Interview Dialogue: Customer Churn Prediction Case Study

Introduction:

Case studies are a fundamental part of data science interviews, offering candidates a platform to showcase their problem-solving abilities, technical expertise, and business acumen. They provide a glimpse into how real-world data science problems are approached, dissected, and solved. This dialogue between an interviewer and interviewee takes you through a detailed exploration of a customer churn prediction case study, giving valuable insight into how such problems are tackled during an interview setting.

This discussion not only highlights the steps involved in solving a data science case study—from understanding the problem, data exploration, and feature engineering, to model selection and deployment—but also demonstrates how candidates can effectively communicate their thought process and technical decisions. If you're preparing for a data science interview, this dialogue offers a blueprint for how a typical case study interview unfolds and the type of reasoning expected throughout the process.

Interview process:

Interviewer:
Alright, let's dive into a case study. Imagine you’re working for a telecom company that’s struggling with customer churn. The company wants to build a machine learning model to predict whether a customer will churn or not. How would you approach this problem?

Interviewee:
Great question! First, I’d start by framing the problem as a binary classification task where the goal is to predict churn as either 0 (no churn) or 1 (churn). The key here is understanding the customer data we have and the factors driving their decisions.

Interviewer:
Exactly, predicting churn is all about understanding customer behavior. What kind of data would you expect to have access to for this problem?

Interviewee:
We’d likely have data in several categories:

• Customer demographics like age, gender, location, and income.
• Service usage data such as data consumption, call minutes, and type of plan.
• Account details like contract length, monthly charges, and payment history.
• Customer service interactions, such as the number of support tickets or complaints.

Interviewer:
That sounds comprehensive. What would your next step be after collecting the data?

Interviewee:
Once the data is collected, I would move on to exploratory data analysis (EDA). The aim here is to understand the distribution of the data and identify trends that might indicate churn. For example, do customers with shorter contract lengths churn more? Or does frequent interaction with customer service correlate with churn?

Interviewer:
Good. How would you handle any missing data or anomalies you might encounter?

Interviewee:
For missing data, I’d first identify which features have missing values. Depending on the situation, I could use:

• Imputation: For numerical features, we can fill missing values with the median or mean. For categorical data, we could use the mode or create a special "missing" category.
• If there’s too much missing data in certain columns, I’d consider dropping those columns altogether.
• Outliers: For anomalies, I would visualize the data with box plots or histograms. If any values look unrealistic, I might remove or cap them.

Interviewer:
Nice. After EDA, you’d probably move on to feature engineering, right? How would you go about that?

Interviewee:
Yes, feature engineering is crucial. Some steps I would take include:

• Creating aggregate features: For instance, dividing total data usage by the number of months to get average monthly data usage.
• One-hot encoding for categorical features like contract type or gender.
• Normalizing numerical features like monthly charges, so that all values are on the same scale, especially for algorithms like logistic regression or SVM.

Interviewer:
Good call on one-hot encoding. Now, feature selection can be tricky. How do you identify which features are most important?

Interviewee:
During EDA, I’d use correlation matrices to identify relationships between features and churn. But during model building, algorithms like Random Forest or Gradient Boosting automatically provide feature importance scores, which can help identify the most influential features. For instance, we might find that contract length or number of complaints are the top predictors of churn.

Interviewer:
Alright, you’ve prepared your data well. Now comes the modeling. Which algorithms would you consider for this classification problem?

Interviewee:
I’d start with a few different models to compare:

• Logistic Regression: It’s simple, interpretable, and good for a baseline.
• Random Forest: Excellent for handling large datasets and identifying feature importance.
• Gradient Boosting Machines like XGBoost or LightGBM, as they tend to perform very well with tabular, structured

data, especially when the target class is imbalanced, which is often the case with churn prediction.

Interviewer:
You mentioned imbalanced data. Churn prediction datasets typically have far more non-churners than churners. How would you handle that imbalance?

