The residential proxy IP network market is experiencing robust growth, driven by increasing demand for data scraping, web automation, and online privacy solutions. Businesses across various sectors, including market research, e-commerce, and social media monitoring, rely on residential proxies to circumvent geo-restrictions, enhance data collection accuracy, and avoid IP blocking. The market's expansion is fueled by the rise of big data analytics, the growing need for real-time insights, and the increasing sophistication of anti-scraping technologies. While challenges exist, such as regulatory scrutiny regarding data privacy and the ethical considerations surrounding web scraping, the overall market outlook remains positive. Assuming a conservative CAGR of 15% (a common growth rate for technology sectors experiencing high demand) and a 2025 market size of $1.5 billion, the market is projected to reach approximately $3.5 billion by 2033. This growth is underpinned by the continuous development of more sophisticated proxy technologies and the expanding user base across different market segments like large enterprises and SMEs who are increasingly recognizing the value proposition of residential proxies. The segmentation of the market into services, software, and applications tailored for large enterprises and SMEs reflects the varied needs and technological capabilities of different user groups. Geographically, North America and Europe currently hold significant market shares due to higher adoption rates and established technological infrastructure. However, growth in Asia-Pacific and other emerging regions is expected to accelerate as internet penetration and digitalization increase. Competition is intense, with numerous providers vying for market share through technological innovation, pricing strategies, and the development of robust and reliable infrastructure. The market continues to evolve, with a clear emphasis on providing enhanced security, anonymity, and performance to address the increasing demands of a sophisticated user base.

Gain a complete view of the real estate market with our Zillow datasets. Track price trends, rental/sale status, and price per square foot with the Zillow Price History dataset and explore detailed listings with prices, locations, and features using the Zillow Properties Listing dataset. Over 134M records available Price starts at $250/100K records Data formats are available in JSON, NDJSON, CSV, XLSX and Parquet. 100% ethical and compliant data collection Included datapoints:

Zpid
City
State
Home Status
Street Address
Zipcode
Home Type
Living Area Value
Bedrooms
Bathrooms
Price
Property Type
Date Sold
Annual Homeowners Insurance
Price Per Square Foot
Rent Zestimate
Tax Assessed Value
Zestimate
Home Values
Lot Area
Lot Area Unit
Living Area
Living Area Units
Property Tax Rate
Page View Count
Favorite Count
Time On Zillow
Time Zone
Abbreviated Address
Brokerage Name
And much more

Gain extensive insights with our Amazon datasets, encompassing detailed product information including pricing, reviews, ratings, brand names, product categories, sellers, ASINs, images, and much more. Ideal for market researchers, data analysts, and eCommerce professionals looking to excel in the competitive online marketplace. Over 425M records available Price starts at $250/100K records Data formats are available in JSON, NDJSON, CSV, XLSX and Parquet. 100% ethical and compliant data collection Included datapoints:

Title Asin Main Image Brand Name Description Availability Subcategory Categories Parent Asin Type Product Type Name Model Number Manufacturer Color Size Date First Available Released Model Year Item Model Number Part Number Price Total Reviews Total Ratings Average Rating Features Best Sellers Rank Subcategory Buybox Buybox Seller Id Buybox Is Amazon Images Product URL And more

Bright Data’s retail data collector is uniquely crafted to enable your digital commerce business gain a competitive edge by collecting key data sets, including: - Pricing - Competitive landscape - Special offers - Customer reviews - Pictures and videos - Seller ratings - Consumer search trends - Search engine results for products, stores and websites - Competitor advertisement scanning

This data enables you to be dynamic and adapt to real-time market realities and trends.

The Balenciaga dataset offers a comprehensive collection of data regarding products offered by the esteemed Balenciaga brand. It includes essential attributes such as product name, description, and seller information, enabling businesses and analysts to gain insights into the fashion offerings from Balenciaga. The dataset captures pricing information with attributes such as initial price, final price, and currency, allowing businesses to analyze pricing trends and evaluate the pricing strategies employed by Balenciaga. Additionally, the availability status, indicated by the "in_stock" attribute, provides valuable information for inventory management and customer service purposes.

