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Maximizing Zendesk Data Analysis With Tags

September 11th, 2014

Maximizing Zendesk Data Analysis With Tags

How do you get the data you need on how your customers engage with your product so that you can make your whole team more successful? Where are customers struggling the most in your product? How can you help them have a better experience overall? Your customer support data provides invaluable insights into questions like this. That’s why DataHero connects directly to Zendesk. It pulls in your support data quickly and then creates suggested charts based on that data. One of the most common ways people track their Zendesk tickets is by using tags. It’s a great system to allow flexibility but it can present some problems later if you want to analyze these tags. You can also now analyze your data based on different tag types with DataHero.

By combining the power of Zendesk and DataHero, you can customize your analytics to really dig into the answers you need with easy filtering, sorting, and advanced charting.

Once you import your Zendesk data into DataHero, you’ll see that DataHero automatically finds and classifies your tags as a Comma Separated List, allowing you to chart these tags out later. Creating charts from these labels can be very time consuming in Excel, or simply not possible at all with some services.

Notice in the screenshot above that the icon represents the Tags attribute being categorized as a comma separated list.

Visualize Tags over Time

To see what questions customers are asking about your site, simply drag on Ticket Created Date and Tag.

In the chart above you can clearly see the cycle of tickets from month to month. Of course, each item may fall into multiple categories. For example, it may be tagged with a single tag or multiple tags and therefore count in multiple places on the visualization.

The tags above may correspond with site traffic or purchases as well. There will be more on combining support data with datasets from other services later in the post. The point is that watching these ticket cycles allows you to ask further questions of customers’ behaviors. For example, why are there more product feedback tickets in December of 2013, then a sharp drop in January of 2014? Questions like this can lead to big insights, like that perhaps once a certain product was discontinued, negative feedback decreased sharply.

Filter to View Tags By Any Variable

Filtering doesn’t have to end with tags, either. Breaking your support data into multiple “slices” can lead to even more insights. For example, which team members handle product tickets most frequently? Product tickets would include any tickets with the label “product feedback, product question, product request and product suggestions”.

The pie chart above provides a good snapshot on who takes the most product tickets. It looks like one person takes the majority of these, this person might be the product specialist, or this may be an inefficient use of support staff resources. This visualization allows us to explore these questions further. You can even look at the breakdown by date, in this case day of the week:

This will look at how the team shares the product-specific tags by day of the week.

Combine Zendesk Data with Other Services

Your support data gives you insight into your customers, and merging that with your revenue or sales data can be a powerful combination. By combining these two datasets we can answer questions like, “what kinds of questions are most common among our highest-paying customers?”  You can event combine your customer support data with data in spreadsheets from your computer or cloud storage services (like Dropbox).

Drag Plan onto the filter portion of the chart canvas and select the plan  you’d like to visualize. Then simply drag on ticket and created date to get a resulting chart like this:

The chart above shows only refunds and cancellations (using DataHero’s filters for tags). It also displays how these cancellations and refunds are broken down by payment plan by date and percentage. The Silver plan accounts for the highest percentage of cancellations and refunds for most quarters, so this may be an issue worth further exploration.

You’ve now seen how easy it is to dig deeper into your support data, and how you can even combine this support data with other datasets to answer questions across multiple channels. DataHero gives you the tools you need to answer your high level data analysis questions. Give it a try for free today.

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By Kelli Simpson

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