Flipkart Reviews Scraper

Pull customer reviews from any Flipkart product at scale — star ratings, full review text, certified-buyer verification, reviewer photos, helpful/not-helpful vote counts, and reviewer location, all in clean structured rows. Built for product managers, brand analysts, and e-commerce teams who need the unfiltered voice of India's largest online marketplace without copying reviews off the page one at a time.

Why This Scraper?

• 18 data points on every review — review text, 1–5 star rating, headline, reviewer name, reviewer location (city + state), certified-buyer flag, helpful and not-helpful vote counts, review date, attached reviewer photos, and direct review URL.
• Reviewer photos included — every photo a customer attached to their review is returned as a high-resolution image URL, so you can mine real product-in-use shots, not just text.
• Certified-buyer verification on every row — each review carries Flipkart's verified-purchase badge as a true/false field, so you can separate confirmed buyers from the rest in one filter.
• Four sort orders — collect reviews as Most Helpful, Most Recent, Positive First, or Negative First to pull exactly the slice you need (top complaints, freshest feedback, or best praise).
• Star-rating and certified-buyer filters — narrow a run to only 1, 2, 3, 4, or 5-star reviews (any combination), and optionally keep verified purchasers only.
• Full review history — set the cap to 0 and the scraper walks the full review history (up to about 100,000 reviews per product); flagship phones and bestsellers carry 9,000+ reviews and it collects the complete set, deduplicating as it goes.
• Every review is self-describing — each row carries its parent product's name, ID, URL, average rating, total rating count, and total review count, so a single spreadsheet needs no joins.
• Paste product OR review-page URLs — works with the normal product page (the one with the price and Add to Cart) or the dedicated All Reviews page; no need to hunt down the reviews link first.

Use Cases

Product & Market Research

• Surface the features customers praise and complain about across a product line
• Compare sentiment on your product versus competing listings side by side
• Track how ratings shift after a relaunch, price change, or new variant
• Mine feature requests and recurring pain points straight from buyers

Brand & Reputation Monitoring

• Watch new negative reviews on your flagship products as they land
• Quantify the share of 1 and 2-star reviews over time
• Spot quality or counterfeit complaints early through Negative First sorting

E-commerce & Seller Intelligence

• Benchmark a catalog of competitor SKUs by average rating and review volume
• Identify high-converting products by review depth and helpful-vote counts
• Gauge demand and satisfaction before sourcing or listing a new product

Voice-of-Customer & CX Analytics

• Feed certified-buyer reviews into sentiment and topic-modeling pipelines
• Segment feedback by reviewer location to spot regional satisfaction gaps
• Prioritize support and roadmap work by helpful-vote-weighted complaints

AI & LLM Training Data

• Build labeled review datasets with ratings, verified-purchase flags, and vote counts
• Assemble image-plus-text pairs from reviews that include reviewer photos
• Create domain-specific corpora for e-commerce recommendation and summarization models

Getting Started

Scrape a Single Product

Paste a product URL and take the defaults (100 most-helpful reviews):

{
    "productUrls": ["https://www.flipkart.com/apple-iphone-15-black-128-gb/p/itm6ac6485515ae4"]
}

Newest Negative Reviews Only

Pull the freshest 1 and 2-star reviews — ideal for catching emerging complaints:

{
    "productUrls": ["https://www.flipkart.com/apple-iphone-15-black-128-gb/p/itm6ac6485515ae4"],
    "sortBy": "MOST_RECENT",
    "ratingFilter": ["1", "2"],
    "maxReviewsPerProduct": 200
}

Full Catalog Sweep

Collect every review from several products, verified buyers only, photos included:

{
    "productUrls": [
        "https://www.flipkart.com/apple-iphone-15-black-128-gb/p/itm6ac6485515ae4",
        "https://www.flipkart.com/samsung-galaxy-s24/p/itm7bd1234567890",
        "https://www.flipkart.com/boat-airdopes-141/product-reviews/itm9ce0987654321"
    ],
    "maxReviewsPerProduct": 0,
    "sortBy": "MOST_HELPFUL",
    "certifiedBuyersOnly": true,
    "includeImages": true
}

Input Reference

Products

Parameter	Type	Default	Description
productUrls	array	[]	One or more Flipkart product page or review page URLs. Both the product page (with price and Add to Cart) and the dedicated All Reviews page work. Each URL is processed independently.

Options

Parameter	Type	Default	Description
maxReviewsPerProduct	integer	100	Maximum reviews to collect per product. Set to 0 to collect every available review. A sensible cap (200–1000) keeps runs fast and predictable.
sortBy	select	Most Helpful	Order reviews are collected in: Most Helpful, Most Recent, Positive First, or Negative First.
ratingFilter	array	[] (all)	Keep only reviews with the chosen star ratings: 5 stars, 4 stars, 3 stars, 2 stars, 1 star. Leave empty to keep every rating.
certifiedBuyersOnly	boolean	false	When on, keep only reviews written by Flipkart Certified Buyers (verified purchasers).
includeImages	boolean	true	Include the URLs of any photos reviewers attached. Turn off for lighter output.