Leverage our Airbnb dataset to gain comprehensive insights into global short-term rental markets. Track property details, pricing trends, reviews, availability, and amenities to optimize pricing strategies, conduct market research, or enhance travel-related applications. Data points may include listing ID, host ID, property type, price, number of reviews, ratings, availability, and more. The dataset is available as a full dataset or a customized subset tailored to your specific needs.

The no-code web scraping tool market is experiencing robust growth, driven by the increasing demand for automated data extraction across diverse sectors. The market's expansion is fueled by several key factors. Firstly, the rise of e-commerce and the need for competitive pricing intelligence necessitates efficient data collection. Secondly, the travel and hospitality industries leverage web scraping for dynamic pricing and competitor analysis. Thirdly, academic research, finance, and human resources departments utilize these tools for large-scale data analysis and trend identification. The ease of use offered by no-code platforms democratizes web scraping, eliminating the need for coding expertise, and significantly accelerating the data acquisition process. This accessibility attracts a wider user base, contributing to market expansion. The market is segmented by application (e-commerce, travel & hospitality, academic research, finance, human resources, and others) and type (text-based, cloud-based, and API-based web scrapers). While the market is competitive, with numerous players offering varying functionalities and pricing models, the continued growth in data-driven decision-making across industries assures continued expansion. Cloud-based solutions are expected to dominate due to scalability and ease of access. Future growth hinges on the development of more sophisticated no-code platforms offering enhanced features such as AI-powered data cleaning and intelligent data analysis capabilities. Geographic regions like North America and Europe currently hold significant market share, but Asia-Pacific is poised for substantial growth due to increasing digital adoption and expanding e-commerce markets. The historical period (2019-2024) likely witnessed a moderate growth rate, setting the stage for the accelerated expansion projected for the forecast period (2025-2033). Assuming a conservative CAGR of 15% for the historical period, resulting in a 2024 market size of approximately $500 million, and applying a slightly higher CAGR of 20% for the forecast period, reflects the increasing adoption and sophistication of these tools. Factors such as stringent data privacy regulations and the increasing sophistication of anti-scraping measures present potential restraints, but innovative solutions are emerging to address these challenges, including ethical data sourcing and advanced proxy management features. The ongoing integration of AI and machine learning capabilities into no-code platforms is also expected to propel market growth, enabling more sophisticated data extraction and analysis with minimal user input.

Amazon Products dataset to explore detailed product listings, pricing, reviews, and sales data. Popular use cases include competitive analysis, market trend forecasting, and e-commerce strategy optimization.

Use our Amazon Products dataset to explore detailed information on products across various categories, including pricing, reviews, ratings, and sales data. This dataset is ideal for e-commerce professionals, market analysts, and product managers looking to analyze market trends, optimize product listings, and refine competitive strategies.

Leverage this dataset to track pricing trends, assess customer feedback, and uncover popular product categories. Whether you're conducting competitive analysis, performing market research, or optimizing product strategies, the Amazon Products dataset provides key insights to stay ahead in the e-commerce landscape.

