Flipkart.com Reviews Scraper: Extract E-Commerce Customer Feedback Data

Understanding Flipkart Reviews and Their Business Intelligence Value

Flipkart stands as India's largest homegrown e-commerce marketplace, serving hundreds of millions of customers across electronics, fashion, home goods, and countless other categories. Customer reviews on Flipkart represent authentic, unfiltered feedback from one of the world's most dynamic consumer markets—insights that drive product development, marketing strategies, and competitive positioning.

Unlike aggregate rating numbers, individual reviews contain rich qualitative data: specific pain points, feature preferences, quality assessments, sizing accuracy, delivery experiences, and use-case scenarios. For brands selling on Flipkart, this feedback reveals what customers truly value and what drives purchase decisions. For market researchers, review patterns expose emerging trends, regional preferences, and category-specific expectations across India's diverse consumer landscape.

Manually collecting reviews means clicking through pagination, copying text, downloading images, and organizing scattered data. With products often having hundreds or thousands of reviews, this becomes impractical. The Flipkart.com Reviews Scraper automates this entire process, transforming review pages into structured datasets ready for sentiment analysis, competitive benchmarking, or quality monitoring.

What This Scraper Extracts and Who Should Use It

The Flipkart.com Reviews Scraper processes product review pages—the sections showing customer feedback for specific products. It captures complete review data including ratings, text feedback, reviewer information, verification status, and media attachments.

Core Data Extracted:

The scraper collects reviewer identity (author name, location, verified buyer status, user badges), review content (title, detailed text, rating stars), engagement metrics (helpful count, creation date), media attachments (customer-uploaded images), product context (specific attributes reviewed like size, color), and review metadata (unique ID, review type, URL).

This comprehensive extraction enables multi-dimensional analysis. Sentiment analysis on review text reveals emotional responses. Rating distributions show quality consistency. Verified buyer flags ensure authentic feedback filtering. Geographic data exposes regional preference patterns. Image analysis provides visual quality insights beyond text descriptions.

Target Users:

E-commerce Brands monitor product performance, identify improvement opportunities, and respond to customer concerns. Product Managers gather feature requests and pain points directly from users to guide development roadmaps. Market Researchers analyze consumer sentiment, competitive positioning, and emerging category trends across India's e-commerce landscape. Quality Assurance Teams identify defect patterns and manufacturing issues through systematic review analysis. Marketing Teams extract positive testimonials for campaigns and understand messaging that resonates with Indian consumers. Competitive Intelligence Analysts benchmark competitor products, identify their strengths/weaknesses, and discover market gaps.

Input Configuration: Targeting Review Pages

The scraper processes Flipkart product review page URLs. These are the specific pages showing customer reviews for a product, typically accessed by clicking "Reviews" on product listings.

Example Input Configuration:

{
  "proxy": {
    "useApifyProxy": false
  },
  "max_items_per_url": 20,
  "ignore_url_failures": true,
  "urls": [
    "https://www.flipkart.com/guti-flared-women-blue-jeans/product-reviews/itm9d67a6a8368c2?pid=JEAH3ZTFHYG5GZ2G&lid=LSTJEAH3ZTFHYG5GZ2GGMISXF&marketplace=FLIPKART&page=2"
  ]
}

Example Screenshot:

Parameter Explanation:

proxy configuration: Controls proxy usage for scraping. Setting useApifyProxy: false means no proxy is used—suitable for small-scale scraping or testing. For large-scale extraction or to avoid rate limiting, enable Apify's proxy service with residential proxies.

max_items_per_url: Limits reviews extracted per URL. Set to 20 to collect 20 reviews per page. Flipkart typically displays 10 reviews per page, so this captures multiple pages. Increase for comprehensive extraction (e.g., 100 for deep analysis).

ignore_url_failures: When true, failed URLs don't stop the entire scraping run. Essential when processing multiple products—some may have deleted pages or access issues.

urls array: Contains review page URLs to scrape. URL format: https://www.flipkart.com/[product-name]/product-reviews/[product-id]?pid=[variant-id]&page=[page-number]. Collect these by navigating to product pages, clicking "Reviews," and copying URLs. Include page parameters for pagination.

