Sephora Product Reviews Scraper: Extract Beauty Product Customer Feedback

What This Scraper Does

The Sephora Product Reviews Scraper extracts customer reviews from individual product pages on Sephora.com. It captures complete review data including ratings, review text, user information, helpfulness votes, verification status, and temporal metadata—essential for understanding product performance and customer sentiment in the competitive beauty market.

Sephora hosts millions of authentic customer reviews across makeup, skincare, haircare, and fragrance products. This data reveals product strengths and weaknesses, ingredient concerns, shade matching issues, and usage patterns that drive purchasing decisions. For beauty brands, retailers, and market researchers, this review data provides unfiltered customer intelligence.

Key Users:

• Beauty brands monitoring product reception and competitor performance
• Market researchers analyzing beauty trends and consumer preferences
• E-commerce platforms building review aggregation systems
• Product development teams identifying improvement opportunities from customer feedback
• Marketing agencies extracting testimonials and understanding messaging effectiveness

Input Configuration

The scraper processes individual Sephora product detail pages—the pages showing full product information and customer reviews.

Example Configuration:

{
  "urls": [
    "https://www.sephora.com/product/diorshow-waterproof-mascara-P99901?skuId=838763"
  ],
  "sort_by": "SubmissionTime:desc",
  "offset": 0,
  "ignore_url_failures": true,
  "max_items_per_url": 50
}

Parameters Explained:

urls: Array of Sephora product detail URLs. Format: https://www.sephora.com/product/[product-name]-[product-id]. Collect these by browsing Sephora or using search/category scrapers.

sort_by: Controls review ordering. Options:

• "" (Any): Default Sephora sorting
• "TotalPositiveFeedbackCount:desc": Most helpful reviews first
• "Rating:desc": Highest ratings first
• "Rating:asc": Lowest ratings first (critical for finding issues)
• "SubmissionTime:desc": Newest reviews first (trending sentiment)
• "SubmissionTime:asc": Oldest reviews first (historical analysis)

offset: Starting position for pagination. Use 0 to start from beginning, increment by max_items_per_url for subsequent pages (e.g., 0, 50, 100).

ignore_url_failures: Set true when scraping multiple products. Individual failures (discontinued products, removed pages) won't stop the entire run.

max_items_per_url: Reviews to extract per product (1-100 recommended). Sephora products can have thousands of reviews—set higher values for comprehensive extraction or paginate through offset.

Output Structure: Complete Review Data

Sample Output:

{
  "id": "385595464",
  "cid": null,
  "source_client": "sephora",
  "badges": {
    "verified_purchaser": {
      "content_type": "REVIEW",
      "id": "verifiedPurchaser",
      "badge_type": "Custom"
    }
  },
  "badges_order": [
    "verifiedPurchaser"
  ],
  "last_moderated_time": "2026-04-27T20:31:19.000+00:00",
  "last_modification_time": "2026-04-27T20:31:19.000+00:00",
  "product_id": "838763",
  "original_product_name": "Diorshow Waterproof Mascara Catwalk Black",
  "campaign_id": "iOS App",
  "context_data_values_order": [
    "IncentivizedReview",
    "StaffContext",
    "skinTone",
    "skinType",
    "eyeColor",
    "ageRange",
    "skinConcerns"
  ],
  "author_id": "62575127160",
  "content_locale": "en_US",
  "is_featured": false,
  "total_inappropriate_feedback_count": 0,
  "total_client_response_count": 0,
  "total_comment_count": 0,
  "rating": 5,
  "is_ratings_only": false,
  "is_recommended": true,
  "total_feedback_count": 0,
  "total_negative_feedback_count": 0,
  "total_positive_feedback_count": 0,
  "moderation_status": "APPROVED",
  "submission_id": "r18723-en__17773206ovQY785LCU",
  "submission_time": "2026-04-27T20:10:23.000+00:00",
  "review_text": "The first try wasn’t good, since I went under my eyes. But after giving it second chance I absolutely loved it. Lasts all day keep eyelashes curled very well! Recommend.",
  "title": "Definitely worth a try",
  "user_nickname": "Toma9",
  "additional_fields": {
    "sociallockup": {
      "value": "avatar=https://community.sephora.com/t5/image/serverpage/avatar-name/default-avatar/avatar-theme/sephora/avatar-collection/sephora/avatar-display-size/profile/version/2?xdesc=1.0|biBadgeUrl=/html/rank_icons/birole_insider.png|engagementBadgeUrl=/html/rank_icons/window_shopper_1.png|biTier=vib",
      "id": "sociallockup",
      "label": "sociallockup"
    }
  },
  "additional_fields_order": [
    "sociallockup"
  ],
  "context_data_values": {
    "skin_tone": {
      "value": "fairLight",
      "id": "skinTone",
      "value_label": "Fair Light",
      "dimension_label": "Skin Tone"
    },
    "eye_color": {
      "value": "brown",
      "id": "eyeColor",
      "value_label": "Brown",
      "dimension_label": "Eye Color"
    },
    "staff_context": {
      "value": "false",
      "id": "StaffContext",
      "value_label": "No",
      "dimension_label": "I am a Sephora employee"
    },
    "skin_type": {
      "value": "oilySk",
      "id": "skinType",
      "value_label": "Oily",
      "dimension_label": "Skin Type"
    },
    "incentivized_review": {
      "value": "False",
      "id": "IncentivizedReview",
      "value_label": "No",
      "dimension_label": "I received this product as a free sample"
    },
    "skin_concerns": {
      "value": "acneBlemishes",
      "id": "skinConcerns",
      "value_label": "Acne/Blemishes",
      "dimension_label": "Skin Concerns"
    },
    "age_range": {
      "value": "20s",
      "id": "ageRange",
      "value_label": "20's",
      "dimension_label": "Age Range"
    }
  },
  "videos": [],
  "pros": null,
  "comment_ids": [],
  "photos": [],
  "user_location": null,
  "inappropriate_feedback_list": [],
  "secondary_ratings_order": [],
  "client_responses": [],
  "tag_dimensions": {},
  "product_recommendation_ids": [],
  "is_syndicated": false,
  "rating_range": 5,
  "helpfulness": null,
  "secondary_ratings": {},
  "cons": null,
  "tag_dimensions_order": []
}

