Interview Questions — Customer Churn Analysis¶
These are the questions an interviewer might ask after you present this project. Prepare answers for all of them.
[Beginner] What is customer churn and why do companies care about it?
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Churn is when a customer stops using a product or service. Companies care because: (1) acquiring a new customer costs 5-7× more than retaining an existing one; (2) churned customers represent permanent lost revenue; (3) high churn signals product or service problems.
For a subscription business, even a 2% monthly churn = 22% annual churn — the business must replace nearly a quarter of its customers every year just to stand still.
[Beginner] How did you calculate churn rate in this project?
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For this dataset, the churn rate is a cross-sectional snapshot:
In a production setting, you'd calculate it as a period metric:
This dataset doesn't have dates, so we can't compute the true time-based rate — a limitation I'd note when presenting.
[Mid-level] You found that fiber optic customers churn at 42% — how would you determine if this is a pricing problem or a quality problem?
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I'd segment the analysis further:
Price signal: do fiber customers who pay the highest charges churn more than lower-paying fiber customers? If yes, price sensitivity may be a factor.
Quality signal: do fiber customers who contacted support multiple times churn more? I'd need to join with the support ticket table: JOIN support_tickets ON customerID. If churners had 2+ unresolved tickets in their final 30 days, quality is the likely cause.
Competitor signal: did churn increase around competitor promotions? Needs external market data.
A practical first step: a post-churn survey. Ask recently churned fiber customers why they left. Qualitative data is often faster than building the cross-join.
[Mid-level] Walk me through the churn risk scoring logic you built.
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The churn risk score is a weighted sum of the features most correlated with churn in the analysis:
| Feature | Weight | Rationale |
|---|---|---|
| Month-to-month contract | +30 | Highest churn rate (42.7%) |
| Tenure < 12 months | +20 | New customers churn most |
| Fiber optic service | +15 | 42% churn vs 19% DSL |
| Monthly charge > £70 | +10 | Correlated with fiber + high expectations |
| 0 add-ons | +15 | No sticky services → easy to leave |
| Manual payment | +10 | Lower commitment signal |
This is a heuristic model, not a trained ML classifier. It's interpretable and can be built entirely in SQL. A proper churn model would be logistic regression or gradient boosting — but this is a good first approximation that the business can use immediately.
Limitation: I haven't validated the weights statistically. I would calibrate these against actual churn data — do high-scoring customers actually churn at higher rates than low-scoring ones?
[Mid-level] What other data sources would improve this analysis?
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Support tickets — customers who churn often contact support 30-60 days before leaving. Number of tickets, resolution time, and ticket category are powerful predictors.
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Product usage logs — how often does the customer log in, use the app, consume data? Declining usage often precedes churn.
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Billing history — late payments are a leading indicator. Customers who consistently pay late are more likely to churn.
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Competitor pricing — knowing when competitor promotions launched helps explain churn spikes.
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Marketing touchpoints — was this customer recently emailed? Did they open the retention campaign? Response to retention efforts is itself a predictive signal.
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NPS/CSAT scores — customers who rate satisfaction 6/10 or below are at risk. This is a direct early warning signal.
[Senior] The retention team has budget to contact 200 customers this month. You have 847 at-risk customers. How do you prioritise?
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I'd prioritise by expected value of retention:
Practical approach: 1. Prioritise by revenue at risk, not just churn probability. A 60% churn-risk customer paying £120/month (£72 expected MRR at risk) outranks a 80% churn-risk customer paying £25/month (£20 expected MRR at risk).
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Within same revenue band, prioritise by win-back probability. Customers who contacted support about a specific issue (fixable) are more likely to be retained than customers who said "switching to competitor" in their cancellation survey.
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Use propensity scoring. If we have historical retention campaign data, build a model: given a customer profile, what is P(retain | outreach)?
Segmentation for the 200 contacts: - Top 50: >£100/month MRR, score 70+, fixable issue → call - Next 100: £50-100/month, score 50+, discount eligible → call + offer - Next 50: £30-50/month, score 60+ → email + discount
[Senior] This dataset shows 26.5% churn — much higher than typical telecom. A stakeholder argues the model won't generalise to TelcoMax's real customers. How do you respond?
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The stakeholder is right to raise this. This is a dataset bias concern.
Valid limitations: - The IBM dataset was created for educational purposes and may not reflect any real company's distribution - Our risk score was trained and evaluated on the same dataset — no out-of-sample validation - TelcoMax's product mix, pricing, and customer demographics may differ significantly
How I would address it: 1. Validate feature direction on TelcoMax data. I expect the same features to matter (contract type, tenure, add-ons) even if the exact weights differ. Run the same SQL queries on TelcoMax's real data and verify the directional relationships hold.
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Recalibrate scores. Using TelcoMax's historical churn data, fit a logistic regression to re-estimate the weights for each feature.
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Track model lift. After using the risk score to prioritise retention outreach for one quarter, compare: did customers with high risk scores actually churn more than low-risk customers? This is the real validation.
The educational dataset teaches methodology. Production deployment requires validation on real data.
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