According to a survey conducted in January 2023, 33 percent of consumers in the United States reported to always read online reviews of local businesses. Over 43 percent said that they regularly read online reviews of local businesses and just two percent said that they never read online reviews.

Our online review trends and statistics unlock valuable insights into consumer perceptions. Empower your business decisions to enhance customer experiences.

In 2021, Google's share of online reviews increased to 71 percent, up from 67 percent in 2020, indicating a rise in willingness from consumers to share their experiences and opinions online. Overall, Google is the platform and search engine on which most consumers leave reviews for local businesses.

During a December 2020 survey of U.S. online customers, 94 percent of respondents stated that positive reviews made them more likely to use a business. On the other hand, 92 percent said that negative reviews made them less likely to patronize a local business.

The Google Reviews dataset is perfect for obtaining comprehensive insights into businesses and their customer feedback globally. Easily filter by location, business type, or reviewer details to extract the precise data you need. The Google Reviews dataset includes key data points such as URL, place ID, place name, country, address, review ID, reviewer name, total reviews and photos by the reviewer, reviewer profile URL, and more. This dataset provides valuable information for sentiment analysis, business comparisons, and customer behavior studies.

A November 2021 survey of online users in the United States found that 81 percent of respondents had used Google as a tool to evaluate local businesses in the past 12 months. Yelp was ranked second with over half of respondents using the review platform for such purpose.

• Don't worry about solving CAPTCHAs, rotating proxies or installing headless browsers
• No need to update scrapers with every minor or major website layout or design change

• Simple pricing, pay per successful result only. Say goodbye to being charged for failed requests.

• Filter results by number of reviews, date

• Review data includes meta data about customers such as avatar, location, profile url, etc.

• Get page meta data like product price information, rating distribution, etc.

Consumer Ratings And Reviews Software Market size is growing at a moderate pace with substantial growth rates over the last few years and is estimated that the market will grow significantly in the forecasted period i.e. 2024 to 2031.

Global Consumer Ratings And Reviews Software Market Drivers

The market drivers for the Consumer Ratings And Reviews Software Market can be influenced by various factors. These may include:

A Growing Priority for Customer Experience: Companies are considering customer experience as a means of gaining a competitive edge. With the use of ratings and reviews software, businesses may better understand their customers’ needs, pinpoint areas for development, and raise customer satisfaction levels.
User-generated information is becoming more and more influential: Online reviews and ratings, for example, have a big influence on consumers’ decisions to buy. Companies monitor and utilise user-generated material for marketing and brand management through the use of rating and review software.
Demand for Social Proof and Trust-Building: In order to make well-informed purchasing decisions, consumers rely on peer reviews and ratings. Software with ratings and reviews enables companies to increase sales, establish credibility with potential clients, and provide social proof.
Importance of Reputation Management: It is imperative that organisations manage their internet reputations. By monitoring and swiftly responding to client input, ratings and reviews software helps organisations mitigate unfavourable reviews and improve their brand’s reputation.
Concentrate on Improving the Quality of Products and Services: Customer feedback gathered by rating and review software offers insightful data on the performance of products and services, allowing companies to pinpoint problems with quality and make the required adjustments.
Effect on SEO and Search Engine Rankings: Enhanced search engine exposure and rankings might result from positive reviews. Software for ratings and reviews assists companies in producing real user content that improves internet presence and SEO.
Rise of Digital and E-Commerce: As e-commerce and digital-commerce platforms expand, so does the need for ratings and reviews software to optimise online shopping experiences, handle consumer feedback, and boost conversions.
Integration with Customer Relationship Management (CRM) Systems: Companies can centralise customer data and use feedback into more comprehensive customer engagement plans by utilising integration capabilities with CRM systems.
Emphasise Making Decisions Based on Data: Software for ratings and reviews offers useful information and analytics that support data-driven decision-making in the areas of product development, customer support, and marketing.
Consumer Protection and Regulatory Compliance: Businesses must implement ratings and reviews software that guarantees compliance and protects consumer information in order to comply with regulations pertaining to consumer rights and data privacy.

We present a collection of Amazon reviews specifically designed to aid research in multilingual text classification. The dataset contains reviews in English, Japanese, German, French, Chinese and Spanish, collected between November 1, 2015 and November 1, 2019. Each record in the dataset contains the review text, the review title, the star rating, an anonymized reviewer ID, an anonymized product ID and the coarse-grained product category (e.g. 'books', 'appliances', etc.)

Annual review of statistics and its application FAQ - ResearchHelpDesk - The Annual Review of Statistics and Its Application informs statisticians, and users of statistics about major methodological advances and the computational tools that allow for their implementation. The Annual Review of Statistics and Its Application debuted in the 2016 Release of the Journal Citation Report (JCR) with an Impact Factor of 3.045.

Context

This dataset consists of reviews of fine foods from amazon. The data span a period of more than 10 years, including all ~500,000 reviews up to October 2012. Reviews include product and user information, ratings, and a plain text review. It also includes reviews from all other Amazon categories.