Pro Tip: To scrape all reviews for a product, either include multiple page URLs (page=1, page=2, etc.) or set max_items_per_url higher than total reviews to let the scraper auto-paginate.

Complete Output Structure: Understanding Review Data Fields

The scraper returns JSON data with detailed review information. Each field serves specific analytical purposes:

Type: Review classification (e.g., "REVIEW"). Purpose: Distinguishing reviews from other content types, filtering data by content category.

Author: Reviewer's name as displayed on Flipkart. Purpose: Identifying repeat reviewers, analyzing reviewer credibility patterns.

Certified Buyer: Boolean indicating if reviewer purchased product on Flipkart. Purpose: Filtering verified vs. unverified reviews—certified buyers provide more reliable feedback.

Created: Timestamp when review was posted. Purpose: Tracking review recency, analyzing sentiment trends over time, identifying launch period feedback.

Helpful Count: Number of users who marked review as helpful. Purpose: Identifying most valuable reviews, weighting sentiment analysis by helpfulness, prioritizing actionable feedback.

ID: Unique identifier for each review. Purpose: Preventing duplicates in databases, tracking specific reviews, linking to source data.

Images: Array of customer-uploaded image URLs. Purpose: Visual quality assessment, detecting defects/damages, analyzing product appearance vs. expectations.

Location: Reviewer's geographic location. Purpose: Regional preference analysis, identifying location-specific issues (e.g., sizing for different regions), targeting marketing by area.

Media: Media attachments object containing images/videos. Purpose: Accessing all visual content, analyzing product presentation in real-world contexts.

Product Attribute / Product Attribute List: Specific product variant reviewed (size, color, model). Purpose: Segmenting reviews by variant, identifying which attributes receive best/worst feedback.

Rating: Star rating (typically 1-5). Purpose: Quantitative sentiment metric, calculating average ratings, filtering by satisfaction level.

Review Property Map: Additional structured review properties. Purpose: Accessing extended metadata, platform-specific attributes.

Review Type Display Text: Human-readable review classification. Purpose: Understanding review context (e.g., "Customer Review," "Expert Review").

Text: Full review content written by customer. Purpose: Core sentiment analysis data, extracting specific feedback, identifying pain points and praise.

Title: Review headline/summary. Purpose: Quick sentiment scanning, keyword extraction, identifying common themes.

Total Count: Total number of reviews for this product. Purpose: Assessing review volume, calculating statistical significance of ratings.

URL: Direct link to this review on Flipkart. Purpose: Verification, sharing specific reviews, tracking review changes.

User Badge: Special badges/status indicators for reviewer. Purpose: Identifying expert reviewers, verified purchasers, trust indicators for weighting analysis.

Sample Output:

