Pareto analysis of support tickets: which issues to fix first
Support backlogs almost never spread evenly across issue types. A handful of recurring problems usually create most of the volume, and a Pareto analysis makes that visible in one chart. Below is a complete worked example you can run and edit yourself.
· 6 min read
Example support ticket volume for one month
| Issue | Tickets |
|---|---|
| Login failures | 2,100 |
| Payment errors | 1,400 |
| Slow dashboard | 800 |
| Billing questions | 400 |
| Email not received | 300 |
| Export problems | 180 |
| Feature requests | 120 |
These numbers are an illustrative sample, not real customer data. Replace them with your own export to get your result.
Step 1 — Group tickets into causes, not symptoms
A Pareto analysis is only as good as its categories. Group tickets by the underlying cause a team could actually fix, not by the wording customers used. "Cannot log in", "password reset loop" and "session expired" belong in one login bucket.
Keep the list short. Somewhere between five and fifteen categories is usually enough to see the shape; more than that and the chart turns into noise.
Step 2 — Read the cumulative curve
The bars show volume per issue. The line shows the running total as a percentage. Where that line crosses your threshold — 80% by default — is your cut-off: everything to its left is the vital few.
In the sample above, login failures and payment errors alone dominate the total. Fixing those two removes far more work than the four smallest categories combined.
Step 3 — Turn the split into a decision
The point of the analysis is the next action. For each category inside the vital few, name an owner and one concrete change: a bug fix, a help article, an in-product hint, a retry on a failing payment call.
Everything in the long tail goes on a watch list. You are not ignoring it — you are choosing not to spend this cycle on it.
- Vital few → scheduled work with an owner this cycle
- Long tail → reviewed next month, no work now
- Re-run the analysis after the fixes ship to confirm the shape changed
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Example support ticket volume for one month
- Total
- 5,300
- Categories
- 7
- Vital few
- 3
- Top 20% share
- 66%
Priority #1
Login failures
39.62% of total · 2,100
| # | Category | Value | Share | Cumulative |
|---|---|---|---|---|
| 1 | Login failures Vital few | 2,100 | 39.62% | 39.62% |
| 2 | Payment errors Vital few | 1,400 | 26.42% | 66.04% |
| 3 | Slow dashboard Vital few | 800 | 15.09% | 81.13% |
| 4 | Billing questions | 400 | 7.55% | 88.68% |
| 5 | Email not received | 300 | 5.66% | 94.34% |
| 6 | Export problems | 180 | 3.4% | 97.74% |
| 7 | Feature requests | 120 | 2.26% | 100% |
3 of 7 categories account for 81.1% of total impact.
Vital few: Login failures, Payment errors, Slow dashboard.
Login failures is the largest contributor at 39.62%.
Login failures represents 2,100 of a total of 5,300.
Moderate concentration: the top 20% of categories hold 66%.
There is a leading group, but the long tail still matters.
4 categories share the remaining 18.9%.
These are the trivial many under the Pareto principle.
Review Login failures first.
It is the single largest contributor at 39.62% of total impact.
Review opportunities across the wider process.
Impact is spread out — the top 20% only accounts for 66%.
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Key takeaways
- Group by fixable cause, not by customer wording.
- The threshold crossing point, not the number 80, defines your shortlist.
- Re-run monthly: the vital few move once you fix them.