Output

Each row is one review, enriched with its parent product's context. Here is a representative result:

{
    "productName": "Apple iPhone 15 (Black, 128 GB)",
    "productId": "MOBGTAGPTB3VS24W",
    "productUrl": "https://www.flipkart.com/apple-iphone-15-black-128-gb/p/itm6ac6485515ae4",
    "productRating": 4.6,
    "productRatingCount": 184250,
    "productReviewCount": 12480,
    "reviewId": "a1b2c3d4e5f6",
    "rating": 5,
    "title": "Superb camera and battery",
    "reviewText": "Using it for two weeks now. The camera quality is excellent in daylight and the battery easily lasts a full day of heavy use.",
    "author": "Rahul Sharma",
    "reviewLocation": "Pune, Maharashtra",
    "certifiedBuyer": true,
    "helpfulCount": 342,
    "downvoteCount": 18,
    "reviewDate": "Aug, 2024",
    "reviewImages": [
        "https://rukminim2.flixcart.com/image/720/720/review/example.jpg?q=90"
    ],
    "reviewUrl": "https://www.flipkart.com/apple-iphone-15-black-128-gb/product-reviews/itm6ac6485515ae4"
}

Review Fields

Field	Type	Description
reviewId	string	Unique review identifier (used for deduplication)
rating	number	This review's star rating, 1 to 5
title	string	Review headline
reviewText	string	Full review body
author	string	Reviewer's display name
reviewLocation	string	Reviewer's city and state, when stated
certifiedBuyer	boolean	Whether the reviewer is a Flipkart Certified Buyer
helpfulCount	number	Count of "helpful" upvotes
downvoteCount	number	Count of "not helpful" downvotes
reviewDate	string	Review date as shown on Flipkart (e.g. "Aug, 2024")
reviewImages	array	High-resolution URLs of photos attached to the review
reviewUrl	string	Direct link to this review

Product Context

Attached to every review row, so each row stands on its own.

Field	Type	Description
productName	string	Parent product title
productId	string	Flipkart product identifier
productUrl	string	Canonical product page URL
productRating	number	Product's overall average star rating
productRatingCount	number	Total number of ratings on the product
productReviewCount	number	Total number of text reviews on the product

Tips for Best Results

• Start with a cap of 50–100 to confirm the data fits your needs, then raise maxReviewsPerProduct or set it to 0 for the full history.
• Set maxReviewsPerProduct to 0 for a complete review export — the scraper paginates to the very last review and deduplicates by review ID, so a bestseller with thousands of reviews comes back complete with no repeats.
• Pair ratingFilter with sortBy for sharp slices — for example, ["1", "2"] with Most Recent surfaces the newest complaints first; ["5"] with Most Helpful surfaces your strongest testimonials.
• Narrow rating filters scan more reviews to reach the cap — asking for only 1-star reviews means the scraper reads past many higher-rated ones to fill your requested count, so allow a slightly higher cap when filtering tightly.
• Almost all Flipkart text reviews are already from certified buyers, so certifiedBuyersOnly rarely changes the result set — leave it off unless you specifically need to drop the rare non-certified review.
• Batch many products in one run — add every product or review URL to productUrls; each is processed independently and every row is tagged with its own product context.
• Turn off includeImages when you only need text and ratings to keep the output lighter and faster to process.

Pricing

From $1.5 per 1,000 results — one of the most affordable Flipkart review extractors available, and cheaper at every volume than comparable tools. No compute charges — you only pay per result returned. Bronze, Silver, and Gold subscribers pay progressively less; the table below shows total cost at each discount tier.

Results	No discount	Bronze	Silver	Gold
100	$0.18	$0.17	$0.16	$0.15
1,000	$1.80	$1.70	$1.60	$1.50
10,000	$18.00	$17.00	$16.00	$15.00
100,000	$180.00	$170.00	$160.00	$150.00

A "result" is one review row in the output dataset. There are no compute-time charges — you pay only for the reviews returned. Platform fees (storage and data transfer) depend on your Apify plan.

Integrations

Export data in JSON, CSV, Excel, XML, or RSS. Connect to 1,500+ apps via:

• Zapier / Make / n8n — Workflow automation
• Google Sheets — Direct spreadsheet export
• Slack / Email — Notifications on new results
• Webhooks — Trigger custom APIs on run completion
• Apify API — Full programmatic access

Legal & Ethical Use

This actor is designed for legitimate product research, market analysis, and customer-experience work. You are responsible for complying with applicable laws and Flipkart's Terms of Service. Treat reviewer names, locations, and photos as personal data: do not use them for spam, harassment, profiling, or any unlawful purpose, and respect data-protection regulations that apply to you.