[
  {
  "type": "ProductReviewValue",
  "author": "Flipkart Customer",
  "certified_buyer": true,
  "created": "9 months ago",
  "helpful_count": 68,
  "id": "780b9585-e177-4fe2-bfe7-230efa6a7754",
  "images": [
    {
      "action": {
        "constraints": null,
        "custom_tracking_events": null,
        "fallback": null,
        "login_type": "LOGIN_NOT_REQUIRED",
        "non_widgetize_redirection": null,
        "omniture_data": null,
        "omniture_tracking": null,
        "original_url": "/review-image-fullscreen?pid=JEAH3ZTFZ6HGFGYY&imageId=blobio-imr_b9fb620e0ca34ed5999619efe41d4b71.jpg&reviewReferenceId=JEAH3ZTFZ6HGFGYY:2&language=en",
        "params": {
          "image_id": "blobio-imr_b9fb620e0ca34ed5999619efe41d4b71.jpg",
          "images_shown": 2,
          "screen_name": "productReviewFullScreenPage",
          "horizontally_paginated": true,
          "pagination_url": "/review-image-grid?pid=JEAH3ZTFZ6HGFGYY&reviewId=780b9585-e177-4fe2-bfe7-230efa6a7754&reviewReferenceId=JEAH3ZTFZ6HGFGYY:2",
          "total_image_count": 2
        },
        "required_permission_type": null,
        "screen_type": "multiWidgetPage",
        "store_value": {},
        "sub_type": null,
        "tracking": {},
        "trigger_extra_standard_events": null,
        "type": "NAVIGATION",
        "url": "/review-image-fullscreen?pid=JEAH3ZTFZ6HGFGYY&imageId=blobio-imr_b9fb620e0ca34ed5999619efe41d4b71.jpg&reviewReferenceId=JEAH3ZTFZ6HGFGYY:2&language=en",
        "validation_meta": null,
        "widget_tracking": null,
        "widgetized_actions": null
      },
      "meta_data": null,
      "rc_type": null,
      "tracking": null,
      "tracking_data": null,
      "value": {
        "type": "ReviewImage",
        "image_id": "blobio-imr_b9fb620e0ca34ed5999619efe41d4b71.jpg",
        "image_u_r_l": "https://rukminim1.flixcart.com/blobio/{@width}/{@height}/imr/blobio-imr_b9fb620e0ca34ed5999619efe41d4b71.jpg?q={@quality}",
        "view_type": "DEFAULT_VIEW"
      }
    },
    {
      "action": {
        "constraints": null,
        "custom_tracking_events": null,
        "fallback": null,
        "login_type": "LOGIN_NOT_REQUIRED",
        "non_widgetize_redirection": null,
        "omniture_data": null,
        "omniture_tracking": null,
        "original_url": "/review-image-fullscreen?pid=JEAH3ZTFZ6HGFGYY&imageId=blobio-imr_2f46085c1dbb4b8d943c9b63230ce1da.jpg&reviewReferenceId=JEAH3ZTFZ6HGFGYY:2&language=en",
        "params": {
          "image_id": "blobio-imr_2f46085c1dbb4b8d943c9b63230ce1da.jpg",
          "images_shown": 2,
          "screen_name": "productReviewFullScreenPage",
          "horizontally_paginated": true,
          "pagination_url": "/review-image-grid?pid=JEAH3ZTFZ6HGFGYY&reviewId=780b9585-e177-4fe2-bfe7-230efa6a7754&reviewReferenceId=JEAH3ZTFZ6HGFGYY:2",
          "total_image_count": 2
        },
        "required_permission_type": null,
        "screen_type": "multiWidgetPage",
        "store_value": {},
        "sub_type": null,
        "tracking": {},
        "trigger_extra_standard_events": null,
        "type": "NAVIGATION",
        "url": "/review-image-fullscreen?pid=JEAH3ZTFZ6HGFGYY&imageId=blobio-imr_2f46085c1dbb4b8d943c9b63230ce1da.jpg&reviewReferenceId=JEAH3ZTFZ6HGFGYY:2&language=en",
        "validation_meta": null,
        "widget_tracking": null,
        "widgetized_actions": null
      },
      "meta_data": null,
      "rc_type": null,
      "tracking": null,
      "tracking_data": null,
      "value": {
        "type": "ReviewImage",
        "image_id": "blobio-imr_2f46085c1dbb4b8d943c9b63230ce1da.jpg",
        "image_u_r_l": "https://rukminim1.flixcart.com/blobio/{@width}/{@height}/imr/blobio-imr_2f46085c1dbb4b8d943c9b63230ce1da.jpg?q={@quality}",
        "view_type": "DEFAULT_VIEW"
      }
    }
  ],
  "location": {
    "type": "Location",
    "city": "Jhajjar",
    "state": "Haryana"
  },
  "media": [
    {
      "action": {
        "constraints": null,
        "custom_tracking_events": null,
        "fallback": null,
        "login_type": "LOGIN_NOT_REQUIRED",
        "non_widgetize_redirection": null,
        "omniture_data": null,
        "omniture_tracking": null,
        "original_url": "/review-image-fullscreen?pid=JEAH3ZTFZ6HGFGYY&imageId=blobio-imr_b9fb620e0ca34ed5999619efe41d4b71.jpg&reviewReferenceId=JEAH3ZTFZ6HGFGYY:2&language=en",