Contents

• Reviews.csv: Pulled from the corresponding SQLite table named Reviews in database.sqlite
  
• database.sqlite: Contains the table 'Reviews'
  
  

Data includes:
- Reviews from Oct 1999 - Oct 2012
- 568,454 reviews
- 256,059 users
- 74,258 products
- 260 users with > 50 reviews

Acknowledgements

See this SQLite query for a quick sample of the dataset.

If you publish articles based on this dataset, please cite the following paper:

• J. McAuley and J. Leskovec. From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews. WWW, 2013.

Use our Trustpilot dataset to monitor market sentiment and customer satisfaction trends. Access a comprehensive database of customer reviews and ratings for companies across various industries. Tailor your analysis with customized subsets of the data, or opt for the full dataset to gain deeper insights into consumer behavior and competitive benchmarking. The dataset includes all major data points: companies name companies location companies contact details companies rating reviewer names reviews date reviews rating and much more.

In 2021, many online shoppers in the United Kingdom (UK) considered what previous buyers had to say about products before purchasing the items themselves. Approximately nine in ten UK consumers stated they would check online reviews before buying from a particular business. Even more shoppers said they often avoid enterprises with a rating lower than four.

PromptCloud offers unparalleled data extraction services, enabling businesses to access real-time, comprehensive data from the global travel industry. Our Global Travel Data Dataset is a fundamental resource for companies aiming to understand and capitalize on travel market trends. It provides insights into airfare fluctuations, hotel pricing strategies, traveler preferences, and destination popularity. This dataset is invaluable for tracking industry movements, understanding customer sentiment, and staying ahead in the competitive travel sector.

We are committed to putting data at the heart of your business. Reach out for a no-frills PromptCloud experience- professional, technologically ahead and reliable.

Beyond basic travel data, PromptCloud caters to a wide range of travel-related data needs, from airline databases to hotel aggregators. Our advanced web scraping services are fully customizable, allowing clients to choose their data sources, collection frequencies, and specific data points. This flexibility ensures that our data extraction solutions are perfectly tailored to each client's unique requirements. Our sophisticated data aggregation technology allows for efficient extraction from multiple travel sources, making it ideal for travel agencies, hoteliers, and market researchers.

Leveraging over a decade of experience in travel data extraction, PromptCloud stands as an authoritative source in the field. We ensure the highest data quality and reliability, with a rigorous verification process that underscores our commitment to accuracy and trustworthiness.

Product Review Datasets: Uncover user sentiment

Harness the power of Product Review Datasets to understand user sentiment and insights deeply. These datasets are designed to elevate your brand and product feature analysis, help you evaluate your competitive stance, and assess investment risks.

Data sources:

• Trustpilot: datasets encompassing general consumer reviews and ratings across various businesses, products, and services.

Leave the data collection challenges to us and dive straight into market insights with clean, structured, and actionable data, including:

• Product name;
• Product category;
• Number of ratings;
• Ratings average;
• Review title;
• Review body;

Choose from multiple data delivery options to suit your needs:

1. Receive data in easy-to-read formats like spreadsheets or structured JSON files.
2. Select your preferred data storage solutions, including SFTP, Webhooks, Google Cloud Storage, AWS S3, and Microsoft Azure Storage.
3. Tailor data delivery frequencies, whether on-demand or per your agreed schedule.

Why choose Oxylabs?

1. Fresh and accurate data: Access organized, structured, and comprehensive data collected by our leading web scraping professionals.

2. Time and resource savings: Concentrate on your core business goals while we efficiently handle the data extraction process at an affordable cost.

3. Adaptable solutions: Share your specific data requirements, and we'll craft a customized data collection approach to meet your objectives.

4. Legal compliance: Partner with a trusted leader in ethical data collection. Oxylabs is a founding member of the Ethical Web Data Collection Initiative, aligning with GDPR and CCPA standards.

Pricing Options:

Standard Datasets: choose from various ready-to-use datasets with standardized data schemas, priced from $1,000/month.

Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

Experience a seamless journey with Oxylabs:

• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
• Developing a customized solution: Our experts create a custom framework to extract public data using our in-house web scraping infrastructure.
• Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Join the ranks of satisfied customers who appreciate our meticulous attention to detail and personalized support. Experience the power of Product Review Datasets today to uncover valuable insights and enhance decision-making.

These datasets contain reviews from the Steam video game platform, and information about which games were bundled together.

Metadata includes

• reviews

• purchases, plays, recommends (likes)

• product bundles

• pricing information

Basic Statistics:

• Reviews: 7,793,069

• Users: 2,567,538

• Items: 15,474

• Bundles: 615

Get the needed Amazon product review data right from the data extractor! Collect Amazon review information from 19 Amazon countries from the following domains: - amazon.com - amazon.com.au - amazon.com.br - amazon.ca - amazon.cn - amazon.fr - amazon.de - amazon.in - amazon.it - amazon.com.mx - amazon.nl - amazon.sg - amazon.es - amazon.com.tr

Request Ecommerce Product Review dataset by: - keyword - category - seller - product ID (ASIN)

Amazon E-commerce Reviews Data datasets gathered by keyword, seller, category, or ASIN contain: - Product ID (can be extended to the full product information) - Review content and rating - Review metadata

Amazon extraction results can be delivered by schedule or API request, so the data can be extracted in real-time.

