As a business, customer feedback, in the form of reviews, survey responses and social mentions can help you understand what they love about you and what they don't. Though reading complete reviews provide a great context on the issues raised by the customers, going through hundreds of reviews to get actionable insights can be very difficult. Birdeye's 'Natural Language Processing' algorithm (NLP) provides a great solution to the problem. 

Customer feedback is run through Birdeye's robust NLP engine where the constituting keywords are first given a sentiment score to indicate whether they are positive, negative or neutral. These keywords are then made available in a keyword cloud for running deep analytics. The keywords in themselves can provide some insight into the issues that the company's customers have highlighted, but again are unable to provide actionable items that the executives can work on. This is why Birdeye allows you to create buckets of related keywords called 'Categories'. Each keyword can be custom added/or removed from a defined category.

Usually, the 'Categories' created by a business are in line with their defined internal operational metrics that a company uses to measure itself. For eg., a Pizza company may create the following categories based on its internal operational metrics - 'Quality of food', 'Staff', 'Home Delivery', etc.

Once these 'Categories' are defined within the Birdeye dashboard, the business can use these to compare one location Vs another. For eg., Customers are seeing issues with 'Facility' at 'Location A' and with 'Staff' at 'Location B'.