Core Identifiers:

• ID: Unique review identifier for deduplication
• Product ID: Links review to specific product
• Submission ID: Alternative review identifier
• Author ID: User identifier (when available)

Review Content:

• Title: Review headline summarizing opinion
• Review Text: Full review body with detailed feedback
• Rating: Star rating (1-5 scale)
• Is Recommended: Boolean indicating product recommendation
• Pros/Cons: Structured lists of positives and negatives

Engagement Metrics:

• Total Feedback Count: Total helpfulness votes
• Total Positive Feedback Count: "Helpful" votes
• Total Negative Feedback Count: "Not helpful" votes
• Helpfulness: Calculated helpfulness score
• Total Comment Count: Replies to review
• Total Client Response Count: Brand responses

Verification & Quality:

• Badges: Verification badges (e.g., "Verified Purchase")
• Is Featured: Sephora-highlighted review
• Moderation Status: Review approval state
• Is Syndicated: Indicates syndicated content

Temporal Data:

• Submission Time: When review was posted
• Last Modification Time: Last edit timestamp
• Last Moderated Time: Moderation timestamp

User Information:

• User Nickname: Reviewer's display name
• User Location: Geographic location
• Context Data Values: Demographic data (skin type, age, etc.)

Multimedia:

• Photos: Array of customer-uploaded images
• Videos: Customer-uploaded video reviews

Secondary Ratings:

• Product-specific attribute ratings (e.g., "Longevity," "Application," "Value")

Additional Context:

• Tag Dimensions: Product attributes mentioned
• Additional Fields: Extended metadata
• Source Client: Review submission platform

Step-by-Step Usage

1. Collect Product URLs: Browse Sephora categories or use search to find target products. Copy URLs from product pages. For competitive analysis, gather URLs from competitor brand pages.

2. Configure Extraction: Set sort_by based on needs (newest for trending sentiment, highest-rated for testimonials, lowest-rated for issues). Set max_items_per_url to 50-100 for comprehensive extraction.

3. Handle Pagination: For products with 500+ reviews, either:

• Run multiple times incrementing offset (0, 100, 200...)
• Set high max_items_per_url and let scraper paginate

4. Run & Monitor: Launch scraper. Check dataset preview to verify data quality. Typical extraction: 100 reviews in 1-2 minutes.

5. Export & Analyze: Export JSON for database integration or CSV for spreadsheet analysis. Filter by rating, date, or verification status for targeted analysis.

Strategic Applications

Product Performance Monitoring: Track review volume, average ratings, and sentiment trends over time. Identify quality issues early through rating drops or negative review spikes.

Competitive Intelligence: Compare your products vs. competitors on secondary ratings (longevity, value, application). Identify features customers prefer in competitor products.

Sentiment Analysis: Process review text with NLP to extract common themes—ingredient concerns, shade matching issues, packaging complaints, scent preferences.

Customer Insight Mining: Analyze context data (skin type, age) to understand which demographics love or hate products. Tailor marketing to high-satisfaction segments.

Feature Prioritization: Extract pros/cons and tag dimensions to identify most-valued features and common complaints. Guide product development roadmaps.

Review Response Strategy: Identify high-visibility negative reviews (high helpfulness scores) requiring brand responses. Track total_client_response_count to measure engagement.

Social Proof Extraction: Pull verified, high-rated reviews with photos for marketing materials. Filter by badges: ["Verified Purchase"] and photos: not empty.

Temporal Trend Analysis: Sort by submission_time to track sentiment changes after reformulations, price changes, or marketing campaigns.

Best Practices

Schedule Regular Scraping: Weekly extraction captures new reviews for trending sentiment. Monthly comprehensive scraping builds historical datasets.

Segment by Rating: Analyze 1-2 star vs. 4-5 star reviews separately. Low ratings reveal issues; high ratings identify strengths.

Leverage Secondary Ratings: Beauty products have attribute-specific ratings (longevity, value, application). These granular metrics reveal specific performance dimensions.

Validate Verification: Prioritize badges: ["Verified Purchase"] reviews for authenticity. Track verified vs. unverified ratios to assess review quality.

Enrich with Product Data: Combine review data with product information (price, ingredients, category) from product detail scrapers for comprehensive analysis.

Monitor Helpfulness: High total_positive_feedback_count reviews represent community consensus. Weight these more heavily in analysis.

Track Review Velocity: Sudden review volume spikes indicate viral moments, influencer mentions, or quality issues requiring immediate attention.

Conclusion

The Sephora Product Reviews Scraper transforms customer feedback into actionable beauty market intelligence. From identifying product issues to extracting competitive insights, this tool delivers the customer voice data driving decisions in the $500B+ beauty industry. Start extracting Sephora review insights today.