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Zalando Reviews Scraper: Complete Reviews Data Extraction Tool

Understanding Zalando: Europe's Leading Fashion Platform

Zalando stands as Europe's premier fashion and lifestyle e-commerce platform, serving over 50 million active customers across 25 European markets. Starting as a retailer in 2008, Zalando has evolved from a pure retailer into a comprehensive platform hosting thousands of brands. The platform offers an extensive range of products from fashion and beauty to lifestyle items, making it a treasure trove of customer insights through its vast review ecosystem.

For businesses operating in the fashion industry, understanding customer sentiment and preferences is crucial for success. Zalando's customer reviews provide authentic, unfiltered feedback that can inform product development, marketing strategies, and competitive positioning. However, manually collecting this data from multiple product pages across different country domains is time-consuming and impractical at scale.

Comprehensive Review Data Extraction Solution

The Zalando Reviews Scraper is designed to efficiently extract comprehensive customer review data from any Zalando product page across all country domains. This powerful tool automates the tedious process of manually collecting review information, enabling businesses to gather large volumes of customer feedback data in minutes rather than hours or days.

The scraper is particularly valuable for fashion retailers, brand managers, market researchers, and e-commerce professionals who need to analyze customer sentiment, track competitor performance, or understand market trends. Whether you're launching a new product line, conducting competitive analysis, or monitoring brand reputation, this tool provides the data foundation for informed decision-making.

Built with reliability and accuracy in mind, the scraper handles various Zalando domains including zalando.com, zalando.de, zalando.co.uk, zalando.nl, and others, ensuring comprehensive coverage across European markets.

Input and Output Format Details

Example url 1: https://www.zalando.nl/apricot-watercolour-floral-smocked-maxi-jurk-green-aph21c0nx-m11.html

Example url 2: https://en.zalando.de/marc-opolo-kaira-trainers-whitesand-ma311a0om-a11.html

Example url 3: https://www.zalando.co.uk/nike-sportswear-dunk-trainers-whiteblack-ni111a0zk-a13.html

Example Screenshot of product information page:

Input Format

The scraper accepts a JSON configuration specifying the product URLs to extract data from, along with proxy settings and retry parameters:

{
  "max_retries_per_url": 2, // Maximum waiting time when accessing the links you provided.
  "proxy": { // Add a proxy to ensure that during the data collection process, you are not detected as a bot.
    "useApifyProxy": true,
    "apifyProxyGroups": [
      "RESIDENTIAL" 
    ],
    "apifyProxyCountry": "SG" // You should choose an Country that coincides with the Country you want to collect data from
  },
  "max_items_per_url": 20,
  "urls": [ // Links to product information pages.
    "https://www.zalando.nl/apricot-watercolour-floral-smocked-maxi-jurk-green-aph21c0nx-m11.html",
    "https://en.zalando.de/marc-opolo-kaira-trainers-whitesand-ma311a0om-a11.html",
    "https://www.zalando.co.uk/nike-sportswear-dunk-trainers-whiteblack-ni111a0zk-a13.html"
  ]
}

Configuration Parameters Explained:

• max_retries_per_url: Defines retry attempts for failed requests (recommended: 2-3)
• proxy settings: Essential for avoiding bot detection during large-scale scraping operations
• apifyProxyCountry: Should match the target country domain for optimal results
• max_items_per_url: Controls the maximum number of reviews per product (1-100 recommended)
• urls: Array of Zalando product page URLs from any supported country domain

Output Format

You get the output from the Stockx.com Reviews Page Scraper stored in a tab. The following is an example of the Information Fields collected after running the Actor.

[ // List of product information
  {
  "id": "129cdaf8-77f3-3876-8047-39e4f2d03a46",
  "rating": "FIVE",
  "original_content": {
    "__typename": "ProductReviewContent",
    "title": "Fällt viel kleiner aus",
    "text": "Die Hose ist super, aber ich musste sie mehrmals kleiner bestellen. Ich habe sonst 29/34 und jetzt habe ich sie in 26/33. \r\nDas ist schon deutlich kleiner als sonst. \r\nDas Material ist super. Der Schnitt auch.",
    "locale": "de_DE"
  },
  "published_at": "2025-08-16T20:50:23Z",
  "is_verified_purchase": true,
  "syndication_attributes": null,
  "review_attributes": [],
  "customer_attributes": [],
  "author_name": "Mimi",
  "from_url": "https://www.zalando.nl/levis-xl-straight-relaxed-fit-jeans-thanks-friend-le221n0nx-k12.html"
}, // ... Many other product details
]

The scraper returns structured review data with nine key fields, each providing specific business intelligence value:

Output Fields and Their Business Applications

ID: Unique review identifier enabling data deduplication and tracking across multiple scraping sessions. Essential for maintaining data integrity in large datasets and preventing duplicate analysis.

