How Atlassian Built a Scalable Data Pipeline to Close the Loop with Customers

Atlassian has never been short on feedback. Every day, users share thoughts across Jira, Confluence, Trello, support chats, Reddit, and more. But while feedback came in from all sides, making sense of it, at scale, was something else entirely.

Product teams were drowning but struggling to extract insights. They had access to plenty of customer comments but no reliable way to translate them into patterns, themes, or clear next steps.

This article shares how Atlassian reworked its approach, moving from scattered listening to a structured feedback system that supports product discovery, faster resolution, and a better experience for everyone involved.

Identifying the Core Business Problem

Much of Atlassian’s feedback came in through support, community posts, and in-app messages. But there wasn’t a clear process for organizing or understanding it. Feedback was either too raw, too repetitive, or simply too hard to route to the right teams.

At the same time, customers expected more. Developer communities, in particular, weren’t just offering suggestions—they wanted to know their feedback was being heard. The research team noticed this tension when users started declining to participate in interviews. Their reason? They’d already given feedback, and nothing had changed.

Internally, teams were eager for insight. But most product managers were overwhelmed. There were too many inputs from too many channels, and no structured way to prioritize what mattered most.

Atlassian had to make feedback usable at scale.

Tackling the Problem as an Engineering Challenge

To solve this, Atlassian treated feedback as a data engineering challenge. It wasn’t enough to “listen better.” They needed a system: something to collect, clean, analyze, and distribute insight automatically.

After more than a year of evaluating tools, Atlassian chose Thematic, a best-in-class white-box NLP platform. It gave them:

• Data consistency: No matter the channel, feedback was structured, deduplicated, and aligned to the same taxonomy.
• Machine learning at scale: Themes and sentiment were auto-tagged with accuracy and coverage better than manual teams.

Thematic’s white-box model meant Atlassian could refine the system as it learned—there was no black-box guessing. Their analysts could validate patterns, refine categories, and continuously improve.

See the infographic below for a breakdown of Atlassian’s Feedback Intelligence Stack.

Automating Insights for Immediate Impact

With structured feedback flowing into a central Tableau dashboard (a tool for visualizing and exploring structured data), the whole company could explore what customers were saying, from product managers to executives. Teams could filter feedback by product, timeframe, sentiment, or volume, making it easier to spot trends and identify pain points.

This changed how Atlassian acted on feedback. Instead of gathering dust, feedback now triggers targeted actions. When users mentioned “dark mode,” an email campaign launched to let them know it had shipped. Open rates hit 26%—more than double the average. Thematic’s workflow automation helped route key themes into action channels quickly, whether that meant triggering messaging or flagging priorities for PMs.

Measurable Results and Cultural Shift

This wasn’t just a workflow improvement. It marked a broader cultural change inside Atlassian. Teams began thinking differently about the value of feedback, not just as something to respond to, but as a continuous signal to act on.

To measure this shift, Atlassian introduced a new internal metric: heardness—an indicator of whether users felt their feedback was acknowledged and addressed. This gave teams a tangible way to track something that had previously been difficult to quantify.

And it worked. Heardness showed a strong correlation with CSAT, reinforcing that when users felt heard, their satisfaction improved. Issue resolution times dropped by 50%. Teams began linking recurring feedback themes to product outcomes like feature adoption, support load, and even churn.

Feedback was no longer just a support artifact—it became a lens into customer experience, product health, and business performance.

Strategic Impact and Future Steps

Today, Atlassian’s feedback system does more than close the loop—it guides roadmaps, shapes UX, and tells the customer story at scale. From investor relations to engineering standups, feedback now fuels decisions.

Next up: integrating more feedback channels into Thematic, and measuring how product changes shift sentiment over time. Collecting feedback is just one part of the goal—the real aim is to get better at learning from what they already receive.

Atlassian's journey shows what happens when you treat feedback as a strategic asset. By making it structured, actionable, and visible, they turned a sea of noise into a continuous discovery loop that scales.