Daraz.pk Product Search Scraper: Extract Pakistani E-commerce Data Efficiently

Understanding Daraz.pk and Its Market Value

Daraz.pk dominates Pakistan's e-commerce landscape as the country's leading online shopping platform, offering millions of products across electronics, fashion, home goods, and more. As part of the Alibaba Group ecosystem, Daraz connects local and international sellers with Pakistani consumers from Karachi to Lahore.

For e-commerce businesses tracking competitor pricing, market researchers analyzing consumer trends, or retailers monitoring product availability, Daraz data provides critical market intelligence. Manual collection across thousands of products and categories is impractical. This scraper automates extraction from search result pages, delivering structured datasets for immediate analysis.

What This Scraper Extracts

The Daraz.pk scraper processes product search pages—results displayed after applying category filters, search queries, or price ranges. It captures comprehensive product data from listings, ideal for building competitive intelligence databases.

Key Data Extracted:

Product Identification: Name, NID (Node ID), Item ID, and SKU information for tracking specific products across the platform.

Pricing Intelligence: Price, original price, price display format, and promotional details reveal pricing strategies and discount patterns.

Performance Metrics: Rating score, review counts, and items sold statistics indicate product popularity and customer satisfaction.

Visual Assets: Primary images and thumbnail arrays provide product presentation documentation.

Seller Information: Seller details, brand name, brand ID, and location data enable vendor analysis and geographic tracking.

Inventory Data: Stock status, SKU variants, cheapest SKU, and SKU type indicate availability and product variations.

Marketing Elements: Icons (badges), promotion IDs, ad flags, and grid title formatting reveal promotional strategies.

Product Classification: Categories array and T Item Type provide taxonomy for market segmentation.

Navigation Data: Item URL, direct similar URL, and click trace enable product access and tracking user behavior patterns.

Technical Metadata: UT Log Map and restricted age flags provide platform-specific information.

Input Configuration

The scraper processes Daraz product search URLs containing category filters, search queries, and pagination parameters.

Example Configuration:

{
  "proxy": {
    "useApifyProxy": false
  },
  "max_items_per_url": 20,
  "ignore_url_failures": true,
  "urls": [
    "https://www.daraz.pk/invisible-unisex-fashion-3/?clickTrackInfo=...&page=2&q=hoodies%2B%2Bsweatshirts"
  ]
}

Example Screenshot:

Parameter Breakdown:

proxy: Default false for testing. Enable useApifyProxy: true with residential proxies for large-scale operations to avoid detection.

max_items_per_url: Limits extraction per page. Set to 20 for standard results. Daraz typically displays 40 items per page, so adjust higher (40-50) for complete page extraction.

ignore_url_failures: When true, continues processing if individual URLs fail. Critical for batch scraping multiple categories or pages.

urls: Product search or category page URLs. Include multiple URLs for different categories, search terms, or pagination. URL contains encoded parameters for category, search query, and page number.

URL Structure: Daraz URLs contain tracking parameters (clickTrackInfo, pvid, spm), category identifiers (categoryId), search queries (q), and pagination (page=2). Perform manual searches first, then copy URLs with desired filters applied.

Output Structure and Field Meanings

The scraper returns JSON arrays with detailed product objects.

Core Product Fields:

• Name: Product title as displayed on search results
• NID/Item ID: Platform identifiers for tracking specific products across scrapes
• Item URL: Direct link to product detail page
• Description: Brief product description from listing

Pricing Fields:

• Price: Current selling price in Pakistani Rupees (PKR)
• Original Price: Pre-discount price showing markdown amount
• Price Show/Original Price Show: Formatted price strings as displayed to users
• Cheapest SKU: Lowest-priced variant for products with multiple options

Performance Indicators:

• Rating Score: Average customer rating (typically 0-5 scale)
• Review: Number of customer reviews indicating product feedback volume
• Item Sold Count Show: Formatted display of units sold, revealing popularity

Visual Assets:

• Image: Primary product image URL
• Thumbs: Array of thumbnail images for product gallery

Seller & Brand Data:

• Seller: Vendor/merchant information
• Brand Name/Brand ID: Product brand for competitive analysis
• Location: Seller geographic location (city/region)

Inventory Management:

• In Stock: Boolean indicating current availability
• SKUs: Array of all product variants (sizes, colors, configurations)
• SKU/SKU ID/SKU Type: Specific variant identifiers and classifications

Marketing Elements:

• Icons: Badge array showing features (Free Shipping, Flash Sale, Mall Product)
• Promotion ID: Active promotion identifier for tracking campaigns
• Ad Flag: Indicates sponsored/promoted listings
• Grid Title Line: Formatted title for grid display

