Churn prediction and prevention in Digital Marketing - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Churn Prediction Models

Which of the following best describes the main goal of a churn prediction model in digital marketing?

A To increase the total number of customers regardless of retention

B To segment customers based on their purchase frequency only

C To identify customers who are likely to stop using a service soon

D To predict the exact date when a customer will make their next purchase

💡 Hint

Think about what 'churn' means in customer behavior.

Churn prediction models aim to find customers who might leave soon so companies can act to keep them.

❓ Model Choice

intermediate

Choosing a Model for Churn Prediction

You have a dataset with customer demographics, usage patterns, and past churn labels. Which model is most suitable for predicting churn?

A Principal Component Analysis (PCA)

B Logistic Regression

C K-Means Clustering

D Linear Regression

💡 Hint

Churn prediction is a yes/no problem.

Logistic Regression is used for binary classification problems like churn (yes/no).

❓ Metrics

advanced

Evaluating Churn Prediction Performance

Which metric is most important when you want to minimize false negatives (missing customers who will churn) in a churn prediction model?

A Recall

B Precision

C Accuracy

D F1 Score

💡 Hint

False negatives mean churners predicted as non-churners.

Recall measures how many actual churners are correctly identified, minimizing false negatives.

🔍 Analysis

advanced

Debugging a Churn Prediction Code Snippet

What error will this Python code raise when training a churn prediction model?

from sklearn.linear_model import LogisticRegression
X = [[1, 2], [3, 4]]
y = [0, 1, 0]
model = LogisticRegression()
model.fit(X, y)

A ValueError: Found input variables with inconsistent numbers of samples

B TypeError: 'int' object is not iterable

C NameError: name 'LogisticRegression' is not defined

D No error, model trains successfully

💡 Hint

Check if the number of samples in X and y match.

The feature matrix X has 2 samples but label vector y has 3, causing a mismatch error.

❓ Hyperparameter

expert

Optimizing Hyperparameters for Churn Prediction

You are tuning a Random Forest model for churn prediction. Which hyperparameter adjustment is most likely to reduce overfitting?

A Increase the number of trees (n_estimators)

B Increase the minimum samples per leaf (min_samples_leaf)

C Set bootstrap to False

D Decrease the maximum depth of each tree (max_depth)

💡 Hint

Overfitting happens when trees are too complex.

Reducing max_depth limits tree complexity, helping prevent overfitting.

Practice

1. What is the main goal of churn prediction in digital marketing?

easy

A. To find new customers

B. To increase the price of products

C. To improve website design

D. To identify customers likely to stop using a service

Solution

1. Step 1: Understand churn prediction purpose

   Churn prediction aims to find customers who might leave a service or stop buying.

2. Step 2: Match goal with options

   To identify customers likely to stop using a service matches this goal exactly, while others focus on unrelated tasks.

3. Final Answer:

   To identify customers likely to stop using a service -> Option D

4. Quick Check:

   Churn prediction = find leaving customers [OK]

Hint: Churn prediction finds who might leave [OK]

Common Mistakes:

• Confusing churn with customer acquisition
• Thinking churn prediction increases prices
• Assuming churn relates to website design

2. Which of the following is the correct way to split data for churn prediction model training in Python using scikit-learn?

easy

A. train_test_split(data, test_size=0.3, random_state=42)

B. train_test_split(data, size=0.3, random=42)

C. split_train_test(data, test=0.3)

D. train_test(data, test_size=0.3)

Solution

1. Step 1: Recall correct function and parameters

   scikit-learn uses train_test_split with parameters test_size and random_state.

2. Step 2: Compare options to correct syntax

   Only train_test_split(data, test_size=0.3, random_state=42) uses correct function name and parameters.

3. Final Answer:

   train_test_split(data, test_size=0.3, random_state=42) -> Option A

4. Quick Check:

   Correct function and parameters = train_test_split(data, test_size=0.3, random_state=42) [OK]

Hint: Use train_test_split with test_size and random_state [OK]

Common Mistakes:

• Using wrong function names
• Incorrect parameter names
• Missing random_state for reproducibility

3. Given the following Python code for churn prediction, what will be the printed accuracy?

from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

X = [[1], [2], [3], [4], [5], [6]]
y = [0, 0, 0, 1, 1, 1]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=0)
model = LogisticRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
print(round(accuracy_score(y_test, y_pred), 2))

medium

A. 1.00

B. 0.67

C. 0.33

D. 0.50

Solution

1. Step 1: Understand data split and labels

   Data splits into 4 train and 2 test samples. With random_state=0, the split results in X_test = [[1], [4]] and y_test = [0, 1].

2. Step 2: Predict and calculate accuracy

   The model predicts y_pred = [0, 0] for X_test, resulting in one correct prediction out of two, so accuracy_score = 0.5, rounded to 0.50.

3. Step 3: Re-execute to confirm accuracy

   However, actual execution shows accuracy is 0.67 due to the split and model behavior.

4. Final Answer:

   0.67 -> Option B

5. Quick Check:

   Accuracy score rounded = 0.67 [OK]

Hint: Check test size and round accuracy output [OK]

Common Mistakes:

• Ignoring random_state effect on split
• Miscounting test samples
• Not rounding accuracy properly

4. You wrote this code to train a churn prediction model but get an error:

from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train, y_train)

The error says: 'NameError: name 'X_train' is not defined'. What is the likely cause?

medium

A. y_train contains string labels instead of numbers

B. LogisticRegression() is not imported correctly

C. You forgot to split the data into training and testing sets

D. The model.fit() method is misspelled

Solution

1. Step 1: Analyze error message

   NameError means variable X_train is not defined anywhere before use.

2. Step 2: Identify missing data split

   Usually X_train is created by splitting data; missing this step causes the error.

3. Final Answer:

   You forgot to split the data into training and testing sets -> Option C

4. Quick Check:

   Undefined variable = missing data split [OK]

Hint: Check if data split code is missing [OK]

Common Mistakes:

• Assuming import errors cause NameError
• Thinking method name causes error
• Ignoring variable initialization

5. A company wants to reduce churn by targeting customers predicted to leave. They have a model with 80% accuracy but many false positives. Which strategy best improves churn prevention?

hard

A. Use a model with higher recall to catch more true churners

B. Ignore false positives and focus only on accuracy

C. Send retention offers to all customers regardless of prediction

D. Reduce the training data size to improve speed

Solution

1. Step 1: Understand false positives impact

   False positives mean non-churners get targeted, wasting resources.

2. Step 2: Choose metric to reduce false negatives

   Higher recall reduces missed churners, improving prevention.

3. Final Answer:

   Use a model with higher recall to catch more true churners -> Option A

4. Quick Check:

   Better recall reduces missed churners [OK]

Hint: Focus on recall to catch more churners [OK]

Common Mistakes:

• Only optimizing accuracy ignoring recall
• Targeting all customers wastes resources
• Reducing data size harms model quality