Practice Case Study 1: Predicting Customer Churn in Telecom - 18.5.1 | 18. Data Science for Business and Decision- Making | Data Science Advance
K12 Students

Academics

AI-Powered learning for Grades 8–12, aligned with major Indian and international curricula.

Academics

Grades

Grade 8 Grade 9 Grade 10 Grade 11 Grade 12

Curriculum

CBSE ICSE IB
Professionals

Professional Courses

Industry-relevant training in Business, Technology, and Design to help professionals and graduates upskill for real-world careers.

Professional Courses
Games

Interactive Games

Fun, engaging games to boost memory, math fluency, typing speed, and English skills—perfect for learners of all ages.

games
Typing
Memory
Math
English Adventures
Knowledge

18.5.1 - Case Study 1: Predicting Customer Churn in Telecom

Enroll to start learning

You’ve not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take mock test.

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