Categorization:

• Categories: Product category hierarchy for market segmentation
• T Item Type: Product type classification
• Restricted Age: Age restriction flags for regulated products

Technical Tracking:

• Click Trace: Tracking parameters for user interaction monitoring
• UT Log Map: Platform analytics mapping
• Direct Similar URL: Link to similar products feature

Sample Output:

[
  {
  "name": "Cute Panda Printed Hoodie For Men's & Women's",
  "nid": "599090798",
  "item_id": "599090798",
  "icons": [
    {
      "dom_class": "144231",
      "type": "img",
      "group": "6",
      "show_type": "0"
    },
    {
      "dom_class": "116218",
      "text": "Voucher applied",
      "type": "text",
      "group": "2",
      "show_type": "0"
    },
    {
      "dom_class": "209133",
      "type": "img",
      "group": "3",
      "show_type": "0"
    }
  ],
  "image": "https://static-01.daraz.pk/p/6ee2aaf05d94687132508f29177681ba.jpg",
  "ut_log_map": {
    "srp_name": "LazadaMainSrp",
    "x_object_type": "item",
    "src": "organic",
    "traffic_type": "organic",
    "x_sku_ids": "3994588577",
    "promotion_price": "645",
    "x_item_ids": "599090798",
    "icon_list": "144231;116218;209133",
    "s_n": "82cb633f5d418fb2acaaa19c10ffba0b",
    "current_price": "685.99",
    "x_object_id": "599090798"
  },
  "original_price_show": "",
  "price_show": "Rs. 645",
  "rating_score": "4.744736842105263",
  "review": "760",
  "location": "Sindh",
  "description": [
    "Product Description=============Condition:NewFabric: Fleece.StylishRegular Fit.Export Quality Leftover Products.Soft and comfortable.Random colours will be delivered if posted colours are not available.All products are available on wholesale prices. 【Care instructions】 Recommended hand wash or machine wash with cold water, do not soak the coat in water for a long time.Please allow 2-3CM size differences due to manual measurement.* Product images are for illustrative purposes only and may differ from the product due to differences in monitors; colours of products may also appear different from those shown on the site. *thanks for your understanding!"
  ],
  "thumbs": [],
  "seller": {
    "seller_name": "A.Outfits ",
    "seller_id": "6005158800976"
  },
  "brand_name": "No Brand",
  "brand_id": "39704",
  "cheapest_sku": "599090798_PK-3994588577",
  "sku_id": "3994588577",
  "sku": "599090798_PK",
  "categories": [
    1819,
    10001925,
    10001932,
    10001971
  ],
  "price": "645",
  "restricted_age": null,
  "in_stock": true,
  "original_price": "2500",
  "click_trace": "query:hoodies%2B%2Bsweatshirts;nid:599090798;src:LazadaMainSrp;rn:82cb633f5d418fb2acaaa19c10ffba0b;region:pk;sku:599090798_PK;price:685.99;client:mobile;supplier_id:6005158800976;biz_source:hp_categories;slot:0;utlog_bucket_id:470687;asc_category_id:10001971;item_id:599090798;sku_id:3994588577;shop_id:1560530;templateInfo:-1_A3_C#1120_L#",
  "item_sold_cnt_show": "3.6K sold",
  "skus": [
    {
      "id": "599090798_PK-3994588577"
    }
  ],
  "promotion_id": "",
  "t_item_type": "nt_product",
  "sku_type": "",
  "ad_flag": "0",
  "direct_similar_url": "https://native.m.lazada.com/dynamicxresult?item_img=https%3A%2F%2Fstatic-01.daraz.pk%2Fp%2F6ee2aaf05d94687132508f29177681ba.jpg&src=srp_findsimilar&item_id=599090798&sku_id=3994588577&params=%7B%22sub_src%22%3A%22long_press%22%2C%22src%22%3A%22srp_findsimilar%22%7D&m=tpp_findSimilar&q=hoodies%252B%252Bsweatshirts&similarType=findSimilarV1&price=645.00&spuTriggerItem=0",
  "grid_title_line": "2",
  "item_url": "//www.daraz.pk/products/-i599090798.html",
  "from_url": "https://www.daraz.pk/invisible-unisex-fashion-3/?clickTrackInfo=matchType--20___description--237K%252B%2Bsearch___seedItemMatchType--c2i___bucket--0___spm_id--category.hp___seedItemScore--0.0___abId--379344___score--0.1___pvid--c761fd39-ded8-4005-b0f2-5ceb8de2c38d___refer--___appId--7253___seedItemId--170714327___scm--1007.17253.379344.0___categoryId--10001971___timestamp--1767356326893&from=hp_categories&item_id=170714327&page=2&params=%7B%22catIdLv1%22%3A%221819%22%2C%22pvid%22%3A%22c761fd39-ded8-4005-b0f2-5ceb8de2c38d%22%2C%22src%22%3A%22ald%22%2C%22categoryName%22%3A%22Hoodies%2B%2BSweatshirts%22%2C%22categoryId%22%3A%2210001971%22%7D&q=hoodies%2B%2Bsweatshirts&service=hp_categories&spm=a2a0e.tm80335142.categoriesPC.d_5_10001971&src=hp_categories&up_id=170714327&version=v2"
}
]