Dataset Features

• Title: The name or title of the product.
• seller_name: The name of the seller offering the product.
• Brand: The brand associated with the product.
• Description: A detailed description of the product, including key features.
• initial_price: The original price of the product before any discounts.
• final_price: The current price of the product after discounts.
• Currency: The currency in which the product is priced (e.g., GBP, USD).
• Availability: The stock status (e.g., in stock, out of stock).
• reviews_count: The total number of customer reviews.
• Categories: The specific category the product belongs to.
• asin: Amazon Standard Identification Number.
• buybox_seller: The seller currently winning the Amazon Buy Box.
• number_of_sellers: The number of sellers offering this product.
• root_bs_rank: The overall ranking of the product in the Amazon best-sellers list.
• answered_questions: The number of questions answered in the product Q&A section.
• domain: The website domain where the product is being sold.
• images_count: The number of images available for the product.
• URL: The link to the product page on Amazon.
• video_count: The number of videos available for the product.
• image_url: The URL of the primary image associated with the product.
• item_weight: The weight of the product.
• Rating: The average rating of the product based on customer reviews.
• product_dimensions: The dimensions of the product (e.g., length, width, height) and weight.
• seller_id: The unique identifier for the seller.
• date_first_available: The date when the product was first made available on Amazon.
• discount: Any discount applied to the product.
• model_number: The model number of the product.
• manufacturer: The company that manufactures the product.
• department: The department under which the product is categorized (e.g., Health & Household).
• plus_content: A flag indicating if the product has Amazon’s “Plus Content” (additional marketing content).
• upc: The Universal Product Code (UPC) associated with the product.
• video: URL(s) of any video content associated with the product.
• top_review: A summary or excerpt from the top customer review.
• variations: Different product variations (e.g., different sizes or flavors).
• delivery: Information on the delivery options (e.g., free delivery or Prime delivery).
• features: Key features or highlights of the product.
• format: The format of the product (e.g., powder, liquid).
• buybox_prices: Pricing details for the product, including the base and tiered prices.
• parent_asin: The ASIN of the parent product (if the product is part of a larger group of similar products).
• input_asin: The ASIN of the product as input for Amazon searches.
• ingredients: List of ingredients in the product (if applicable).
• origin_url: The source URL for product-related information or ingredients.
• bought_past_month: A flag indicating if the product was bought in the past month.
• is_available: Availability status of the product (True/False).
• root_bs_category: The broad product category (e.g., Health & Household).
• bs_category: The specific subcategory the product belongs to.
• bs_rank: The rank of the product in its specific subcategory.
• badge: Any badge or label the product has earned (e.g., Amazon's Choice).
• subcategory_rank: The rank of the product within its subcategory.
• amazon_choice: A flag indicating if the product has been selected as Amazon’s Choice.
• images: A list of URLs for additional product images.
• product_details: Detailed product specifications and features.
• prices_breakdown: A breakdown of the price, including any discounts or promotions.
• country_of_origin: The country where the product is made.
• from_the_brand: Information from the brand or manufact

Gain valuable insights into consumer behavior, market trends, and competitive analysis with our comprehensive Taobao Dataset. Designed for businesses, analysts, and e-commerce professionals, this dataset provides structured and reliable data from Taobao to enhance product development, pricing strategies, and sales optimization.

Dataset Features

Product Listings: Access detailed product data, including titles, descriptions, categories, pricing, and availability. Seller Information: Extract seller details, including store names, ratings, locations, and customer feedback. Customer Reviews & Ratings: Analyze user-generated reviews, star ratings, and sentiment trends to understand consumer preferences. Pricing & Discounts: Track product pricing, promotional offers, and historical price changes to optimize pricing strategies. Sales & Demand Trends: Monitor product popularity, sales volume, and seasonal demand fluctuations.

Customizable Subsets for Specific Needs Our Taobao Dataset is fully customizable, allowing you to filter data based on product categories, pricing, seller ratings, or customer sentiment. Whether you need broad coverage for market research or focused data for competitive intelligence, we tailor the dataset to your needs.

Popular Use Cases

Consumer Behavior Analysis: Understand purchasing patterns, customer preferences, and emerging trends. Competitive Intelligence: Track competitor pricing, product performance, and sales strategies. Market Research & Trend Forecasting: Identify high-demand products, seasonal trends, and emerging market opportunities. AI & Machine Learning Applications: Use structured e-commerce data to train AI models for recommendation engines, demand forecasting, and automated pricing. Supply Chain Optimization: Monitor inventory levels, shipping trends, and supplier performance to enhance logistics and fulfillment strategies.

Whether you're optimizing pricing, analyzing consumer trends, or enhancing your e-commerce strategy, our Taobao Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

Bright Horizons Family Solutions reported $509.79M in Cost of Sales for its fiscal quarter ending in March of 2025. Data for Bright Horizons Family Solutions | BFAM - Cost Of Sales including historical, tables and charts were last updated by Trading Economics this last July in 2025.

Zoopla Properties Listing dataset to explore detailed property information, including pricing, location, and features. Popular use cases include real estate market analysis, property valuation, and investment research.

Use our Zoopla Properties Listing Information dataset to explore detailed property listings, including property details, pricing, location, and market trends across various regions. This dataset provides valuable insights into property valuations, consumer preferences, and real estate dynamics, enabling businesses and researchers to make data-driven decisions.

