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.

Explore our online review survey for real experiences. Discover online review trends and statistics to make informed decisions and enhance consumer journey.

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 **** in *** 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.

In a November 2021 survey conducted in the United States, 85 percent of respondents stated that the overall average star rating of a business was one of the most important considerations when judging a local business based on reviews. The business having a higher than average star rating than other businesses was also a very important factor, with 76 percent of respondents stating that this was one of the most important aspects.

The statistic presents information on the most common types of services that U.S. consumers read online reviews about before selecting a new service provider. During the April 2017 survey, 54 percent of responding online review users read online doctor or dentist reviews before visiting.

Online Survey Market size was valued at USD 7,687.43 Million in 2024 and is projected to reach USD 16,903.37 Million by 2032, growing at a CAGR of 11.91% from 2026 to 2032.

Global Online Survey Market Overview

The global online survey market is undergoing a major transformation driven by advancements in data security, AI, and real-time analytics. With increasing concerns over privacy, companies are adopting advanced security protocols to protect respondent data. This includes encryption, multi-factor authentication, secure storage, and real-time monitoring. Additionally, there is a clear shift towards real-time data collection and analysis, as businesses now require instant insights to remain competitive.

Online Survey Software Market size was valued at USD 1.35 Billion in 2024 and is projected to reach USD 4.05 Billion by 2032, growing at a CAGR of 11.91% from 2026 to 2032.Global Online Survey Software Market DriversThe Global Online Survey Software Market is experiencing accelerated growth, fueled by the mandatory digital shift across all industries and the increasing value placed on quantifiable human insights. As a senior research analyst at VMR, I provide a detailed, SEO-optimized analysis of the key forces driving this market.Growing Demand for Data-Driven Decision-Making: The shift toward data-driven decision-making is the paramount strategic driver for the online survey software market. Organizations across every sector from finance and retail to healthcare and education are moving away from reliance on intuition toward actionable, measurable insights derived from customer and employee feedback. Survey platforms provide the most direct, scalable, and cost-effective method for quantifying subjective sentiment, allowing executives to guide strategy, optimize marketing spend, refine product features, and manage service delivery based on hard evidence. This growing corporate appetite for real-time, validated feedback ensures that online survey tools remain an indispensable component of the enterprise data ecosystem, driving adoption across major corporate segments globally.Expansion of Digital Transformation and Remote Work Models: The sustained, large-scale digital transformation of businesses and the pervasive adoption of remote/hybrid work models have permanently cemented the role of online survey software. As physical touchpoints diminish and workflows move to the cloud, organizations require digital tools to maintain contact and gather feedback from distributed teams and online customers.

Discover the booming user feedback software market! This comprehensive analysis reveals key trends, growth drivers, and market segmentation (cloud-based, web-based, enterprise, SME), highlighting leading players like Trustpilot and Bazaarvoice. Explore regional insights and forecast data (2025-2033) for informed business decisions.

This document contains the raw data from an anonymous, cross-sectional, global online survey that aimed to identify the experiences and operation of research ethics review committees (ERCs) during the COVID-19 pandemic. Respondents were chairs (or their delegates) of ERCs who were involved in the review of COVID-19-related research protocols after March 2020. The 203 participants [130 from high-income countries (HICs) and 73 from low- and middle-income countries (LMICs)] came from diverse entities and organizations from 48 countries (19 HICs and 29 LMICs) in all World Health Organization regions.The survey questionnaire, administered through the Qualtrics Experience Management (XM) online platform, consisted of 50 items, with opportunities for open text responses. This document includes two Excel spreadsheets with the original data from Qualtrics, one for participants from HICs and the other for participants from LMICs.The study received approval from Western University’s Non-Medical Research Ethics Board (Protocol ID 120455). Additionally, it was evaluated by the World Health Organization Research Ethics Review Committee (Protocol ID CERC.0181) and was exempted from further review.

Business Problem Background

We have a client who has a website where people write different reviews for technical products. Now they are adding a new feature to their website i.e. The reviewer will have to add stars(rating) as well with the review. The rating is out 5 stars and it only has 5 options available 1 star, 2 stars, 3 stars, 4 stars, 5 stars. Now they want to predict ratings for the reviews which were written in the past and they don’t have a rating. So, we have to build an application which can predict the rating by seeing the review.

