Diagnose the E-commerce Revenue Drop
The dashboard shows September revenue down about 15% at a home-goods retailer, and the team blames soft demand. Check whether that holds — and whether 15% is even the real number — then make the call on the paid social budget.
The brief
You have joined UrbanNest, a direct-to-consumer home goods retailer, as an analyst.
Your manager forwards a thread from the monthly business review:
"September net revenue came in around 15% below August on the dashboard. The assumption on the call was that demand has softened and competitors are discounting. Marketing has proposed cutting the paid social budget by 40% next quarter. Before we sign that off, can someone check the numbers properly?"
Nobody has verified the assumption — or the 15%. You have eleven months of order-level data.
What you are asked to do: establish what actually changed, and give a recommendation on the paid social cut that your manager can take back to that meeting.
All timestamps in the data are UTC. Report by UTC calendar month; there is no other timezone to reconcile.
Two notes from the team, worth reading before you start:
A payment gateway change went live in late August. Engineering mentioned something about retry behaviour; it was not followed up.
Refunds are recorded as separate rows, not as adjustments to the original order.
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
- 1Executive summary, one page — what happened, why it happened, and what you would do. Put it in your repo README.
- 2Analysis covering the revenue trend, customer behaviour, channel mix, and the refund and payment data. Show your SQL (or Python/R).
- 3A dashboard or chart — Tableau, Power BI, Looker Studio, or even Excel. Any tool, as long as the link is public.
- 4A recommendation on the question asked: should UrbanNest cut the paid social budget? Five slides maximum, or the equivalent in your README.
- 5Your five verified answers, entered on the submission form.
- 6Optional: a three-minute Loom walking through your reasoning.
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 test the assumption you were handed rather than confirm it? Restating the brief scores low. | 15 |
Analysis and methodreviewed Did you handle the duplicate gateway rows and the separate refund rows — and show how? Correct numbers with no visible method score low. | 20 |
Insight qualityreviewed Naming the symptom is not the finding. The cause, with evidence, is. | 15 |
Recommendationreviewed A clear answer on the paid social cut, argued from your own numbers. Hedging scores low. | 10 |
The data
Three files, ~2 MB total.
orders.csv — one row per order line. Columns: order_id, customer_id, order_ts_utc, sku, category, quantity, unit_price, discount_code, discount_amount, revenue, channel, device, is_refund.
customers.csv — customer_id, signup_date, region.
products.csv — sku, product_name, category, list_price.
The data covers December 2025 through October 2026. It is synthetic, generated for this challenge, and contains the kind of mess real order tables contain. No customer in it is a real person.
Results
All 3 submissions, scored on diagnosis, rigour, judgment and communication.
- 1Lasni G. Fanyati S.WinnerPerfect Objective Score83
- 2Rid T.Top 368
- 3HaleemaTop 331
Scores are 40% verified answers, graded automatically, and 60% written analysis against the published rubric. All-time leaderboard
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 analysis, 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
- 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