Tailored for real estate professionals, investors, and market analysts, this dataset supports market trend analysis, property valuation assessments, and investment strategy development. Whether you're evaluating property investments, tracking market conditions, or conducting competitive analysis, the Zoopla Properties Listing Information dataset is a key resource for navigating the real estate landscape.

Dataset Features

• url: The original listing URL on Zoopla.
• property_type: Type of property (e.g., Flat, Detached, Terraced).
• property_title: Title or headline of the listing.
• address: Full postal address of the property.
• google_map_location: Geographical coordinates (latitude, longitude).
• virtual_tour: Link to a virtual walkthrough or 360° tour.
• street_view: Link to the Google Street View of the property.
• url_property: Zoopla-specific property page URL.
• currency: Currency in which the property is priced.
• deposit: Security deposit required (typically for rentals).
• letting_arrangements: Letting details (e.g., short-term, long-term).
• breadcrumbs: Category breadcrumbs for location and type navigation.
• availability: Availability status (e.g., Available now, Under offer).
• commonhold_details: Information about commonhold ownership.
• service_charge: Annual service charge (for leasehold properties).
• ground_rent: Annual ground rent cost.
• time_remaining_on_lease: Lease duration remaining in years.
• ecp_rating: Energy Performance Certificate rating.
• council_tax_band: Council tax band.
• price_per_size: Price per square meter or foot.
• tenure: Tenure type (Freehold, Leasehold, etc.).
• tags: Descriptive tags (e.g., New build, Chain-free).
• features: List of property features (e.g., garden, garage, en-suite).
• property_images: URLs to property photos.
• additional_links: Other related links (e.g., brochures, agents).
• listing_history: Changes in price, listing dates, and status over time.
• agent_details: Information about the listing agent or agency.
• points_ofInterest: Nearby landmarks or facilities (schools, transport).
• bedrooms Number of bedrooms.
• price: Listed price of the property.
• bathrooms: Number of bathrooms.
• receptions: Number of reception rooms (living, dining, etc.).
• country_code: Country code of the listing (e.g., GB for UK).
• energy_performance_certificate: Detailed EPC documentation or summary.
• floor_plans: URL or data related to property floor plans.
• description: Detailed property description from the listing.
• price_per_time: Price frequency for rentals (e.g., per week, per month).
• property_size: Area of the property (in sq ft or sq m).
• market_stats_last_12_months: Market stats for the area over the past year.
• market_stats_renta_opportunities: Data on rental yields and opportunities.
• market_stats_recent_sales_nearby: Sales history for nearby properties.
• market_stats_rental_activity: Local rental activity trends.
• uprn: Unique Property Reference Number for UK properties.
• listing_label: Label/category of the listing.

Distribution

• Data Volume: 44 Columns and 95.92K Rows
• Format: CSV

Usage

This dataset is ideal for a variety of high-impact applications:

• Property Valuation Models: Train ML models to estimate market value using features like size, location, and amenities.
• Real Estate Market Analysis: Identify pricing trends, demand patterns, and neighbourhood growth over time.
• Investment Research: Analyse rental yields, price per square foot, and historical price changes for investment opportunities.
• Recommendation Systems: Develop intelligent recommendation engines for property buyers and renters.
• Urban Planning & Policy Making: Use location and infrastructure data to guide city development.
• Sentiment & Description Analysis: NLP-driven insights from listing descriptions and agent narratives.

Coverage

• Geographic Coverage: Global
• Time Range: Ongoing collection; historical data may span multiple years

License

CUSTOM

Please review the respective licenses below:

1. Data Provider's License
   -

Dataset containing over 5000 data metrics (including raw data and BQ calculated scores & metrics) for over 4000 public companies (~95% of the Russell 3000). Includes financials (from SEC filings) as well as data that is not reported to the SEC, including monthly headcount, detailed employee benefits data, credit events related to contributions to benefits plans. Also includes BQ scores, industry and macro statistics that provide a comprehensive view of the sector & industry.

BQ's Public Companies dataset is applicable to both quantitative investment managers as well as fundamentals public equity investors, who wish to use alternative (non-financial) data to enhance their investment analysis and investment decisions.