• A recent survey (Hinckley, 2015) revealed that 67.7% of consumers are effectively influenced by online reviews when making their purchase decisions. More precisely, 54.7% recognized that these reviews were either fairly, very or absolutely important in their purchase decision making. Relying on online reviews has thus become a second nature for consumers.

• **Consumers want to find useful information as quickly as possible. However, searching and comparing text reviews can be frustrating for users as they feel submerged with information. **

• The overall star ratings of the product reviews may not capture the exact polarity of the sentiments. This makes rating prediction a hard problem as customers may assign different ratings for a particular review. In addition, reviews may contain anecdotal information, which do not provide any helpful information and complicates the predictive task.

• **The question that arises is how to successfully predict a user’s numerical rating from its review text content. **

Dataset Information

Data is collected from Amazon.in and flipkart.com using selenium and saved in CSV file. Around 50000 Reviews are collected for this project.

• We have scrape around 50000 reviews of different products along with ratings from amazon website. Basically, we have these columns in dataset. 1) reviews of the product. 2) rating of the product.

• You can fetch other data as well, if you think data can be useful or can help in the project. It completely depends on your imagination or assumption.

Model Building Phase

• **You need to build a machine learning model. **

• This is multi-classification problem and Rating is our target feature class to be predicated in this project. There are five different categories in feature target i.e., The rating is out 5 stars and it only has 5 options available 1 star, 2 stars, 3 stars, 4 stars, 5 stars.

• Try different models with different hyper parameters and select the best model.

Enterprise Survey Software Market size was valued at USD 9.95 Billion in 2023 and is estimated to reach USD 15.95Billion by 2031, growing at a CAGR of 7.2% from 2024 to 2031.

Global Enterprise Survey Software Market Drivers

The market drivers for the Enterprise Survey Software Market can be influenced by various factors. These may include:

Focus on Data-Driven Decision Making: Businesses are depending more and more on data-driven decision making to improve their business strategies and run their operations more efficiently. Through data collection, analysis, and reporting, enterprise survey software gives companies useful information that helps them make smart choices based on feedback from workers, customers, and other important people. Strong survey solutions are in high demand because of the focus on using data for strategic benefit.

Projects to Improve the Customer and Employee Experience: Businesses are putting more money into projects to make customers and employees happier. Enterprise survey software is needed to do performance reviews, customer feedback surveys, and polls about how happy your employees are with their jobs. As businesses try to make the experiences of both their employees and customers better, the need for all-in-one survey tools that can collect and properly analyze feedback grows.

Global Enterprise Survey Software Market Restraints

Several factors can act as restraints or challenges for the Enterprise Survey Software Market. These may include:

High Implementation Costs: Setting up and maintaining business survey software can cost a lot of money. This includes not only the price of buying or subscribing to the software, but also the costs of integrating it, making changes to it, and teaching people on how to use it. Small and medium-sized businesses (SMEs) may not invest in these kinds of solutions because they are too expensive, which limits the growth of the market.

Integration Difficulty: It can be hard and take a lot of time to connect survey software to current business systems like CRM, HR, or ERP. Organizations may have problems with system interoperability, data transfer, and compatibility, which can make it harder for survey software to be used effectively and widely.

Title: “Survey on Online Shopping Preferences”

Dataset Description: This dataset contains responses from an online survey conducted to understand consumer preferences and behavior when shopping online across various product categories. The survey covered topics such as the frequency of online shopping, age demographics, platform preferences for electronics, fashion, beauty products, groceries, and household essentials, as well as factors influencing platform choice, trust in product reviews, and the perceived quality of return and refund policies. The responses were collected from a diverse group of individuals, making this dataset suitable for analyzing trends in e-commerce behavior. Context: The dataset was compiled to gain insights into the changing dynamics of online shopping, especially in an era where digital platforms dominate retail. Understanding consumer preferences can help businesses tailor their offerings to better meet customer needs.

