Let’s be honest: Nobody likes to wait. This is especially true for dissatisfied customers. In order to prevent customers in a negative mood from migrating somewhere else, time plays a decisive role in customer service. Customers in a positive mood can usually wait longer for an answer without developing negative associations with your company, your products or your services.
But how do you filter those customers, who should receive an answer as quickly as possible due to their negative mood, out of the many inquiries?
The solution is not as complicated as you might think! The answer is Sentiment-Analysis through ‘Natural Language Processing‘ (NLP) in Zendesk. But how exactly can the content of Zendesk tickets be processed via NLP in order to perform actions on tickets?
Example of ticket-mood detection
We will parse the following comment of a Zendesk ticket through NLP (natural language processing):
Every time I order in your shop I have following problems. I can’t understand why my experience is so awful!
The parcel is delivered too late – it is damaged every second time and the content seems to be used by some other person already.
When I try to return it you tell me It’s not possible!??
Which information can be retrieved using NLP in Zendesk tickets?
- Sentiment analysis – retrieves the mood of a text.
It analyses the text and counts the number of sentiments like positive, negative, neutral.
- The above example has negative mood
- The mood can be stored inside a custom ticket field called “mood”
When a new tickets gets created the content will be processed through the Leafworks Zendesk AddOn Api Middleware.
The mood will be stored in a custom ticket field called “ticket-mood“.
You can then make use of Zendesk triggers to prioritize incoming tickets based on the mood of the customers. Happy customers will be fine when receiving a response later – un-happy customers could get the response faster!