Step-by-Step Implementation

1. Define Target Products: Identify categories, brands, or search terms relevant to your research. Test searches on Daraz manually to verify results.

2. Collect Search URLs: Copy URLs from manual searches. For comprehensive data, gather URLs across multiple categories, price ranges, and pages.

3. Configure Input: Set max_items_per_url to 40 for full page extraction. Enable ignore_url_failures for resilience across multiple URLs.

4. Launch Scraper: Run via Apify console. Processing 3-5 pages with 40 items typically completes in 2-3 minutes.

5. Validate Results: Check that pricing, availability, and seller data populate correctly. Verify item IDs match expected products.

6. Export Data: Download as JSON for databases, CSV for spreadsheets, or Excel for business analysis.

7. Handle Pagination: Include sequential page URLs (page=1, page=2, etc.) for datasets exceeding single-page results.

Strategic Applications

Price Monitoring: Track competitor pricing across categories. Identify price trends, seasonal discounts, and promotional patterns. Calculate average prices and detect outliers.

Competitor Analysis: Monitor competitor product ranges, pricing strategies, stock levels, and customer ratings. Identify market gaps and opportunities.

Market Research: Analyze product popularity via ratings, reviews, and sales counts. Understand consumer preferences across categories and price points.

Inventory Intelligence: Track stock status across sellers and categories. Identify frequently out-of-stock items indicating high demand.

Seller Performance: Aggregate by seller field to analyze vendor market share, average ratings, pricing strategies, and geographic distribution.

Brand Monitoring: Track your brand's product listings, pricing compliance, stock levels, and customer feedback across authorized and unauthorized sellers.

Promotional Analysis: Monitor promotion IDs and ad flags to understand competitor marketing tactics. Track icon badges like "Free Shipping" or "Flash Sale" prevalence.

Category Trends: Analyze category distributions to identify growing segments, saturated markets, and emerging product types.

Advanced Techniques

Time-Series Price Tracking: Scrape daily or weekly, storing historical data. Track price fluctuations, identify optimal purchase timing, and detect dynamic pricing patterns.

Rating Correlation Analysis: Analyze relationship between ratings, reviews, sales counts, and pricing. Identify pricing sweet spots and quality expectations.

Geographic Market Analysis: Segment by location field to understand regional seller distribution, pricing variations, and market coverage.

SKU Optimization: Analyze SKU arrays to understand variant offerings. Identify which variations (colors, sizes) perform best in different categories.

Promotion Effectiveness: Track products before, during, and after promotions. Measure impact on sales counts and ratings.

Cross-Category Comparison: Compare same brands across categories to identify pricing strategies, product positioning, and market focus.

Seller Benchmarking: Build seller databases tracking total products, average ratings, price points, and category specialization.

Best Practices

Scraping Frequency: E-commerce pricing changes frequently. Daily scraping suits price monitoring and competitive intelligence. Weekly scraping works for broader market trend analysis.

Data Validation: Verify prices are positive numbers, ratings fall within valid ranges (0-5), and required fields populate. Flag missing or anomalous data.

Currency Handling: All prices in Pakistani Rupees (PKR). Note scrape dates for exchange rate conversion if needed for international analysis.

Duplicate Management: Use Item ID or NID to deduplicate across multiple scrapes or overlapping category searches.

Stock Status Monitoring: Track "In Stock" field changes over time to understand inventory velocity and restock patterns.

Privacy Compliance: Seller contact information may be personal data. Handle responsibly per Pakistani data protection norms and platform terms.

Rate Limiting: For large-scale operations, implement delays between requests or use proxy rotation to avoid platform restrictions.

Conclusion

The Daraz.pk Product Search Scraper transforms Pakistan's largest e-commerce marketplace into actionable business intelligence. From competitive pricing to inventory tracking, this tool delivers comprehensive insights into Pakistani online retail. Whether monitoring competitors, researching markets, tracking brands, or analyzing consumer trends, structured Daraz data enables data-driven decisions in Pakistan's rapidly growing digital commerce landscape.