The Slow Support Queue
Average resolution time rose a third the month the support team's new helpdesk went live, and the VP wants it rolled back. Find out what happened to the typical ticket, and what happened to the average, before anyone signs a rollback.
The brief
You have joined Bramblewick, a B2B software company, as an analyst on the customer operations team.
Your manager forwards a thread from the ops review:
"We moved support to the new helpdesk tool on 1 July. Average resolution time was about 20 hours in June and it is 27 hours in July — a third worse in one month. I want a decision on rolling back to the old tool by Friday."
Nobody has looked past the average. You have four months of ticket history, May to August.
What you are asked to do: establish what actually changed for the typical ticket, explain the number on the dashboard, and give a straight answer on the rollback.
All timestamps are UTC, and there is no other timezone to reconcile. The export was taken on 6 September.
Three notes from the team, worth reading before you start:
When a customer replies to a resolved ticket, both the old and the new tool open a NEW ticket row and record the original in reopened_from.
Some tickets were still open when the export was taken. Their resolved_at is blank.
One enterprise account was running a data migration in July. Nobody has checked what that did to the numbers.
Treat this as you would any first week on a new dataset: check the shape of the data before you trust a number, and be ready to show how you got every figure you report.
What to submit
- 1Your five verified answers, entered on the submission form.
- 2A one-page recommendation in your repo README — what happened to resolution time, why the dashboard says what it says, and whether to roll back. One page, not five.
- 3The SQL, Python or R you used to get your five answers, in the same repo.
- 4Optional: a chart, a dashboard link, or a three-minute Loom. None of these are required and none of them earn marks on their own.
How it is scored
| Area | Weight |
|---|---|
Data accuracyauto-graded The five verified answers. Graded automatically against the dataset, so this half of your score is arithmetic, not opinion. | 40 |
Problem framingreviewed Did you question which statistic the dashboard reports and whether it describes the typical ticket, rather than accepting a third worse as the fact to explain? | 15 |
Analysis and methodreviewed Did you separate reopens from new tickets, exclude still-open tickets from resolution time, and show what one account did to the mean — with the queries visible? | 20 |
Insight qualityreviewed Naming mean versus median is not the finding. Quantifying how much of the July average one account accounts for, and what the typical ticket actually did after the launch, is. | 15 |
Recommendationreviewed A clear answer on the rollback, argued from your own numbers, plus what the dashboard should report instead. Hedging scores low. | 10 |
The data
Two files, about 700 KB.
tickets.csv — one row per ticket. Columns: ticket_id, customer_id, opened_at, resolved_at (blank while the ticket is open), status (open or resolved), priority (P1–P4), channel, reopened_from (the original ticket_id when this row is a reopen, otherwise blank).
customers.csv — customer_id, plan, region.
Tickets opened 1 May–31 August 2026. The data is synthetic, generated for this challenge, and contains the kind of mess a real helpdesk export contains. No customer in it is a real company.
Recognition
Free to enter — everyone is scored and ranked
Every entrant is graded against the same rubric, ranked on the public leaderboard, and gets written feedback on their analysis — no fee, no prize, just the credential.
- The full brief and dataset
- Your score on the verified answers
- Written feedback on your analysis
- Ranked on the public leaderboard
- A certificate and badges you can add to your CV
Rules
- Scoring follows the published rubric below: 40% verified answers, graded automatically, and 60% your recommendation, scored against the rubric
- Scoring is automated. The top 10 submissions and any disputed score are reviewed by a human before results are finalized
- Your repository must be public and its commit history must show the work happening before the deadline
- Work individually. You may use any tool, including AI assistants — but you must be able to explain every number you submit
- One submission per person. You can update it any time before the deadline
- Everyone who submits is ranked by name on the public leaderboard when results are published
- Your repo has to be public for us to read it — but do not promote your solution on LinkedIn, X or Discord until judging closes, so others can still attempt it
