

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Why use this Argos review scraper?

• Max Mode — maximizes unique coverage; often the full set for typical products. High-volume SKUs may be partial due to site limits.
• Rich fields — stars, review text, photos, helpful votes, secondary ratings, seller replies, and rating breakdowns.
• Analysis-ready output — product stats (Average_Rating, Total_Reviews, star counts) included on every row, plus an optional product_summary row.
• Flexible filters — newest / oldest / most helpful / highest / lowest × 1–5 stars.
• Easy exports — run on Apify, push to Dataset, download CSV/Excel/JSON for dashboards (Power BI, Looker, Sheets) or Python.

Ideal for: product managers, brand analysts, market research, VOC / voice-of-customer, assortment teams, and AI/NLP pipelines.

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Max Mode — maximize unique coverage

Turn maxMode ON when you want the broadest unique review set for a product.

Max Mode maximizes unique coverage — often the full set for typical products. High-volume SKUs may be partial due to site limits.

While Max Mode is ON, sort and star filters are ignored — the actor focuses on maximizing coverage automatically.

{
  "URL": "https://www.argos.co.uk/product/8086071",
  "maxMode": true,
  "maxreviews": 10000
}

Tip: keep maxreviews high (e.g. 10000) so the run does not stop early.

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Filters (Max Mode OFF)

When maxMode = false, you control exactly what is collected using sort × star combinations.

Available sorts

Input value	Meaning
newest	Most recent reviews
oldest	Oldest first
most_helpful	Most helpful
highest_rating	Highest star first
lowest_rating	Lowest star first

Star ratings

all · 5 · 4 · 3 · 2 · 1

Examples

Latest reviews only:

{
  "URL": "https://www.argos.co.uk/product/8086071",
  "maxMode": false,
  "sorts": ["newest"],
  "starRatings": ["all"],
  "maxreviews": 100
}

Only negative feedback (1–2 stars):

{
  "URL": "https://www.argos.co.uk/product/8086071",
  "maxMode": false,
  "sorts": ["newest"],
  "starRatings": ["1", "2"],
  "maxreviews": 2000
}

Most helpful + highest rated:

{
  "URL": "https://www.argos.co.uk/product/8086071",
  "maxMode": false,
  "sorts": ["most_helpful", "highest_rating"],
  "starRatings": ["all"],
  "maxreviews": 2000
}

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Get broad / near-complete coverage without Max Mode

You can still aim for wide coverage with filters alone:

1. Set maxMode to false
2. Select all 5 sorts
3. Select stars 5, 4, 3, 2, 1 (or all)
4. Set maxreviews high enough

{
  "URL": "https://www.argos.co.uk/product/8086071",
  "maxMode": false,
  "sorts": ["newest", "oldest", "most_helpful", "highest_rating", "lowest_rating"],
  "starRatings": ["5", "4", "3", "2", "1"],
  "maxreviews": 10000
}

Duplicates are removed automatically. For the broadest pull with the least setup, Max Mode is the simplest choice.

Approach	Best for
Max Mode ON	Maximize unique coverage / VOC dumps / model training
Filters (Max Mode OFF)	Targeted pulls — complaints, latest only, most helpful

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Input reference

Field	Required	Description
URL	Yes	Argos UK product URL (must include /product/{partNumber})
maxMode	No (default true)	true = maximize unique coverage; false = use sort/star filters
sorts	No	Multi-select; used only when maxMode is false
starRatings	No	all or 1–5; used only when maxMode is false
maxreviews	No	Maximum unique reviews to keep (e.g. 30 for a quick test)

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Output — sample review object

Each dataset item is one review (or a product_summary row first), with review details and product-level stats:

{
  "Record_Type": "review",
  "Review_ID": "123456789",
  "Title": "Great product",
  "ReviewText": "Really happy with this purchase...",
  "Rating": 5,
  "SubmissionDate": "2026-08-11",
  "LastModificationDate": "2026-08-12",
  "UserNickname": "Chris",
  "UserLocation": "London",
  "PartNumber": "8086071",
  "Product_URL": "https://www.argos.co.uk/product/8086071",
  "ProductName": "Example Product Name",
  "Brand": "Example Brand",
  "IsRecommended": true,
  "IsRatingsOnly": false,
  "IsFeatured": false,
  "Helpfulness": 0.85,
  "TotalPositiveFeedbackCount": 12,
  "TotalNegativeFeedbackCount": 2,
  "PhotoCount": 1,
  "PhotoUrls": ["https://example.com/photo.jpg"],
  "HasSellerResponse": false,
  "Review_Type": "with_review_text",
  "With_Review": "Yes",
  "Sort": "newest",
  "Star_Filter": "all",
  "Total_Reviews": 6359,
  "Average_Rating": 4.85,
  "RecommendedCount": 6100,
  "NotRecommendedCount": 200,
  "RecommendPercent": 96.83,
  "Product_5_Star_Count": 5682,
  "Product_4_Star_Count": 529,
  "Product_3_Star_Count": 78,
  "Product_2_Star_Count": 29,
  "Product_1_Star_Count": 41
}

Download from the Apify Dataset as JSON / CSV / Excel.

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What analysis can you run on this data?

1. Rating & quality monitoring

• Track Average_Rating and star mix (Product_1_Star_Count … Product_5_Star_Count)
• Spot rating changes after product updates or new listings

2. Sentiment & text mining

• Run sentiment on ReviewText / Title
• Find common complaints vs praise themes
• Compare language in 1–2★ vs 4–5★ reviews

3. Trust & authenticity signals

• Use IsRecommended, IsRatingsOnly, IsFeatured, helpfulness votes
• Separate rating-only rows (Review_Type) from written reviews

4. Photo & UGC research

• Use PhotoCount / PhotoUrls for visual customer content

5. Brand response quality

• Measure reply rate with HasSellerResponse
• Compare SubmissionDate vs SellerResponse_Date

6. Competitor & assortment research

• Run multiple Argos product URLs
• Benchmark volume, average rating, and negative-review share across brands

7. Trends over time

• Chart review volume by SubmissionDate
• Detect launch spikes or seasonal patterns

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Tips

• Prefer Max Mode when you want maximum unique coverage (full set for typical products; high-volume SKUs may be partial due to site limits).
• Use filters + a small maxreviews for cheap tests or “latest only” dashboards.
• One URL = one product; run multiple times (or a workflow) for category research.
• Some fields may be empty when the site does not provide them (e.g. location, seller reply) — that is normal.

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Keywords

Argos reviews scraper · Argos product reviews · scrape Argos ratings · Argos customer feedback · extract Argos review text · Argos sentiment analysis data · e-commerce review scraping Apify · VOC competitor analysis · Argos UGC photos · Argos UK star ratings dataset

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Built for teams who need reliable Argos UK review data — without manual copy-paste.