MagazineLuiza Product Reviews Scraper: Extract Customer Feedback Data

Understanding MagazineLuiza Reviews and Their Business Value

MagazineLuiza (Magalu) is Brazil's largest retail company and e-commerce platform, serving millions of customers across electronics, appliances, furniture, and consumer goods. Product reviews on Magalu provide authentic customer feedback crucial for understanding product performance, customer satisfaction, and purchase decisions in the Brazilian market.

Review data reveals what customers actually think about products—quality issues, feature satisfaction, delivery experiences, and value perceptions. For brands selling on Magalu, this feedback drives product improvements and reputation management. For market researchers, it provides sentiment trends across product categories. For retailers and competitors, it offers competitive intelligence on product-market fit.

Manually collecting reviews across multiple products and pages is time-intensive. This scraper automates extraction, delivering structured review datasets ready for sentiment analysis, quality monitoring, or competitive benchmarking.

What This Scraper Extracts

The scraper processes MagazineLuiza product review pages, capturing individual customer reviews with complete metadata.

Core Fields:

Product: Product information including name, ID, and URL—links reviews to specific items.

Review ID & Campaign ID: Unique identifiers for tracking individual reviews and promotional campaigns.

Description: Full review text containing customer opinions, experiences, and detailed feedback.

Recommended: Boolean indicating if customer recommends the product—key satisfaction metric.

Status: Review publication status (approved, pending, rejected).

Rating: Numerical score (typically 1-5 stars)—primary satisfaction indicator.

Title: Review headline summarizing customer's main point.

Submission Date: Timestamp enabling trend analysis and recency filtering.

User Data: Reviewer information (may include name, location, verification status).

Attributes: Review characteristics (verified purchase, helpful votes).

Dimension Defs & Dimensions: Structured rating breakdowns (e.g., quality, value, delivery) providing granular satisfaction metrics.

Use Cases:

E-commerce Brands monitor customer feedback, identify product issues, and track satisfaction trends. Market Researchers analyze sentiment across categories, demographics, and time periods. Retailers benchmark competitor products and identify market gaps. Product Managers prioritize improvements based on customer pain points.

Input Configuration

Example:

{
  "urls": ["https://www.magazineluiza.com.br/review/240066500/notebook-asus-vivobook-go-15-amd-ryzen-5-7520u-8gb-ram-512gb-ssd-156-full-hd-windows-11-e1504fa-nj836w/IN/NASS/?page=2"],
  "ignore_url_failures": true,
  "max_items_per_url": 50
}

Parameters:

urls: Array of product review page URLs from MagazineLuiza. Navigate to a product's review section on Magalu, copy the URL. Include pagination URLs (e.g., ?page=2) to collect reviews across multiple pages. Can add multiple product URLs to batch process.

ignore_url_failures: Set true to continue scraping remaining URLs if some fail (recommended for batch processing). Set false if all URLs must succeed.

max_items_per_url: Maximum reviews to extract per URL (default: 20, recommended: 50-100 for comprehensive collection). Magalu typically displays 10-20 reviews per page, so higher values enable multi-page extraction from single URL.

Finding Review URLs: Go to any product on magazineluiza.com.br → scroll to reviews section → click to view all reviews → copy URL from browser. For multi-page extraction, manually increment page parameter or set high max_items_per_url.

Output Structure and Field Definitions

Sample Output:

{
  "product": {
    "images": [],
    "product_link": "https://mixer-hybrid.magazineluiza.com.br//notebook-asus-vivobook-go-15-amd-ryzen-5-7520u-8gb-ram-512gb-ssd-156-full-hd-windows-11-e1504fa-nj836w/p/240066500/in/nass/",
    "product_name": null,
    "rating_value": "5",
    "sku": "240066500",
    "videos": []
  },
  "review_id": "0dab0bd9-b1f1-430a-a283-b2d24c5d909f",
  "campaign_id": "CRM",
  "description": "Ótima qualidade!!",
  "recommended": null,
  "status": "APPROVED",
  "rating": 5,
  "title": null,
  "submission_date": "2026-05-02T10:23:06.533Z",
  "user_data": {
    "email": null,
    "name": "Manoel",
    "customer_id": null
  },
  "attributes": [
    {
      "label": "color",
      "value": "Mixed Black"
    }
  ],
  "dimension_defs": null,
  "dimensions": [
    {
      "id": "QualidadeGeral",
      "label": "Qualidade Geral",
      "rating": 5
    }
  ]
}

Product: Product object containing SKU, name, and identifier. Purpose: Link reviews to inventory systems, analyze feedback by product line.

