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Your tickets are a sensor.Stop treating them like noise.

Every ticket is a customer telling you something broke. Analytics reads every query to show what customers ask, why they ask it, and how that changes over time. It groups tickets by root cause, shows you the evidence, and helps you fix the thing so people stop needing to write in. The best ticket is the one that never gets sent.

Book a call → See example insights
Root causes, not word clouds Every claim comes with examples Per active user, not raw counts
What an insight looks like

Not "sentiment is down".A cause, the proof, and who should fix it.

Each insight says how sure it is and why. It shows the examples, the period, how much of your data it covered, and what else could explain it.

Insight #IN-031UI confusion

"Where are my invoices?" since the Settings redesign

Signal
Invoice-export questions per 1,000 active users doubled in the 3 weeks after the new Settings page shipped.
Examples
184 conversations · 12 picked for review
Period · coverage
Sep 30 – Oct 20 · 94% of tickets classified
Alternative explanations
  • ✓Quarter-end spike? Checked: last quarter-end rose far less.
  • ✓New users only? No: mostly accounts older than 6 months.
Insight #IN-034Payment failure

Renewals failing for cards that need extra bank checks

Signal
"Charged but no access" tickets cluster on renewals that needed a bank verification step.
Examples
67 conversations · 10 picked for review
Period · coverage
Oct 1 – Oct 21 · 88% of tickets classified
Alternative explanations
  • ✓One bank's outage? No: spread across many banks.
  • ?App version: not ruled out. Needs your event logs.
Insight #IN-037Manual ops → flow

Your team changes shipping addresses by hand, every day

Signal
Address changes before dispatch are the most repeated manual action by your human agents.
Examples
310 conversations · same 4 steps each time
Period · coverage
Last 30 days · 97% of tickets classified
Alternative explanations
  • ✓Checkout bug? Partly: 1 in 5 are typos a field check would catch.

Illustrative insights with made-up numbers. They show the format, not results from a real company.

What it finds

Six kinds of broken.Each one has an owner outside support.

Behind every cluster is a real person who got stuck and took the time to tell you. Analytics makes sure that message reaches the team who can fix the cause, not just the person who answered it.

Docs

Documentation gaps

Questions your help center doesn't answer, or answers wrong.

→ Owner: docs or support lead
Product

Onboarding and UI confusion

Where new users get stuck. Buttons people can't find after a redesign.

→ Owner: product or design
Revenue

Payment and access failures

Charged but locked out. Renewals that fail. Logins that loop.

→ Owner: payments or engineering
Automation

Frequent manual work → new flow

The same steps done by hand, over and over. These become Agent flows.

→ Owner: support ops
Experience

Causes of dissatisfaction

Not "customers are unhappy". Why: slow refunds, unclear pricing, broken promises.

→ Owner: depends on the cause
Process

Process drift

The gap between the process you documented and the one your team actually follows.

→ Owner: support lead
The closed loop

A finding isn't a fix.The loop closes when the number moves.

Dashboards stop at step one, then wait politely for someone to do something. We follow one insight all the way to a measured result, then check it worked.

  1. Signal

    A cluster grows faster than your user base.

    Invoice questions ×2

  2. Examples & check

    Read real conversations. Rule out the boring explanations.

    12 read · not seasonal

  3. Hypothesis

    A plain sentence that could be wrong.

    "Settings redesign hid it"

  4. Owner

    One named team, not "someone".

    Product team

  5. Change

    The fix ships, with a date.

    Search added · Oct 22

  6. Measured result

    Same metric, same denominator, before and after.

    Back to old rate?

Illustrative example. If the number doesn't move, the hypothesis was wrong and the loop starts again. That's the point.

Honest measurement

Raw counts lie.Divide by the people who could write in.

If your user base doubles, your tickets will too. That's not a problem, that's growth (congratulations, by the way). So Analytics measures tickets per active user or per order, not raw ticket counts.

Switch the view and see how the story flips.

Login problemsInvoice export
Weekly tickets for two topics over 8 weeks
Login problems, week 1 → 8—
Invoice export, week 1 → 8—

Illustrative data. Active users grow from 40k to 80k over the 8 weeks.

Support weather forecast

Next week's tickets, before they arrive.Bring an umbrella.

Analytics knows which questions followed your past releases, renewals and price changes. Put that next to what's on next week's calendar and you get a heads-up: what customers are likely to ask, so the answer is ready before they have to write in. Pick a day to see the reasoning.

Illustrative forecast with made-up numbers and a made-up calendar. It's a playful way to show how trends in Analytics can warn you early. It isn't a forecasting feature with a published accuracy.

Straight answers

Analytics questions.No marketing answers.

How is this different from tagging reports?
Tags tell you what someone filed a ticket under. Analytics groups tickets by root cause, shows example conversations, and says how much of your data it covered. It also checks the obvious alternative explanations before calling something a trend.
Where does "active users" come from?
From you. We need a denominator you trust, such as active users, orders or subscriptions, sent via API or a regular export. Without it, we'll show raw counts and say clearly that they're raw.
Can it be wrong?
Yes. That's why every insight shows its examples, its coverage and the alternatives it checked. A hypothesis is a guess until the change ships and the number moves. If it doesn't move, we say so, even when it was our idea.
Do you train models on our tickets?
No. We sign an NDA and DPA before we access anything, and your customer data is never used to train models. Read exactly what we do.
How much does it cost?
Analytics is included in the Scale plan. If you're on another plan or only want Analytics, talk to us. See pricing.
P.S.

Every ticket in your queue is someone who took the time to tell you something broke, instead of quietly leaving. Let’s make sure that time wasn’t wasted. I’ll bring the umbrella.

OleksiiSay hi on LinkedIn →

What are your customers trying to tell you?Let's read it together.

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