The Pipeline Mirage
Win rate has fallen from about 31% to 10% since the new SDR team started, and the VP of Sales wants it disbanded. Check what the dashboard is dividing by, and whether the two teams are being compared at the same stage, before six people lose their jobs.
The brief
You have joined Northlane, a B2B software company, as an analyst on the revenue operations team.
Your manager forwards a thread from the VP of Sales:
"Win rate was 31% in Q1. Since the outbound SDR team started on 1 April it is 10% and falling. Six salaries for a third of the results. I want a recommendation on winding the team down before the Q3 plan is locked next week."
Nobody has asked what the dashboard's win rate is made of. You have nine months of opportunities and the accounts they belong to.
What you are asked to do: establish what has actually happened to win rate, whether the new team is worse, and give a straight answer on disbanding it.
All timestamps are UTC. The export was taken on 1 July 2026.
Three notes from the team, worth reading before you start:
The dashboard's win rate is won deals divided by deals created in the quarter.
The old process only created an opportunity once a deal reached Proposal. The new team creates one at Qualification. proposal_at records the date a deal reached Proposal, whichever way it got there.
A CRM migration on 15 May re-created some open opportunities as new rows. Those rows carry migrated_from.
Treat this as you would any rate that fell off a cliff: look at the denominator first, then at whether the two things being compared are the same thing — 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 win rate, whether the new team is worse, and whether to disband it. 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 ask what the dashboard divides by and whether unresolved deals belong in it, rather than taking the fall as the fact to explain? | 15 |
Analysis and methodreviewed Did you compute win rate on resolved deals, compare the teams at the same stage, and count each opportunity once — with the queries visible? | 20 |
Insight qualityreviewed Naming right-censoring is good. Sizing how much of the fall is open pipeline and how much is stage mix, and showing the stage-matched comparison, is the finding. | 15 |
Recommendationreviewed A clear answer on the team, argued from your own numbers, plus what the dashboard should report instead. Hedging scores low. | 10 |
The data
Two files, about 700 KB.
opportunities.csv — one row per opportunity record. Columns: opp_id, account_id, created_at, source_team (inbound, sdr_core or sdr_new), initial_stage, proposal_at (the date the deal reached Proposal; blank if it never did), current_stage, outcome (won, lost or open), closed_at, amount_usd, owner, migrated_from.
accounts.csv — account_id, account_name, segment, industry.
Opportunities created 1 October 2025–30 June 2026. A typical deal takes about two months from Proposal to a decision. The data is synthetic, generated for this challenge. No account 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