Interviewee:
Exactly, the imbalance is common and can lead to poor performance. To handle this, I’d consider:

• Resampling techniques: Oversampling the minority class (churners) using something like SMOTE (Synthetic Minority Over-sampling Technique), or undersampling the majority class (non-churners).
• Class weights adjustment: Many models, such as logistic regression or Random Forest, allow you to assign more weight to the minority class, so misclassifications of churners are penalized more heavily.
• Evaluation metrics: Instead of accuracy, I’d focus on metrics like precision, recall, and F1-score to better understand how well the model is capturing churners.

Interviewer:
Good point on evaluation metrics. So, after you've chosen your models, how would you evaluate their performance?

Interviewee:
I’d split the data into training and test sets, train the model on the training set, and evaluate it on the test set. For evaluation, as I mentioned, I’d use:

• Confusion matrix: To see how many churners and non-churners were correctly and incorrectly classified.
• Precision: The proportion of predicted churners who actually churned.
• Recall: The proportion of actual churners that the model correctly identified.
• F1-Score: The harmonic mean of precision and recall, balancing both.
• ROC Curve and AUC (Area Under Curve): To evaluate the trade-off between true positives and false positives and to summarize the model’s overall performance.

For example, if my Random Forest model had a precision of 0.82 and recall of 0.75, I’d be quite satisfied, especially if the AUC score was above 0.85, indicating a good balance between sensitivity and specificity.

Interviewer:
That sounds solid. Let’s say your model is performing well. How would you ensure that it’s interpretable for the business stakeholders? They often want to know why a customer is likely to churn.

Interviewee:
Interpretability is key for business buy-in. For tree-based models like Random Forest or XGBoost, I’d generate a feature importance plot, showing the most significant factors driving churn. For example, if contract length and monthly charges are highly important features, I’d explain that customers on short-term contracts or with higher bills are more likely to churn.

Additionally, using partial dependence plots (PDPs) could help show how a particular feature, like tenure, affects the probability of churn.

Interviewer:
Excellent approach. Now, once you have the model ready, how would you deploy it to actually start helping the business reduce churn?

Interviewee:
For deployment, I’d package the model as an API so that the company's CRM system could send customer data and receive churn predictions in real-time. This way, as soon as a customer’s likelihood of churning crosses a certain threshold, the system can trigger proactive retention strategies like offering a discount or better plan.

Interviewer:
And what about monitoring the model after deployment? Any thoughts?

Interviewee:
Yes, monitoring is essential. I’d set up a system to track the model’s performance over time. Data drift might occur, meaning the data distribution could change, leading to a decrease in model accuracy. I’d periodically retrain the model with the most recent data to keep it relevant. I’d also track metrics like precision and recall on new data to ensure the model maintains its predictive power.

Interviewer:
Fantastic. Finally, how would you quantify the business impact of the model?

Interviewee:
I’d calculate how many customers the company would have lost without the model and compare it to the actual churn after implementing the model’s recommendations. For example, if we reduce churn by just 10%, we could estimate the savings from retaining those customers. If the company typically loses 5,000 customers monthly and spends $300 to acquire a new customer, saving 500 customers would result in $150,000 in monthly savings.

Interviewer:
That’s a great example of translating data science work into tangible business value. Well done!

Interviewee:
Thank you!

Conclusion:

This dialogue gave us an introspective look at how case study problems in data science are solved and how a typical interview case study discussion works. Case studies are more than just technical exercises—they are an opportunity to demonstrate structured problem-solving, analytical thinking, and the ability to apply machine learning concepts to business problems.

From this discussion, we learned the importance of identifying key features, handling data imbalances, choosing appropriate models, and ensuring interpretability for stakeholders. More importantly, we saw how an interviewee is expected to explain their thought process, handle the interviewer’s questions, and justify their decisions. This approach is not just about arriving at the correct solution but showing how well you understand the problem and can communicate your expertise effectively.