Practice Case Study 1: Predicting Customer Churn In Telecom (18.5.1) - Data Science for Business and Decision- Making

Practice: Case Study 1: Predicting Customer Churn in Telecom

Lesson 18.5.1Data Science for Business and Decision- Making

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8 questions

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6 sample questions

Question 1.
What is customer churn?
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Think about the typical behavior of customers in a subscription service.
Question 2.
What is a classification model used for?
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Consider how Netflix recommends shows based on viewing habits.
3Question 3.
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
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Consider what the classification process entails.
4Question 4.
True or False: Targeted interventions are usually less effective than generic strategies.
- True
- False
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Think about personalization vs general offers.
Question 5.
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%?
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Think critically about customer experiences and how to enhance satisfaction.
Question 6.
Evaluate the effectiveness of using machine-learning models for customer retention strategies compared to traditional marketing methods. What advantages might machine learning offer?
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Consider the scalability and data analytics capabilities of machine learning.

4 more questions in this section. Enroll free to practise them all.