The Context
Growth created chaos:
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New agents struggled to find answers quickly
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Product teams lacked feedback loops from the field
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Documentation lagged behind real-world issues
Support data held the answers — but no one could see the patterns.
The same approach applies wherever recurring failures leave patterns in ticket data — software defects, hardware component issues, integration failures.
The Challenge
Leadership wanted clarity on where customers actually struggled most, without another analytics platform or survey.
They needed:
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A data-driven view of customer friction
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Insight that covered both technical and usability issues
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Actionable takeaways that could shorten onboarding and improve docs
Our Approach
Using topic clustering and semantic analysis, Arti surfaced hidden patterns in 15K tickets:
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19% of all support volume: integration failures (specific APIs)
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10%: license provisioning friction (process bottlenecks)
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5%: onboarding confusion and documentation gaps
Each finding was validated with product and support leaders to distinguish:
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Implementation fixes (engineering)
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Clarity fixes (UX, documentation, training)
The Results
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Integration issues prioritized in the next sprint
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Licensing workflows redesigned for simplicity
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Onboarding documentation rewritten using real user confusion data
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Training materials updated, reducing new-agent ramp-up by 40%
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Product roadmap now informed by quantified customer friction
“We thought we were buying a support automation tool. We got a product intelligence engine.”
— VP Engineering
The Impact
By refining unstructured support data, the client created a living feedback loop between Product, Support, and Documentation.
UX clarity improved, onboarding time shortened, and roadmap prioritization became data-backed rather than anecdotal.