Rating: Numerical score (typically 1-5 stars) representing customer satisfaction. This metric is crucial for calculating average product ratings, identifying satisfaction trends, and comparing performance against competitors.

Original Content: Complete review text in the customer's original language. This unfiltered feedback contains valuable insights about product quality, fit, design, and customer experience that can inform product improvements and marketing messaging.

Published At: Review publication timestamp enabling temporal analysis. Track how product reception changes over time, identify seasonal patterns, and monitor the impact of product updates or marketing campaigns.

Verified Purchase: Boolean indicator confirming whether the reviewer actually purchased the product. This field is critical for filtering authentic feedback and ensuring data quality in analysis, as verified reviews carry more weight in decision-making.

Syndication Attributes: Metadata about review distribution across platforms, useful for understanding review visibility and reach across Zalando's ecosystem.

Review Attributes: Additional review characteristics such as helpfulness votes, reply status, and moderation flags that provide context about review quality and community engagement.

Customer Attributes: Demographic and behavioral data about the reviewer (when available), including customer type, purchase history indicators, and engagement patterns that help segment feedback by customer profiles.

Author Name: Reviewer identification (often anonymized) allowing for author-based analysis while respecting privacy requirements.

Sample Output Structure

Each extracted review follows this standardized format, ensuring consistent data processing and analysis across different products and markets.

Implementation Guide and Best Practices

Step-by-Step Usage Process

1. Prepare Target URLs: Collect Zalando product page URLs from your target markets. Ensure URLs are direct product pages, not category or search result pages.

2. Configure Parameters: Set appropriate retry limits (2-3 recommended), enable proxy usage for large-scale operations, and define review limits based on your analysis needs.

3. Execute Scraping: Run the scraper with your configuration. Monitor progress and adjust parameters if needed for optimal results.

4. Data Validation: Verify extracted data completeness and accuracy, particularly for verified purchase status and rating consistency.

Optimization Tips

Choose proxy countries matching your target Zalando domains for better performance and reduced blocking risks. For comprehensive analysis, limit initial runs to 20-50 reviews per product to assess data quality before scaling up. Schedule regular scraping sessions to track review trends over time rather than one-time bulk extractions.

Common Issue Resolution

If encountering access limitations, ensure proper proxy configuration and consider reducing concurrent requests. For incomplete data extraction, verify that target URLs are direct product pages and adjust retry parameters accordingly.

Business Value and Strategic Applications

The extracted review data delivers immediate value across multiple business functions. Time efficiency is dramatically improved, with automated collection reducing manual data gathering from days to hours. Competitive intelligence becomes actionable through systematic comparison of customer sentiment across brands and products.

Market research capabilities expand significantly with access to authentic customer voices across different European markets, enabling better product positioning and feature development. Quality assurance teams can identify recurring issues and improvement opportunities directly from customer feedback, while marketing teams gain insights for messaging optimization and influencer identification.

The data supports strategic decision-making in product development, inventory management, and market expansion by providing quantitative and qualitative customer insights at scale.

Unlock Customer Intelligence Today

The Zalando Reviews Scraper transforms how fashion businesses understand their customers and market position. By automating review data collection, you gain access to authentic customer insights that drive informed business decisions and competitive advantages.

Start extracting valuable customer feedback today and transform raw review data into actionable business intelligence that drives growth and customer satisfaction.

Related Actors

• Zalando Product Search Scraper: A specialized data extraction solution engineered to harvest comprehensive product information from Zalando's marketplace.

• Zalando Product Details Scraper: A specialized data extraction solution engineered to harvest comprehensive product information from Zalando's marketplace.

Your feedback

We are always working to improve Actors' performance. So, if you have any technical feedback about Zalando Reviews Scraper or simply found a bug, please create an issue on the Actor's Issues tab in Apify Console.