Practice Case Study 1: Predicting Customer Churn in Telecom - 18.5.1 | 18. Data Science for Business and Decision- Making | Data Science Advance
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18.5.1 - Case Study 1: Predicting Customer Churn in Telecom

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Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is customer churn?

💡 Hint: Think about the typical behavior of customers in a subscription service.

Question 2

Easy

What is a classification model used for?

💡 Hint: Consider how Netflix recommends shows based on viewing habits.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What is the purpose of a classification model in predicting churn?

  • To increase customer satisfaction
  • To categorize customers based on churn likelihood
  • To reduce marketing costs

💡 Hint: Consider what the classification process entails.

Question 2

True or False: Targeted interventions are usually less effective than generic strategies.

  • True
  • False

💡 Hint: Think about personalization vs general offers.

Solve and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Develop a comprehensive intervention strategy for a hypothetical telecom company experiencing a 25% churn rate. What data would you analyze, and what specific actions would you implement to reduce churn by 15%?

💡 Hint: Think critically about customer experiences and how to enhance satisfaction.

Question 2

Evaluate the effectiveness of using machine-learning models for customer retention strategies compared to traditional marketing methods. What advantages might machine learning offer?

💡 Hint: Consider the scalability and data analytics capabilities of machine learning.

Challenge and get performance evaluation