Explore a diverse range of fashion items, home goods, and more, with insights into pricing, availability, ratings, and reviews. Popular use cases include trend forecasting, pricing optimization, and inventory management in the fast-fashion market.

The Shein.com Products dataset provides a detailed overview of the extensive product range available on Shein, offering key insights into the fast-fashion market. This dataset includes essential details such as product names, prices, discounts, descriptions, materials, product images, SKUs (Stock Keeping Units), low-stock indicators, and more.

Ideal for eCommerce professionals, fashion analysts, and market strategists, this dataset supports trend analysis, pricing strategies, and inventory management. Whether you're benchmarking competitors, identifying emerging trends, or optimizing your product offerings, the Shein.com Products dataset delivers valuable insights to stay ahead in the dynamic fashion industry.

Dataset Features

• product_name: The name/title of the product listed.
• description: A brief description of the product, including features or materials.
• initial_price: The original price of the product before any discounts.
• final_price: The actual selling price after applying discounts.
• currency: The currency in which the price is listed (e.g., USD).
• in_stock: Availability status of the product (True if in stock, otherwise False).
• color: Available color(s) for the product.
• size: Size(s) available (e.g., S, M, L, or custom sizes).
• reviews_count: Number of user reviews the product has received.
• main_image: URL to the primary product image.
• category_url: Link to the category page the product belongs to.
• url: Direct link to the product page.
• category_tree: Hierarchical path of the product category.
• country_code: Country code indicating where the product is available.
• domain: The Shein domain where the product was found (e.g., shein.com, shein.uk).
• image_count: Total number of product images.
• image_urls: List/array of URLs for all images related to the product.
• model_number: The product’s model or SKU number.
• offers: Details of promotions or discounts available.
• other_attributes: Miscellaneous product features or labels (e.g., eco-friendly, plus-size).
• product_id: Unique identifier for the product.
• rating: Average user rating (typically on a 5-star scale).
• related_products: List of similar or related products.
• root_category: The broadest category classification (e.g., "Women", "Home").
• top_reviews: Highlighted customer reviews.
• category: Specific product category (e.g., "Bikinis", "T-Shirts").
• brand: Brand name (often "Shein" or sub-brands).
• all_available_sizes: List of all size options for the product.
• category_details: Additional metadata about the product category.
• initial_price_usd: Original price converted to USD.
• final_price_usd: Final price converted to USD.
• discount_price: Price discount amount (initial - final).
• discount_price_usd: Discount amount in USD.
• colors: All color variants of the product.
• store_details: Information about the store or seller.
• shipping_details: Information about shipping costs and delivery time.
• shipping_type: Type of shipping offered (e.g., standard, express).
• product_parent_id: ID representing a grouped product variant.
• tags: Keywords or tags associated with the product.
• model_data: Additional attributes from the product model (could include fit, cut, etc.).

Distribution

• Data Volume: 40 Columns and 42.35 M Rows
• Format: CSV

Usage

This dataset is ideal for a wide range of practical and analytical applications: - Trend Forecasting: Identify emerging fashion trends based on product popularity and review sentiment.
- Pricing Optimization: Analyze discount strategies and dynamic pricing patterns.
- Inventory Management: Monitor stock availability and detect low-stock patterns.
- Recommendation Systems: Build personalized fashion recommendations using product attributes and user ratings.
- Market Benchmarking: Compare Shein's offerings with competitors or across regions.
- Computer Vision: Use product images for training models in visual fashion recognition.

Coverage

• Geographic Coverage: Global
• Time Range: Varies by data collection; generally recent and can be updated periodically.

License

CUSTOM

Please review the respective licenses below:

1. Data Provider's License
   • Bright Data Master Service Agreement

Who Can Use It

• Data Scientists: For training ML models like price predictors, review sentiment classifiers, or image-based search engines.
• Researchers:

Our travel datasets provide extensive, structured data covering various aspects of the global travel and hospitality industry. These datasets are ideal for businesses, analysts, and developers looking to gain insights into hotel pricing, short-term rentals, restaurant listings, and travel trends. Whether you're optimizing pricing strategies, analyzing market trends, or enhancing travel-related applications, our datasets offer the depth and accuracy you need.

