Analyze Zendesk support data easily with Keatext

Knowing how to analyze Zendesk support data is the first step

Implementing a support solution like Zendesk provides a platform for your customers to share their experiences with your product or service. However, the challenge is how to analyze Zendesk support data. Feedback lies in the unstructured text which you either need to read and summarize manually or use text analytics technology to get a comprehensive picture.

Categorizing and quantifying issues

Most organizations I speak with are pulling stats from support interactions based on tags and labels their agents use to categorize tickets. This method is a great starting point to quantifying support inquiries based on the nature of issues.

There are a few downsides to this procedure; adding tags and labels to a support ticket post-interaction can be time consuming – the 30 seconds an agent has to spend on data entry after closing a ticket quickly adds-up. It introduces a bias; say a user is having issues accessing their account due to their trial being expired. Is this a login issue, is it an account issue or is it both? If you’re going to be making impactful decisions based on these insights, the methodology needs to be more systematic. Otherwise, you never get a true count of how many times a specific bug or issue was raised, nor do you get a sense of the impact that problem had on your customer’s experience.

Text analytics tackles all of these challenges. You may still want to keep high-level tags and labels to route support tickets to the right teams or individuals with the pertinent expertise, but can rely on algorithms to handle the categorization automatically. This saves time, leaves no room for subjectivity and ensures more comprehensive reporting since text analytics algorithms will tag all of the issues raised by your customer, not just the one the agent spent the most time on or the customer put the most emphasis on in their communications with your team.

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Slice and dice

Text analytics annotations add another dimension that can be used to slice and dice your data and identify correlations. It is when you combine your structured data with text analytics output that you can quickly make the data actionable. One example would be isolating the top 3 issues that affect your customers with a premium subscription or the largest average spend and prioritizing bug fixes and features enhancements for this group. In the hospitality industry, an important problem may only be affecting certain locations or staff members.

Regardless of what metrics drive you to decide the importance you put on customer wants, needs and complaints; it’s important to know how to get insights from your Zendesk support tickets. The best part is, that information is available to you. Keatext provides a responsive and user friendly web interface specifically designed to help you evaluate the intersection between structured and unstructured data.

Problem discovery

Once you know how to get insights from your Zendesk support tickets using Keatext, you can quickly determine what new issues are bubbling up or what impacted your user experience the most in the last week. Pinpointing issues that caused expression of negative opinion towards your brand or your product is key to preventing churn and mitigating the impact of a given problem by prioritizing a bug fix or simply showing some extra love to a customer who’s really irate.

Make product decisions based on quantifiable data

Product teams struggle to prioritize backlog items and feature enhancements. Behavior tracking is a good source of insights to help shape product decisions. But at the end of the day, products and solutions are built for customers, who are telling you what they want and need through: pre-sales discussions, support tickets, calls with customer service, survey responses and many more.

What’s the ROI?

It is mainly monetizing on the existing investments you’ve made in support, CRM and customer interaction platforms like Zendesk, Salesforce, SurveyGizmo and others. The insights you get from understanding customer likes and dislikes will help you from making costly mistakes in product development and strategic initiatives. Ensuring you’re focusing on market needs and customer wants and expectations drives more top level revenue.

Knowing how to analyze Zendesk support data will enable you to shape effective messaging, in the language used by prospects and clients. It can also help you enhance your documentation and FAQs which will in turn lower your support volumes and costs.

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