DATAANT uses the in-house web scraping service with no concurrency limitations, so unlimited data extractions can be performed simultaneously.

Output can and attributes can be customized to fit your particular needs.

Get instant access to Amazon customer reviews. Fully customizable datasets based on your requirements.

Gather product reviews, including fields like product name, product URL, reviewer name, review rating, review text and description, and data points and fields that look interesting for market insights analysis.

Pricing (no order minimums): • <5000 reviews: $0.05 per row • 5001-50000 leads: $0.04 per row • 50000+ rows: $0.03 per row

Fields: • country • countryCode • date • isVerified • position • productAsin • ratingScore • reviewCategoryUrl • reviewDescription • reviewImages/0 • reviewImages/1 • reviewImages/2 • reviewImages/3 • reviewImages/4 • reviewImages/5 • reviewImages/6 • reviewReaction • reviewTitle • reviewUrl • reviewedIn • totalCategoryRatings • totalCategoryReviews • variant

Annual review of statistics and its application Acceptance Rate - ResearchHelpDesk - The Annual Review of Statistics and Its Application informs statisticians, and users of statistics about major methodological advances and the computational tools that allow for their implementation. The Annual Review of Statistics and Its Application debuted in the 2016 Release of the Journal Citation Report (JCR) with an Impact Factor of 3.045.

What Makes Our Data Unique?

Autoscraping’s Google Places Review Data is a premium resource for organizations seeking in-depth consumer insights from a trusted global platform. What sets our data apart is its sheer volume and quality—spanning over 10 million reviews from Google Places worldwide. Each review includes critical attributes such as ratings, comment titles, comment bodies, and detailed sentiment analysis. This data is meticulously curated to capture the authentic voice of consumers, offering a rich source of information for understanding customer satisfaction, brand perception, and market trends.

Our dataset is unique not only because of its scale but also due to the richness of its metadata. We provide granular details about each review, including the review source, place ID, and post date, allowing for precise temporal and spatial analysis. This level of detail enables users to track changes in consumer sentiment over time, correlate reviews with specific locations, and conduct deep dives into customer feedback across various industries.

Moreover, the dataset is continuously updated to ensure it reflects the most current opinions and trends, making it an invaluable tool for real-time market analysis and competitive intelligence.

How is the Data Generally Sourced?

The data is sourced directly from Google Places, one of the most widely used platforms for business reviews and location-based feedback globally. Our robust web scraping infrastructure is specifically designed to extract every relevant piece of information from Google Places efficiently and accurately. We employ advanced scraping techniques that allow us to capture a wide array of review data across multiple industries and geographic locations.

The scraping process is conducted at regular intervals to ensure that our dataset remains up-to-date with the latest consumer feedback. Each entry undergoes rigorous data validation and cleaning processes to remove duplicates, correct inconsistencies, and enhance data accuracy. This ensures that users receive high-quality, reliable data that can be trusted for critical decision-making.

Primary Use-Cases and Verticals

This Google Places Review Data is a versatile resource with a wide range of applications across various verticals:

Consumer Insights and Market Research: Companies can leverage this data to gain a deeper understanding of consumer opinions and preferences. By analyzing ratings, comments, and sentiment across different locations and industries, businesses can identify emerging trends, discover potential areas for improvement, and better align their products or services with customer needs.

Brand Reputation Management: Organizations can use this data to monitor their brand reputation across multiple locations. The dataset enables users to track customer sentiment over time, identify patterns in feedback, and respond proactively to negative reviews. This helps businesses maintain a positive brand image and enhance customer loyalty.

Competitive Analysis: By analyzing reviews and ratings of competitors, companies can gain valuable insights into their strengths and weaknesses. This data can inform strategic decisions, such as product development, marketing campaigns, and customer engagement strategies.

Location-Based Marketing: Marketers can utilize this data to tailor their campaigns based on regional customer preferences and sentiments. The geolocation aspect of the data allows for precise targeting, ensuring that marketing efforts resonate with local audiences.

Product and Service Improvement: Businesses can use the detailed feedback from Google Places reviews to identify specific areas where their products or services may be falling short. This information can be used to drive improvements and innovations, ultimately enhancing customer satisfaction and business performance.

Real-Time Sentiment Analysis: The continuous update of our dataset makes it ideal for real-time sentiment analysis. Companies can track how customer sentiment evolves in response to new products, services, or market events, allowing them to react quickly and adapt to changing market conditions.

How Does This Data Product Fit into Our Broader Data Offering?

Autoscraping’s Google Places Review Data is a vital component of our comprehensive data offering, which spans various industries and geographies. This dataset complements our broader portfolio of consumer feedback data, which includes reviews from other major platforms, social media sentiment data, and customer satisfaction surveys.

By integrating this Google Places data with other datasets in our portfolio, users can develop a more holistic view of consumer behavior and market dynamics. For example, combining review data with sales data or demographic information can provide deeper insights into how different factors influence customer satisfaction and purchasing decisions.

Our commitment to delivering high-...