Review ID: Unique review identifier. Purpose: Track individual reviews, avoid duplicates, reference specific feedback.

Campaign ID: Marketing campaign association. Purpose: Analyze review patterns from promotional periods, incentivized campaigns.

Description: Full customer review text. Purpose: Sentiment analysis, keyword extraction, identifying specific issues/praise.

Recommended: True/false recommendation flag. Purpose: Calculate net promoter score (NPS), overall satisfaction rate.

Status: Publication status. Purpose: Filter only published reviews, track moderation patterns.

Rating: Numerical score (1-5). Purpose: Calculate average ratings, distribution analysis, satisfaction metrics.

Title: Review headline. Purpose: Quick sentiment overview, headline analysis, summary displays.

Submission Date: Review timestamp. Purpose: Trend analysis, identify recent feedback, seasonal patterns.

User Data: Reviewer profile. Purpose: Demographic analysis (if location available), verified buyer filtering.

Attributes: Review metadata (verified purchase, helpful count). Purpose: Weight reviews by verification, identify most helpful feedback.

Dimension Defs & Dimensions: Multi-dimensional ratings (quality, delivery, value). Purpose: Granular satisfaction analysis, identify specific improvement areas.

Step-by-Step Usage

1. Identify Target Products: Browse MagazineLuiza for products you want to analyze. Navigate to review sections.

2. Collect Review URLs: Copy URLs from review pages. For products with many reviews, include pagination URLs (?page=1, ?page=2, etc.).

3. Configure Input: Add URLs to urls array. Set max_items_per_url based on review volume (50-100 for comprehensive extraction). Enable ignore_url_failures for robustness.

4. Run Scraper: Execute via Apify console. Typical run extracting 100 reviews completes in 1-2 minutes.

5. Export Data: Download as JSON for analysis tools, CSV for spreadsheets, or Excel for business reporting.

Handling Pagination: Either manually add page URLs to urls array, or set high max_items_per_url to let scraper automatically paginate.

Strategic Applications

Product Quality Monitoring: Track review sentiment over time. Identify emerging quality issues from description keywords and rating drops.

Competitive Intelligence: Compare review volumes, ratings, and sentiment between your products and competitors on Magalu.

Sentiment Analysis: Use description field for NLP sentiment analysis. Identify common praise themes and complaint patterns.

Feature Demand Analysis: Extract feature mentions from reviews. Understand which product attributes customers value most.

Customer Satisfaction Trends: Track rating distributions and recommendation rates by time period. Identify seasonal patterns or campaign impacts.

Dimension-Level Insights: Analyze granular ratings (quality, value, delivery) to pinpoint specific improvement areas beyond overall scores.

Best Practices

Regular Scraping: Run weekly to track new reviews and sentiment trends. Store historical data for longitudinal analysis.

Segmentation: Collect reviews by product category, price range, or brand for targeted analysis.

Verification Filtering: Prioritize verified purchase reviews for authentic feedback. Use attributes field to filter.

Sentiment Enrichment: Combine review text with sentiment analysis tools or LLMs to classify positive/negative/neutral at scale.

Translation: For international analysis, translate Portuguese reviews using translation APIs while preserving original text.

Quality Checks: Validate that ratings match descriptions. Flag suspicious patterns (all 5-stars from new accounts).

Conclusion

The MagazineLuiza Product Reviews Scraper transforms customer feedback into actionable intelligence for Brazil's largest e-commerce platform. Whether monitoring product performance, conducting competitive analysis, or understanding customer sentiment, this tool delivers the structured review data needed for data-driven decisions in the Brazilian market.