We cluster tickets based on the underlying problem they report. Then we measure how do those clusters grow and what is the impact to your revenue.
Connects to the support stack you already use
01 · The problem
01
The same bug gets logged as 'checkout error,' 'payment failed,' and 'card issue' — and nobody sees it's one problem.
02
Tag-based dashboards spike after the issue has already hit hundreds of customers.
03
Without exposed LTV, prioritization is a guessing game between support and product.
02 · What Issue Radar shows you
Every ticket gets clustered automatically into the issue it's really about. Then we tell you what matters.
Checkout — card declined
47 tickets · 38 customers
Tickets are grouped by what the customer actually means, not by the words they used. 'Card declined,' 'payment won't go through,' and 'checkout broken' land in the same issue.
See how fast an issue is growing — new tickets per hour, percent change vs. yesterday, and a trajectory line that flags acceleration before it becomes obvious.
$184,200
Sum of LTV across 38 affected customers
The total lifetime value of the customers affected by this issue, so you can prioritize what to fix first by revenue at risk — not ticket count.
03 · How it works
RipeText connects to Zendesk, Intercom, or Front and ingests every conversation as it happens.
Issue cluster
5 tickets · 4 customers
Conversations are grouped by meaning, in real time. New issues appear on the radar the moment the second ticket lands.
Ranked by exposed LTV · updated live
Every cluster ranks by velocity and exposed LTV. Click in to see the underlying tickets, the affected customers, and a draft summary you can send to engineering.
04 · Outcomes
A calm, analytical workspace for support and CX leaders who own response time, retention, and risk.
Detect emerging issues earlier
Surface new clusters at ticket #2, not after the dashboard catches up.
Reduce escalation response time
Hand engineering a ranked list with summaries, ticket links, and affected accounts.
Prioritize by customer revenue impact
Sort by exposed LTV instead of ticket count — fix the things that hurt most.
Align support and engineering faster
One source of truth on what's growing, who it's affecting, and what it costs.
Replace fragmented tagging systems
Stop relying on agents to manually classify what AI already understands.
Surface hidden patterns automatically
Catch the slow-burning regressions and policy gaps that never trip a tag-based alert.
Book a 20-minute demo and we'll show you the radar on a real support inbox.