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Answer: Combine clustering algorithms, association rule mining, and predictive analytics to segment customers, uncover feature relationships, and predict churn, providing a holistic view of customer usage patterns and opportunities for revenue growth.
The most comprehensive approach involves combining clustering algorithms, association rule mining, and predictive analytics. This combination allows for the segmentation of customers into groups with similar usage patterns, the discovery of relationships between service features and customer attributes, and the prediction of customer churn. Together, these techniques provide a holistic analysis of customer usage patterns, enabling the telecommunications company to identify upsell and cross-sell opportunities, personalize service offerings, and implement targeted retention strategies effectively.
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As a Microsoft Fabric Analytics Engineer Associate working on a data analytics project for a telecommunications company, your goal is to analyze customer usage patterns to identify potential upsell and cross-sell opportunities. The company provides you with a large dataset containing detailed customer usage data, such as call duration, data usage, and the service features each customer uses. Given the need to comply with data privacy regulations and the requirement to scale the solution to accommodate growing data volumes, which of the following techniques would be the MOST comprehensive approach to analyze this dataset and uncover actionable insights into customer usage patterns? (Choose one option)
A
Implement clustering algorithms to segment customers based on their usage patterns, enabling the identification of distinct customer groups with similar behaviors for targeted marketing strategies.
B
Apply association rule mining to discover relationships between the service features used by customers and their demographic or behavioral attributes, facilitating personalized service recommendations.
C
Utilize predictive analytics to forecast customer churn, identifying at-risk customers for targeted retention campaigns, thereby reducing potential revenue loss.
D
Combine clustering algorithms, association rule mining, and predictive analytics to segment customers, uncover feature relationships, and predict churn, providing a holistic view of customer usage patterns and opportunities for revenue growth.