Improve the Customer Experience with Sentiment Analysis for Zendesk
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- The customer’s mood
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Sentiment analysis for customer satisfaction
Sentiment analysis is a powerful tool for understanding how customers really feel. In order to keep moving the customer satisfaction needle upward, it is imperative for companies to make improvements to customer service on a continual basis.
By running a sentiment analysis for Zendesk, you can classify text to quickly and accurately understand the emotions behind your customers’ feedback or support tickets. The information can be used to gain insights into customer satisfaction, identify common issues, and make data-driven decisions to improve customer experience.
How to Use Sentiment Analysis in Zendesk
Integrating sentiment analysis into your Zendesk account is easy and can be done through NLP. This can be integrated into Zendesk using a middleware solution, which bridges the software and the NLP algorithms. Knots offers a sentiment analysis tool that integrates it into your account. You can start categorizing customer feedback from Zendesk tickets, chat sessions, and social media.
The results from sentiment analysis are displayed directly in the ticket, where you can see the sentiment as a ticket field and also include a tag. You can use this information to automate your flow on Zendesk, and further route the ticket to a specific group of agents, suggest a discount, or offer a voucher for the next purchase.
Which sentiment analysis is best for your Zendesk instance?
There are two ways of performing sentiment analysis on Zendesk data:
- Use a pre-trained machine learning and natural language processing (NLP) model: These models can be fine-tuned on a specific dataset, such as customer feedback or support tickets, to improve their accuracy. A Machine Learning model always takes all words of a given text. So it does not only look for keywords but also for combinations of keywords or even signs (emojis could be relevant as well). Once trained, the model can then be used to automatically categorize customers’ new text into positive, negative, or neutral.
- Use a rule-based approach: This involves defining a set of rules to identify positive, negative, or neutral sentiments in text. For example, a rule might look for certain words or phrases, such as “happy” or “disappointed”, to determine the sentiment of a piece of text. The strict words lookup can be done directly in Zendesk.
With custom triggers, you can set up automated responses based on how customers feel, using the sentiment analysis function in the NLP App. So, when a customer tells you how they feel, you’ll know if it’s good or bad, and you can automatically help them or thank them for their feedback. You can also use custom triggers to respond to certain keywords in customer queries and to start conversations with new customers.
Find out how businesses are using Knots to analyze customer sentiments in Zendesk, gather actionable customer insights, and enhance the customer service they provide. Contact us today for a personalized free trial.