Content: The dataset consists of the following columns:  Timestamp: The date and time the response was submitted.  How often do you shop online?: Frequency of online shopping (e.g., weekly, monthly).  What is your age group?: Age range of the respondent.  Which platform do you prefer for buying electronics?: Respondent’s preferred platform for purchasing electronics.  Where do you usually shop for fashion and apparel?: Preferred platform for fashion items.  Which platform do you prefer for beauty and skincare products?: Platform choice for beauty and skincare products.  Where do you typically buy groceries and household essentials?: The go-to platform for groceries and essentials.  What is the most important factor for you when choosing a platform to shop from?: Key consideration when selecting a shopping platform (e.g., price, product quality, platform security).  Do you trust the product reviews and ratings on these platforms?: Level of trust in reviews and ratings (e.g., fully trust, somewhat trust, neutral).  Which platform’s return and refund policy do you find the best?: Respondent’s opinion on the platform with the best return and refund policy.

Use Cases: This dataset can be used for a variety of research and analytical purposes, including:  Studying consumer behavior trends in online shopping.  Analyzing platform preferences across different age groups.  Identifying key factors influencing online shopping decisions.  Exploring trust in product reviews and return policies on popular e-commerce platforms.

Acknowledgments: This dataset was compiled through a survey done using google form. Special thanks to all the respondents for their time and valuable insights.

A 2021 survey revealed that nearly one-third (32 percent) of consumers worldwide used online reviews and ratings regularly to help make purchase decisions. Indonesian, Indian, and Singaporean consumers were most likely to use online reviews and ratings to help with their purchase decisions, each with 43 percent of respondents reporting so.

List of Top Institutions of Survey Review sorted by citations.

This data set contains:

• Full data files of a 2016 survey of attitudes to Open Peer Review in xls and csv formats
• Survey questions (pdf)
• Readme file (txt)

Between 8 September and 7 October 2016, OpenAIRE held a survey designed to aid the development of appropriate OPR approaches by providing evidence about the attitudes of authors, editors and reviewers towards OPR, their reservations and needs, as well as to gauging current levels of experience and reservation with different types of OPR. A supplementary aim was to collect feedback on a provisional definition of OPR as created during another strand of work. The survey aimed to aid the development of appropriate OPR approaches by providing evidence about the attitudes of authors, editors and reviewers towards OPR, their reservations and needs, as well as to gauge current levels of experience and reservations with different types of OPR. The survey was conducted via an openly accessible online questionnaire (using the scientific survey platform SoSci, www.soscisurvey.de). It received a total of 3062 complete responses (a further 635 responses were discarded as incomplete). The survey was open to all wishing to take part and distributed via social media, scholarly communications mailing lists, publisher newsletters and, in one case, a publisher internal mailing list (Copernicus Publications).

Acknowledgement: This work is funded by the European Commission H2020 project OpenAIRE2020 (Grant agreement: 643410, Call: H2020-EINFRA-2014-1)

Contact: Dr Tony Ross-Hellauer, University of Göttingen, State and University Library, ross-hellauer@sub.uni-goettingen.de

The graph shows the changes in the impact factor of ^ and its corresponding percentile for the sake of comparison with the entire literature. Impact Factor is the most common scientometric index, which is defined by the number of citations of papers in two preceding years divided by the number of papers published in those years.

Discover the booming Company Review & Rating Solutions market! This comprehensive analysis reveals a $2.5B (2025 est.) market growing at a 15% CAGR, driven by employer branding and candidate insights. Learn about key players, regional trends, and future projections until 2033.

The dataset captures customer satisfaction scores for a one-month period at an e-commerce platform called Shopzilla (a pseudonym). It includes various features such as category and sub-category of interaction, customer remarks, survey response date, category, item price, agent details (name, supervisor, manager), and CSAT score etc.
Note: Please be advised that the authentic information has been obfuscated, and the dataset has been fabricated using the Faker library to ensure the concealment of genuine details
Dataset Information:
Rows: 85,907
Columns: 20
Usage:
This dataset serves as a valuable resource for conducting Exploratory data analysis (EDA), Visualization, and Machine Learning Classification tasks pertaining to customer service performance evaluation, satisfaction forecasting, and customer behavior analysis within the e-commerce sector.

Do explore pinned 📌 notebook under code section for quick EDA📊 reference
Consider an upvote ^ if you find the dataset useful

Data Description:

Survey data in SPSS and Excel for the Trust and Peer Review survey. This was a global online survey of 3133 active researchers.