Hi there. In this post I talk about anonymous reviews and its impact on statistics and data analytics.
Reviews are one way for customers to give their feedback towards a company that offers goods or services. These reviews also give information to existing customers and prospective customers. Customers who are unsure if they want to try a store, business or restaurant may use reviews to get an idea of what the place is like before visiting. Low reviews may discourage people from trying a place while high reviews give the business credibility.
In a previous post on Voluntary Samples, I did have a piece on Google Reviews. Reviews in general are not always perfect. One person may have a good experience at a place while another person may not have a good experience at the same place. Service may not be consistent at a certain place. Some reviewers may be paid to give good or bad reviews. Users may or may not be actual customers. In summary, the provided reviews score are not always perfect nor honest. In my opinion, reviews give an idea of what the place is like.
When reviews require users to use their real names, there is likely to be less reviews to be published. In statistics language, a smaller amount of reviews is one example of a smaller sample size. There is less information available as a result of less reviews. Prospective customers will have less information in deciding to go with a certain business.
Users with their real names who post dishonest and bad reviews are likely to have the business respond and defend themselves. Honest and bad reviews from actual users could also have the company come after them. Some awful companies do not like being exposed. It is safer for users to be anonymous. An anonymous good review to the business protects the users identity while giving the business good credibility. Anonymous users who give honest bad reviews avoid being potentially targeted and putting their life at risk.
Thank you for reading.
