Early risk detection
Klarion flags rising volume, recurring friction, and worsening frustration before they threaten renewals or expansions.
Klarion tracks every account’s support health so CSMs can protect renewals, unlock expansions, and forecast with clarity.
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Klarion translates support conversations into reliable, account-level signals.
Klarion flags rising volume, recurring friction, and worsening frustration before they threaten renewals or expansions.
Klarion detects high frustration in support conversations so CS can step in fast and protect the relationship.
Early warnings when support health shifts. Klarion flags rising volume, recurring friction, & worsening frustration before they threaten renewals or expansions.
Account-level support health signals that inform your risk assessment and forecast conversations, without manual ticket review.
Make support-health visibility a standard motion across every account.
Why it matters: Issues compound and become renewal or expansion risk.
Klarion delivers: Early warnings when support health shifts, plus what’s driving it, so you can engage early.
Why it matters: Customers expect you to own their issues and drive progress.
Klarion delivers: A QBR-ready support summary: recent issues, key drivers, trend movement, and what improved, grounded in support conversations.
Why it matters: A single high-frustration thread can erode trust.
Klarion delivers: High-frustration alerts with context, so CS can step in quickly, align internally, and protect the relationship while fixes are underway.
Why it matters: Leadership expects CS to identify risk early.
Klarion delivers: Account-level support health trends you can factor into forecast reviews, without manual ticket review.
Ticket metadata reporting often misses important signals in what customers actually say. In one support thread, a customer can report the issue, describe product friction, request improvements, and express frustration. Klarion captures those distinct signals from the full conversation, so account support health reflects what customers experienced, not just how tickets were labeled.
See how CS teams scale account-level risk visibility.
Get a DemoTicket-level reporting flattens each conversation into one label, which hides the real drivers behind volume, pain, and escalations. Klarion breaks long threads into distinct issues so support leaders can see what is actually creating workload and customer frustration.
Native reporting is useful for operational views, but it usually relies on surface-level tags and single-ticket summaries. Klarion adds conversation-level extraction, issue normalization, and cleaner historical analysis so reporting stays trustworthy as complexity grows.
Klarion reads the full conversation and separates out each distinct issue, request, or complaint instead of forcing the thread into a single bucket. That means long conversations and blended tickets still contribute accurate signal to your reporting.
Klarion identifies tickets that are administrative, transactional, or otherwise not meaningful feedback signal. Removing that noise gives you cleaner trend lines and prevents false spikes from distorting support and product priorities.
Klarion reprocesses historical conversations using the same extraction logic and issue taxonomy, giving you a consistent baseline across time periods. That makes trends more comparable and much more dependable for planning and executive reporting.
Klarion watches for meaningful increases in issue frequency, frustration intensity, and account-level risk across incoming support activity. Alerts are triggered when those shifts cross thresholds that suggest escalating customer pain or growing operational risk.
Klarion scores frustration from the language and context inside conversations, then rolls that up into usable support metrics. High Frustration rates help teams separate routine volume from the issues most likely to damage CSAT, renewals, or escalation load.
Yes. Klarion lets you slice the signal by account, cohort, plan, region, product area, and other business-relevant groupings so you can understand who is affected and where the biggest support or revenue risk is building.