Key Travel Datasets Available:

  Hotel & Rental Listings: Access detailed data on hotel properties, short-term rentals, and vacation stays from platforms like 
    Airbnb, Booking.com, and other OTAs. This includes property details, pricing, availability, guest reviews, and amenities.

  Real-Time & Historical Pricing Data: Track hotel room pricing, rental occupancy rates, and pricing trends 
    to optimize revenue management and competitive analysis.

  Restaurant Listings & Reviews: Explore restaurant data from Tripadvisor, OpenTable, Zomato, Deliveroo, and Talabat, 
    including restaurant details, customer ratings, menus, and delivery availability.

  Market & Trend Analysis: Use structured datasets to analyze travel demand, seasonal trends, and consumer preferences 
    across different regions.

  Geo-Targeted Data: Get location-specific insights with city, state, and country-level segmentation, 
    allowing for precise market research and localized business strategies.



Use Cases for Travel Datasets:

  Dynamic Pricing & Revenue Optimization: Adjust pricing strategies based on real-time market trends and competitor analysis.
  Market Research & Competitive Intelligence: Identify emerging travel trends, monitor competitor performance, and assess market demand.
  Travel & Hospitality App Development: Enhance travel platforms with accurate, up-to-date data on hotels, restaurants, and rental properties.
  Investment & Financial Analysis: Evaluate travel industry performance for investment decisions and economic forecasting.



  Our travel datasets are available in multiple formats (JSON, CSV, Excel) and can be delivered via 
  API, cloud storage (AWS, Google Cloud, Azure), or direct download. 
  Stay ahead in the travel industry with high-quality, structured data that powers smarter decisions.

Booking Listings – Free Cancellation 🏨✈️

Booking Listings is a structured snapshot of accommodation offers worldwide as listed on Booking.com. This subset contains only properties that offer free cancellation, enabling analysts and data scientists to study flexible‑booking behaviour, derive pricing strategies, and build recommendation or revenue‑management systems.

Highlights

75 k hotels & apartments across 84 countries Rich pricing & availability metadata (final vs. original… See the full description on the dataset page: https://huggingface.co/datasets/BrightData/Booking.com-Listings.

Gain valuable insights into the automotive market with our comprehensive Car Prices Dataset. Designed for businesses, analysts, and researchers, this dataset provides real-time and historical car pricing data to support market analysis, pricing strategies, and trend forecasting.

Dataset Features

Vehicle Listings: Access detailed car listings, including make, model, year, trim, and specifications. Ideal for tracking market trends and pricing fluctuations. Pricing Data: Get real-time and historical car prices from multiple sources, including dealerships, marketplaces, and private sellers. Market Trends & Valuations: Analyze price changes over time, compare vehicle depreciation rates, and identify emerging pricing trends. Dealer & Seller Information: Extract seller details, including dealership names, locations, and contact information for lead generation and competitive analysis.

Customizable Subsets for Specific Needs Our Car Prices Dataset is fully customizable, allowing you to filter data based on vehicle type, location, price range, and other key attributes. Whether you need a broad dataset for market research or a focused subset for competitive analysis, we tailor the dataset to your needs.

Popular Use Cases

Market Analysis & Pricing Strategy: Track vehicle price trends, compare competitor pricing, and optimize pricing strategies for dealerships and resellers. Automotive Valuation & Depreciation Studies: Analyze historical pricing data to assess vehicle depreciation rates and predict future values. Competitive Intelligence: Monitor competitor pricing, dealership inventory, and promotional offers to stay ahead in the market. Lead Generation & Sales Optimization: Identify potential buyers and sellers, track demand for specific vehicle models, and enhance sales strategies. AI & Predictive Analytics: Leverage structured car pricing data for AI-driven forecasting, automated pricing models, and trend prediction.

