Diagnose a 62% Spike in Churn Before the Board Meeting on Thursday
The Scenario Your company: Fieldly — a B2B SaaS platform that helps field service teams manage scheduling, dispatch, and invoicing. Customers range from one-person HVAC contractors to mid-size property maintenance firms. Your role: Data…
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What You'll Be Doing
The Scenario
Your company: Fieldly — a B2B SaaS platform that helps field service teams manage scheduling, dispatch, and invoicing. Customers range from one-person HVAC contractors to mid-size property maintenance firms.
Your role: Data Analyst. You report to the VP of Operations and work closely with the Customer Success and Product teams.
The Situation
Maya Chen, Head of Customer Success, has just come off a difficult conversation with the CEO. Monthly churn has risen from 2.1% to 3.4% over the past six months — a 62% increase — and the board wants answers at the quarterly review in five days.
Maya stops by your desk with a simple ask:
"I need to know who is churning, why they're churning, and what we should do about it. I have to present this to the board on Thursday. Can you dig into the data?"
You have access to three datasets (provided as CSV files): Download datasets
| FILE | WHAT IT CONTAINS | KEY COLUMNS |
|---|---|---|
| customers.csv | 500 customer accounts — subscription and revenue data | customer_id, industry, company_size, plan_tier, mrr_usd, signup_date, churned (Y/N), churn_date |
| usage.csv | Product engagement for each account over the past 6 months | customer_id, avg_monthly_logins, feature_scheduling_uses_6mo, feature_dispatch_uses_6mo, feature_invoicing_uses_6mo, features_used_count, last_active_date |
| support.csv | Support ticket history per account (last 6 months) | customer_id, tickets_submitted_6mo, avg_resolution_days, primary_ticket_category |
Your job is to diagnose the churn increase, surface the most important insight, and give Maya something she can take to the board.
Constraints
These constraints reflect real conditions you would navigate in this role. Honor all of them in your submission.
| # | CONSTRAINT |
|---|---|
| 1 | Work only with the provided datasets. You cannot collect additional data, run customer surveys, or access systems not described in the data dictionary. Your analysis must be defensible from what you have. |
| 2 | Your analysis must be reproducible. Document your methodology clearly enough that another analyst on the team could replicate every number in your findings without asking you a question. |
| 3 | Your recommendation to Maya must be actionable within 30 days. No six-month roadmaps, no proposals that require new tooling or budget approval. What can the team do with what they have, right now? |
| 4 | The board presentation context means your key finding must be expressible in two sentences. If it takes a paragraph to state the finding, it is not the finding — it is the methodology. Lead with the conclusion. |
| 5 | You own this analysis. If a number is wrong in your submission, that is your number — regardless of how it was generated. Verify before you publish. |
What You'll Accomplish
Diagnose a business problem using messy, multi-source data — join and analyze three separate datasets to identify the primary drivers behind a meaningful change in a core metric.
Translate analysis into a board-ready narrative — distill complex findings into a two-sentence insight and a clear recommendation that a non-technical audience can act on immediately.
Build a reproducible analytical workflow — document methodology with enough clarity that another analyst can replicate every number without a handover conversation.
Recommend under real constraints — propose interventions that are actionable within 30 days using only the resources the team already has, rather than defaulting to long-term roadmaps or new tooling.
How Your Work Will Be Scored
What to Submit
Deliverable 1 — Analysis Document
Format: .pdf, .doc, .docx, .rtf, .txt, .md
A structured analysis document (PDF or Word) containing your findings, methodology, and recommendations. This is your primary work artifact — it should stand on its own as something Maya could share internally.
- Identify which customer segments are churning at the highest rate
- Determine what behavioral or operational signals best predict churn in this dataset
- Recommend one primary action Maya should prioritize — with a rationale
- Include at least two visualizations or charts that make your findings clearer to a non-technical reader
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Deliverable 2 — README
Format: .pdf, .doc, .docx, .rtf, .txt, .md
A written document with exactly three sections. Name each section clearly.
Section A — Written Analysis (300–500 words)
- Your diagnosis: what is driving the churn increase, and what in the data supports that conclusion?
- Your primary recommendation: what should Fieldly do first, and why?
- Key trade-offs: what are you not recommending, and what are you trading off by focusing here?
- Limits of the analysis: what does this data not tell you — and what would you need to validate your finding?
Section B — Stakeholder Brief (150–200 words)
Write a plain-language summary Maya could present to the board directly. Assume your audience are smart business people who are not data analysts. The key finding must appear in the first two sentences. No technical jargon, no statistical notation.
Section C — AI Usage Log (Mandatory) This is not a trick. We want to see how you work with AI — not whether you used it.
In a short section of your README, document your AI collaboration process. For each significant interaction with an AI tool, briefly note:
- What you asked the AI to help with
- What it gave you
- What you kept, changed, or rejected — and why
Three interactions documented is sufficient. The log does not need to be exhaustive.
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Deliverable 3 — Video Walkthrough
Format: .mp4, .mov, .webm
Record your walkthrough as an MP4 or MOV file and upload it directly on the Provn platform as a separate file.
Structure your video:
- Opening (60 seconds) — state the business problem and your primary finding
- Analysis walkthrough (2–3 minutes) — walk through your analysis document; explain your key decisions, not everything you tried
- Stakeholder brief (1 minute) — read or describe your Section B brief; explain how you made it accessible to a non-analyst audience
- Mandatory AI question (1–2 minutes) — "Walk me through one moment where you disagreed with, pushed back on, or redirected what the AI gave you — and what you did instead. Name the specific moment. Explain what the AI produced that didn't meet the bar, what you did differently, and why."
- Reflection (30–60 seconds) — what would you do differently, or what would you investigate next with more time?
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