As the number of customers increases, businesses have to allocate more resources to understand their customers. Sometimes even tens of human resources are insufficient to analyze tens of thousands of customer comments. With text analytics solutions powered by artificial intelligence, it becomes possible to perform analyses that would take days in a few minutes.
Businesses that give importance to customer experience offer a feedback channel to their customers at every point of touch and after every transaction. Depending on the number of customers and transactions, the number of feedback collected from customers increases. One of the golden rules of maintaining a successful customer experience program is the processing of every feedback. Each feedback should be classified, labelled and closed after necessary actions are taken.
The power of open-ended responses
Open-ended responses (i.e. comments; unstructured feedback; words) in a customer survey are incredibly valuable. They show how your customers feel. Without comments, customer experience professionals don’t have enough information to understand their customers.
Well-structured open-ended survey questions allow respondents to provide feedback in their own words through qualitative free-form written text fields. These customer survey comments are invaluable but difficult to analyse manually due to their unstructured nature. To quickly and easily analyse your customer survey comments at scale, you will need a large workforce or a highly accurate text analytics solution.
Sentiment Analysis
Sentiment analysis is a general definition given to the processes of defining and classifying the customer’s attitude towards the subject in the comments as positive, negative, neutral and calculating and defining them through various algorithms.
The evaluation you request from your customer with metrics such as NPS, happiness, etc. and your customer’s comment may not always coincide. Your customer may have given your NPS question a score of 9 and made suggestions and complaints to you in the comment field, or they may give you a low NPS score and convey their thoughts to you with generally positive expressions. Therefore, it would be correct to analyse customer scores and comments independently of each other.
Automatic Tagging
It is critical to group the feedback you collect about your products and services in a structure that is completely specific to your business, rather than a general grouping (complaint, suggestion, thanks). Each feedback may concern a different department within your business, and it will be necessary to group feedback with many different breakdowns. The easiest and healthiest method to make this grouping will be labelling. After creating your business’s label pool and grouping the labels, you can start labelling all responses. With automatic labelling powered by artificial intelligence, you can perform these operations quickly.
Meet the Wiseback Text Analytics service
Wiseback AI service, trained with real customer comments from different sectors in Turkish language, analyses the sentiment of customer comments and automatically labels the responses. You can save resources by using the service that works with 92% accuracy rate in Turkish language.
