Support ticket analytics that finds the cause behind the volume.

Support ticket analytics is the practice of measuring what your support tickets are about, grouped by cause and tracked over time, so the numbers describe customer problems and not queue activity. It treats the text of the ticket as the data, so a billing failure counts as a billing failure whether the agent filed it under Billing, Account or Other.

The distinction matters because most helpdesk categories exist to route work. They were written to get a ticket to the right team quickly, and they are picked in seconds by someone whose job is to resolve the contact. Reading the conversation itself turns a support queue into a record of what customers are struggling with.

Unwrap ranks every contact driver by how many tickets it accounts for, so the largest sources of volume are visible without anyone building a report.

Because the taxonomy is generated from your feedback, drivers appear whether or not a category already existed for them. A driver that didn’t exist last quarter shows up on the next sync after customers start describing it, and the ranking updates as tickets sync. Volume sits beside the verbatims, so a number in a meeting can be opened and checked against what customers actually wrote.

Most volume reporting stops here. Say a chart shows password resets are up 30%. The reason lives in a thousand unread tickets. Unwrap keeps the two together: the theme carries its own evidence, and that evidence is what convinces the team that owns the fix. Rad Power Bikes traced a 27.3% reduction in “Where is my order?” contacts over 3 months to a driver it found this way.

Tickets arrive from Zendesk, Freshdesk, Kustomer, Gladly, Gorgias and Help Scout, chat transcripts from Intercom, and call transcripts from Gong, Zoom, Aircall, Talkdesk and AWS Connect. All of it lands under one taxonomy, which means channel becomes a filter on a single number. More sources are listed on the customer feedback integrations page.

That closes the argument for the next fix. Rad Power Bikes reported a 27% decrease in overall support volume in the first quarter after identifying the issue, and separately used Unwrap’s contact-rate analysis to cut spare-parts contacts by 6%. Follow-up with the customers who raised the issue runs through close the loop, and anomaly detection on new drivers runs through real-time alerts.