Product review data, as the name suggests, consists mainly of reviews of company products. More than a collection of star ratings, this data tracks how people use and interact with commercial and business products in their daily lives.
Customers use online review websites, social media, and price comparison websites to review products. However, companies also send out feedback requests to their customers. They may also partner with survey data companies to get honest feedback directly from existing and potential customers.
Most product reviews use a star or grade rating which are easy to quantify. However, individuals write reviews in their own words, which can be difficult for machine learning programs to extract actionable information.
Additionally, reviewers rate certain information more important than others, and may focus on some factor outside of your control, like the time it took for a shipped product to arrive to their home. that you are particularly focused on.
Companies use product review data to do many things. They conduct market analysis, competitor analysis, product development, and more.
Product reviews are especially important for marketing teams as the presence of online reviews can increase conversion dramatically—sometimes hundredfold.
Finally, review sites provide a space for you to publicly show your customer service and sales skills when you sign up and respond to customers.
Test the quality of product review data first by checking that your data set is complete and frequently updated. You can also employ web scraping tools to find mentions of your company online but any reasonably active review or e-commerce site will likely have useful reviews, especially as there are increasingly strict consequences for fake reviews.
The quality of your NLP program may be more of an issue. However, a good program that can grade user sentiment down to the sentence fragment and recognize neutral sentiments is not too difficult to set up or maintain. You can test the program at any time by writing a considered review yourself, with a mix of positive and negative critiques.
One of the most important aspects of this data category is the authenticity of the review and reviewer. Even negative reviews are useful to potential buyers as long as they are authentic as what may be negative for one person may be unimportant to another.
Based on data from the high-end gift retailer, we found that as products begin displaying reviews, conversion rates escalate rapidly. The purchase likelihood for a product with five reviews is 270% greater than the purchase likelihood of a product with no reviews.
Flixster Database provides information on movies around the world. This includes plot, filmmaker, crew, current availability, and, of course, reviews.
Flixster even tracks news about movies, and the entertainment industry in general.
The Logz Data Platform enables any data engineer to manage any project, no matter the subject or scale. Their data management tools include cloud security, compliance, log tracing, and even developer analytics and visualizations.
Quandoo Restaurant Database tracks reviews, cuisine type, and location data on more than 17,000 restaurants all over the world