        "params": {
          "image_id": "blobio-imr_b9fb620e0ca34ed5999619efe41d4b71.jpg",
          "total_media_count": 2,
          "screen_name": "productReviewFullScreenPage",
          "horizontally_paginated": true,
          "pagination_url": "/review-media-grid?pid=JEAH3ZTFZ6HGFGYY&reviewId=780b9585-e177-4fe2-bfe7-230efa6a7754&reviewReferenceId=JEAH3ZTFZ6HGFGYY:2",
          "media_shown": 2
        },
        "required_permission_type": null,
        "screen_type": "multiWidgetPage",
        "store_value": {},
        "sub_type": null,
        "tracking": {},
        "trigger_extra_standard_events": null,
        "type": "NAVIGATION",
        "url": "/review-image-fullscreen?pid=JEAH3ZTFZ6HGFGYY&imageId=blobio-imr_b9fb620e0ca34ed5999619efe41d4b71.jpg&reviewReferenceId=JEAH3ZTFZ6HGFGYY:2&language=en",
        "validation_meta": null,
        "widget_tracking": null,
        "widgetized_actions": null
      },
      "meta_data": null,
      "rc_type": null,
      "tracking": null,
      "tracking_data": null,
      "value": {
        "type": "ReviewImage",
        "image_id": "blobio-imr_b9fb620e0ca34ed5999619efe41d4b71.jpg",
        "image_style": {
          "border_radius": 4,
          "height": 92,
          "width": 92
        },
        "image_u_r_l": "https://rukminim1.flixcart.com/blobio/{@width}/{@height}/imr/blobio-imr_b9fb620e0ca34ed5999619efe41d4b71.jpg?q={@quality}",
        "view_type": "DEFAULT_VIEW"
      }
    },
    {
      "action": {
        "constraints": null,
        "custom_tracking_events": null,
        "fallback": null,
        "login_type": "LOGIN_NOT_REQUIRED",
        "non_widgetize_redirection": null,
        "omniture_data": null,
        "omniture_tracking": null,
        "original_url": "/review-image-fullscreen?pid=JEAH3ZTFZ6HGFGYY&imageId=blobio-imr_2f46085c1dbb4b8d943c9b63230ce1da.jpg&reviewReferenceId=JEAH3ZTFZ6HGFGYY:2&language=en",
        "params": {
          "image_id": "blobio-imr_2f46085c1dbb4b8d943c9b63230ce1da.jpg",
          "total_media_count": 2,
          "screen_name": "productReviewFullScreenPage",
          "horizontally_paginated": true,
          "pagination_url": "/review-media-grid?pid=JEAH3ZTFZ6HGFGYY&reviewId=780b9585-e177-4fe2-bfe7-230efa6a7754&reviewReferenceId=JEAH3ZTFZ6HGFGYY:2",
          "media_shown": 2
        },
        "required_permission_type": null,
        "screen_type": "multiWidgetPage",
        "store_value": {},
        "sub_type": null,
        "tracking": {},
        "trigger_extra_standard_events": null,
        "type": "NAVIGATION",
        "url": "/review-image-fullscreen?pid=JEAH3ZTFZ6HGFGYY&imageId=blobio-imr_2f46085c1dbb4b8d943c9b63230ce1da.jpg&reviewReferenceId=JEAH3ZTFZ6HGFGYY:2&language=en",
        "validation_meta": null,
        "widget_tracking": null,
        "widgetized_actions": null
      },
      "meta_data": null,
      "rc_type": null,
      "tracking": null,
      "tracking_data": null,
      "value": {
        "type": "ReviewImage",
        "image_id": "blobio-imr_2f46085c1dbb4b8d943c9b63230ce1da.jpg",
        "image_style": {
          "border_radius": 4,
          "height": 92,
          "width": 92
        },
        "image_u_r_l": "https://rukminim1.flixcart.com/blobio/{@width}/{@height}/imr/blobio-imr_2f46085c1dbb4b8d943c9b63230ce1da.jpg?q={@quality}",
        "view_type": "DEFAULT_VIEW"
      }
    }
  ],
  "product_attribute": null,
  "product_attribute_list": [
    {
      "name": "Color",
      "value": "Ice Wash Blue"
    },
    {
      "name": "Size",
      "value": "32"
    }
  ],
  "rating": 3,
  "review_property_map": {
    "v_e_r_i_f_i_e_d__p_u_r_c_h_a_s_e": true
  },
  "review_type_display_text": null,
  "text": "The jeans is good but i accidentally ordered one size larger i usually wear 30 size and ordered 32 so it's quite big on me so i will exchange it\nOtherwise jeans is superb and let's first use it and see how long it will last",
  "title": "Fair",
  "total_count": 89,
  "url": "/reviews/JEAH3ZTFZ6HGFGYY:2?reviewId=780b9585-e177-4fe2-bfe7-230efa6a7754",
  "user_badge": null,
  "from_url": "https://www.flipkart.com/guti-flared-women-blue-jeans/product-reviews/itm9d67a6a8368c2?pid=JEAH3ZTFHYG5GZ2G&lid=LSTJEAH3ZTFHYG5GZ2GGMISXF&marketplace=FLIPKART"
}
]

