Customer issues don’t announce themselves. They compound quietly, one ticket, one complaint, one churn at a time, until the pattern is obvious and the damage is done. Dovetails catches them in hours, not months.
Get in touch“We keep seeing one-offs that don’t seem connected, but something feels off.”
Issues tagged differently by different agents appear unrelated in isolation. No single ticket crosses the escalation threshold, so no one flags it. Meanwhile, the same problem is happening dozens of times under different labels. By the time anyone notices, you’ve had hundreds of customers experience it.
“An agent escalated something last week that everyone’s now chasing. But I’m not sure it’s actually our biggest problem.”
Your escalations are real signals. The problem is knowing which ones matter most. Without data behind them, you’re prioritizing by whoever was loudest last week, not by what’s actually driving churn. Your instincts are right. The data just needs to back them up.
“By the time my team told me about the issue, we’d been sitting on it for two weeks.”
Every day between first signal and first action multiplies the cost. Waiting for a problem to become obvious is expensive. Get a smoke detector. Don’t wait for the house to be on fire.
Dovetails continuously monitors your support conversations, tickets, and feedback. It groups similar issues automatically, tracks how patterns shift over time, and alerts you when something starts moving in the wrong direction.
Out of the box, it’s adding value the moment you connect your data. No configuration required. No tagging dependencies. No manual querying.
The 80% of value that matters most happens automatically: alerts, pattern detection, anomaly flags. The other 20% is there when you want to go deeper: tag new patterns, customize what you track, tune your thresholds. Most teams will want the first 80. Some will use all of it.
This isn’t a reporting tool. It’s revenue protection.
Dovetails groups similar conversations automatically. No tagging, no rules, no agent classification required. Issues that would have appeared as disconnected one-offs get surfaced as a single pattern. See what’s actually happening, not what your categories were built to capture.
When a pattern starts spiking, even slightly, Dovetails flags it before it becomes a problem. Think of it as a smoke detector for your CX data. You find out in 48 hours, not two weeks from now when a manager escalates it.
Heat maps, time series, and proportional views let you see how issues evolve across configurable time windows. Understand whether something is growing, stabilizing, or fading, and how it compares to your normal baseline.
Once something’s been flagged, you can explore the underlying conversations directly. Filter, read, and query the data in natural language. Most of the time you won’t need this. But when something needs a root cause, it’s there.
These figures come from back-testing Dovetails against real incident data. A single undetected fulfillment issue, caught days later instead of hours, cost one subscription company between $13,000 and $26,000. More than half was preventable.
