Product review data is the collection of product reviews and the data about those reviews.
Customers use online review websites, social media, and price comparison websites to review products.
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 ofonline reviews “can increase conversion by 270%.”
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.
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