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Sentiment Analysis for Zendesk

A top productivity tool for improving customer experience.

Automatically extract customer sentiment from Zendesk tickets

Lower Ticket Volume

Knowing how the client feels will help you route the ticket to the appropriate team the first time, ensuring that agents are working on requests that are essential to the business.

Actionable Insights

Sentiment scores for each customer interaction quantify the sentiment expressed. These scores help track sentiment trends over time and identify areas for improvement.

Better Productivity

Sentiment Analysis empowers agents to gain valuable insights into customer emotions, satisfaction levels, and overall experience, enabling them to make the best use of their time.

Accessible Reports

Intuitive reports make it easy for businesses to understand and communicate sentiment insights, as well as assess the impact of initiatives aimed at improving customer experience.

Happy Customers

When you can solve your customer's concerns by gaining insight into their emotions and satisfaction levels, you can provide them with a wonderful brand experience.

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Prioritize customer feedback

Customer feedback is a valuable asset for businesses looking to improve their customer experience.

But without the right tools, it can be time-consuming and difficult to understand the emotions and opinions behind the feedback. This is where sentiment analysis comes in.

Prior to an agent opening the ticket, automated sentiment analysis occurs, determining the urgency of a customer’s issue and automatically routing them according to their priority. Words like “mad,” “frustrated,” and “not good enough,” to mention a few, are often used by dissatisfied customers to express their feelings.

Such a client needs top-tier support to transform their “not good enough” experience into one that is satisfactory enough that they will hopefully remain a client and even promote the brand as a result of their interactions with the company.

No more doubling-handling

Being able to prioritize an irate customer to be helped through the use of sentiment analysis is priceless for any business.

Tickets can be routed according to the details of the issue and the priority, with only those agents handling it that were auto-assigned.

By circumventing the standard customer service channel, agents no longer need to browse through tickets and check for the customer’s disposition and retrieve additional data before assigning or escalating the case.

Instead, tickets can be routed based on the nature of the issue and the priority, with only the agents best suited for the task handling it.

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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:
  1. 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.

  2. 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.

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