Table of Contents
What is business review and rating data?
Business review and rating data includes reviews and ratings about companies and the quality of their products or services. It may also include salary information and reviews from employees rating the companies as employers.
This data provides feedback from customers and employees, offering insights into customer sentiment. If coming from employees, it provides employee sentiment towards the company culture. This data is used to improve products, services, working conditions, and brand perception.
Where does business review and rating data come from?
Most business reviews are collected from online review websites, such as Yelp. Several online resources include a star rating in addition to the reviews, offering an immediate public perception of the company. The reviews are typically anonymous and may provide generic demographic context.
Online or in-person surveys, customer feedback channels, testimonials on a company’s website, or social media pages also provide reviews and ratings for businesses.
What types of attributes should I expect when working with business review and rating data?
This data has only two key attributes – reviews and ratings. In reviews, customers or employees share their experiences about different aspects of companies, their products, and services. Ratings are numerical, usually one to five stars. They can be converted into fraction data points to make them machine-readable.
It is possible to quantify the reviews as discrete data points for powering analytics. However, not all reviews can be quantified generically. For example, some employee reviews focus on training opportunities while others focus on team events. Some customer reviews describe delivery issues, some praise the product finish, and some complain about the cost. Natural Language Processing (NLP) can extract essential points from textual reviews and generate actionable insights.
How should I test the quality of business review and rating data?
A good NLP tool extracts user sentiment from business reviews and rating data. You can test a small sample of the data from your vendor and assess the results of NLP. This approach can give you a good assessment of the data matching your requirement.
To test the quality of the business reviews and rating data:
- Ensure that the datasets are complete and frequently updated.
- Make sure that the vendor is reliable and performs stringent checks for its sources. Fake reviews are more common, which can skew the results of your analysis.
Who uses business review and rating data?
Businesses use this data to analyze their brand perception and compare it with competitors. They also use reviews to understand market sentiment, monitor brand engagement, and improve brand performance. Business reviews also contribute towards identifying and targeting audiences.
Feedback about products and services can help companies introduce new models or pricing plans.
In case of crises, this data can help understand the business impact and minimize it.
Investors and lenders use the data to make decisions about which companies to invest in. Financial institutions also leverage this data for assessing brand perception in the market and predicting stock prices.
Finally, potential employees use this data to compare companies and to decide which companies to apply at.
What are the common challenges when buying review and rating data?
Companies use business reviews to uncover insights on how customers and employees rate them. The biggest challenge, in this case, is the authenticity of reviews and reviewers.
- Review Authenticity: Fake reviews can present wrong impressions about companies. As companies use the reviews to analyze their public perception, they need to ensure the authenticity of the data. For vendor-sourced reviews and rating data, you need to validate the credibility of the source.
- Range of ratings: Negative reviews can provide opportunities to improve products and services. For accurate insights into the company’s reputation, you should also include negative reviews and low ratings.
- Compliance: Business reviews are often anonymous, but they may include personally identifiable information (PII), such as the IP address of the reviewer. In such cases, data must comply with the region-specific privacy regulations.
What are similar data types to business review and rating data?
Business reviews and rating data is similar to consumer review data, product data, brand data, and web and social presence data.
You can find a variety of examples of B2B and company data in the Explorium Data Gallery
What are the most common use cases of business reviews and rating data?
The most common use cases for business review and rating data are customer sentiment analysis and assessment of investment opportunities. This data can also be used to enrich customer data to create more complete customer profiles. Business reviews and ratings can also enrich other B2B data for a wide variety of use cases.
- Customer sentiment analysis: The process of discovering emotions in reviews and ratings helps companies understand their brand reputation and how customers perceive their products and services. These insights into customer sentiment help improve offerings and deliver better customer experiences. Companies also leverage sentiment analysis to optimize marketing strategies and advertising campaigns.
- Assessment of investment opportunities: Investors use business reviews and ratings to assess the brand value of the company. They also use this data to compare similar businesses and identify potential investment opportunities.
- Supplier Risk: Business review and rating data can augment other B2B data to assess potential suppliers and partners. Possible limitations of services and products can be deduced from this data, which helps make an informed decision about candidates.
Which industries commonly use this type of data?
All types of industries use business reviews and ratings for customer feedback. Consumer goods or CPG, retail, and eCommerce use this data constantly to learn about their customers’ satisfaction and improve customer experiences. Manufacturing, hi-tech, insurance, banks, and financial services also derive insights from this data.