Step-by-Step Implementation Guide

1. Identify Target Products: Determine which products need review analysis. Navigate to product pages on Flipkart, click the "Reviews" section, and copy URLs. For category-wide analysis, compile URLs for multiple competing products.

2. Build URL List with Pagination: For comprehensive extraction, include multiple page URLs per product. Example: ...&page=1, ...&page=2, etc. Alternatively, set high max_items_per_url to auto-collect across pages.

3. Configure Input Settings: Create JSON configuration with your URLs. For large-scale scraping (100+ pages), enable proxy (useApifyProxy: true) to avoid detection. Set ignore_url_failures: true for robustness.

4. Launch Scraping Run: Execute via Apify console. Monitor real-time progress. Scraping 50 review pages typically completes in 3-5 minutes depending on proxy and network performance.

5. Review Data Quality: Check dataset preview. Verify that key fields (rating, text, certified_buyer) are populated correctly. Identify any systematic issues or missing data patterns.

6. Export and Analyze: Export data in preferred format—JSON for databases/analytics tools, CSV for spreadsheet analysis. Apply filters like certified_buyer: true to focus on verified purchases.

7. Handle Pagination Strategically: For products with thousands of reviews, prioritize recent reviews (lower page numbers) or highest-rated/lowest-rated reviews for focused analysis rather than scraping all pages.

Error Handling: Failed URLs often indicate deleted products or region-restricted content. Check URL validity in browser first. The activity log provides detailed error information for troubleshooting.

Strategic Applications for E-Commerce Intelligence

Product Quality Monitoring: Track rating trends and negative review patterns to identify quality issues early. Sudden spikes in 1-star reviews signal manufacturing problems or shipping damages requiring immediate action.

Feature Prioritization: Analyze review text to identify most-requested features and common pain points. Natural language processing on thousands of reviews reveals customer needs more accurately than surveys.

Competitive Benchmarking: Compare your products' reviews against competitors on rating distribution, review volume, verified buyer percentage, and sentiment. Identify competitor weaknesses to exploit and strengths to match.

Size and Fit Optimization: For fashion/footwear, analyze reviews mentioning sizing issues. Calculate percentage reporting "too small" vs. "too large" to adjust size charts and reduce returns.