Whether you're tracking car prices, analyzing market trends, or optimizing sales strategies, our Car Prices Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

Unlock the full potential of LinkedIn data with our extensive dataset that combines profiles, company information, and job listings into one powerful resource for business decision-making, strategic hiring, competitive analysis, and market trend insights. This all-encompassing dataset is ideal for professionals, recruiters, analysts, and marketers aiming to enhance their strategies and operations across various business functions. Dataset Features

Profiles: Dive into detailed public profiles featuring names, titles, positions, experience, education, skills, and more. Utilize this data for talent sourcing, lead generation, and investment signaling, with a refresh rate ensuring up to 30 million records per month. Companies: Access comprehensive company data including ID, country, industry, size, number of followers, website details, subsidiaries, and posts. Tailored subsets by industry or region provide invaluable insights for CRM enrichment, competitive intelligence, and understanding the startup ecosystem, updated monthly with up to 40 million records. Job Listings: Explore current job opportunities detailed with job titles, company names, locations, and employment specifics such as seniority levels and employment functions. This dataset includes direct application links and real-time application numbers, serving as a crucial tool for job seekers and analysts looking to understand industry trends and the job market dynamics.

Customizable Subsets for Specific Needs Our LinkedIn dataset offers the flexibility to tailor the dataset according to your specific business requirements. Whether you need comprehensive insights across all data points or are focused on specific segments like job listings, company profiles, or individual professional details, we can customize the dataset to match your needs. This modular approach ensures that you get only the data that is most relevant to your objectives, maximizing efficiency and relevance in your strategic applications. Popular Use Cases

Strategic Hiring and Recruiting: Track talent movement, identify growth opportunities, and enhance your recruiting efforts with targeted data. Market Analysis and Competitive Intelligence: Gain a competitive edge by analyzing company growth, industry trends, and strategic opportunities. Lead Generation and CRM Enrichment: Enrich your database with up-to-date company and professional data for targeted marketing and sales strategies. Job Market Insights and Trends: Leverage detailed job listings for a nuanced understanding of employment trends and opportunities, facilitating effective job matching and market analysis. AI-Driven Predictive Analytics: Utilize AI algorithms to analyze large datasets for predicting industry shifts, optimizing business operations, and enhancing decision-making processes based on actionable data insights.

Whether you are mapping out competitive landscapes, sourcing new talent, or analyzing job market trends, our LinkedIn dataset provides the tools you need to succeed. Customize your access to fit specific needs, ensuring that you have the most relevant and timely data at your fingertips.

The Zoopla Dataset provides a detailed repository of information covering property listings available on the Zoopla platform. Tailored to support businesses, researchers, and analysts in the real estate sector, this dataset delivers valuable insights into market trends, property valuations, and consumer preferences within the real estate market.

With key attributes such as property details, pricing data, location information, and listing history, users can conduct thorough analyses to refine property investment strategies, assess market demand, and identify emerging trends.

Whether you're a real estate agent seeking to enhance your property listings, a researcher investigating trends in the housing market, or an analyst aiming to refine investment strategies, the Zoopla Dataset serves as an essential resource for unlocking opportunities and driving success in the competitive landscape of real estate

This dataset offers a comprehensive collection of Webmotors car listings data, providing an in-depth view of the automotive market. It includes key attributes such as product URLs, brand, model, year, mileage, condition, fuel type, color, transmission type, seller details, information about single ownership, product description, price, location, creation date, image URLs, contact phone numbers, seller's email address, physical address, type of seller, specific locality, number of previous owners, premium listing status, category, and unique posting ID. With subsets available by car brand and model, this dataset enables businesses to access and analyze valuable automotive information tailored to their needs.

Users can leverage this dataset for price comparison, inventory optimization, and customer sentiment analysis. The data can help identify competitive pricing strategies, optimize stock levels based on market demand, and understand consumer preferences and feedback. Whether you are looking to enhance your sales strategy, manage your inventory efficiently, or gain insights into customer behavior, this dataset serves as a crucial resource for driving informed decisions and staying competitive in the dynamic automotive market.

Use our Zalando DE & UK products dataset to get a complete snapshot of new products, categories, pricing, and consumer reviews. Depending on your needs, you may purchase the entire dataset or a customized subset. Popular use cases: Identify product inventory gaps and increased demand for certain products, analyze consumer sentiment and define a pricing strategy by locating similar products and categories among your competitors. Beat your eCommerce competitors using a Zalando.de & Zalando.co.uk products dataset to get a complete overview of product pricing, product strategies, and customer reviews. The dataset includes all major data points: Product SKU Currency Timestamp Price Similar products Bought together products Top reviews Rating and more