Regional Market Insights: Location data reveals geographic preference patterns. Products rating 4+ stars in metros but 3 stars in smaller cities may indicate different expectations or use cases requiring targeted marketing.

Customer Testimonial Mining: Extract positive reviews with high helpful counts for marketing campaigns. Verified buyer reviews with images provide authentic social proof for product pages and advertisements.

Defect Pattern Detection: Systematic analysis of negative reviews identifies recurring defects (e.g., "zipper broke after 2 weeks" appears 47 times). Quantifying issues enables data-driven quality improvement discussions with manufacturers.

Sentiment Trend Analysis: Track sentiment over time using creation dates. Declining ratings may indicate quality control issues, while improving sentiment validates product iterations.

Maximizing Data Value: Advanced Techniques

Weighted Sentiment Analysis: Weight reviews by helpful count—reviews marked helpful by many users carry more significance. Also weight by certified buyer status (verified reviews more reliable than unverified).

Image-Based Quality Assessment: Use computer vision on customer-uploaded images to automatically detect visible defects, color accuracy issues, or packaging problems at scale.

Attribute-Level Analysis: Segment reviews by product attributes (size, color) to identify which variants perform best. "Size 32 Blue" may have 4.5 stars while "Size 28 Black" has 3.2 stars—indicating variant-specific issues.

Review Volume Velocity: Calculate reviews-per-day to assess product momentum. High review velocity indicates strong sales and engagement, while declining velocity may signal declining interest.

Competitor Response Time Analysis: Track if competitors respond to negative reviews and how quickly. This reveals their customer service commitment and provides benchmarks for your response strategy.

Location-Based Strategy: Identify cities/regions with highest review volumes and ratings. Focus marketing and inventory in high-performing areas while investigating challenges in low-rated regions.

Seasonal Pattern Detection: Analyze review timing for seasonal products. Reviews for winter clothing peak in October-December, revealing optimal launch timing and seasonal quality expectations.

Predictive Return Analysis: Correlate review text patterns with return rates. Reviews mentioning "poor quality," "not as described," or "sizing issues" predict higher return probability—enabling proactive intervention.

Data Governance and Best Practices

Scraping Frequency: For active products, scrape weekly to capture fresh reviews. For competitive monitoring, bi-weekly scraping balances data freshness with resource efficiency. Newly launched products benefit from daily scraping during critical first weeks.

Verified Buyer Filtering: Prioritize certified buyer reviews for decision-making. While unverified reviews provide some signal, verified purchases offer higher reliability for product improvement decisions.

Sentiment Thresholds: Establish rating thresholds for action triggers. Products dropping below 3.5 stars require investigation. Sudden 0.5+ star drops within a week signal urgent quality issues.

Privacy Considerations: Reviewer names and locations are publicly visible on Flipkart, but use data ethically. Aggregate analysis rather than individual targeting respects privacy while extracting insights.

Data Retention Strategy: Archive historical reviews to track product evolution. "Version 1" reviews from 2023 differ from current "Version 2" reviews, revealing improvement impact.

Cross-Platform Validation: Combine Flipkart reviews with Amazon India reviews for the same products to identify platform-specific bias or broader market sentiment.

Response Prioritization: Use helpful count and rating to prioritize which reviews deserve public responses. Address high-visibility negative reviews first to protect brand reputation.

Review Authenticity Checks: While certified buyer status helps, analyze patterns—multiple 5-star reviews with similar wording posted simultaneously may indicate manipulation requiring investigation.

Conclusion

The Flipkart.com Reviews Scraper transforms customer feedback into actionable business intelligence. From identifying quality issues before they escalate to discovering feature opportunities hidden in thousands of reviews, this tool empowers data-driven decisions in India's massive e-commerce market. Whether optimizing products, monitoring competitors, or understanding regional preferences, systematic review analysis drives competitive advantage. Start extracting Flipkart review insights today and turn customer voices into strategic assets.