A marketing manager at a subscription-based streaming service asks a data analyst to identify which current customers are most likely to cancel their subscriptions within the next 60 days. The analyst has access to a dataset containing each customer's viewing history, subscription tenure, and past support interactions, along with information on which similar customers have canceled in the past. The manager's goal is to proactively target these at-risk customers with a special retention offer. Which statistical method is MOST appropriate for fulfilling the manager's primary request?
The correct answer is Predictive analysis. The scenario requires forecasting a future event, specifically, which customers are likely to cancel their subscriptions. Predictive analytics uses historical data to find patterns and make predictions about future outcomes.
Descriptive analytics is incorrect because it focuses on summarizing past data to understand what has already happened (e.g., calculating the average subscription length of customers who have already churned). It does not forecast future events.
Prescriptive analytics is incorrect because it recommends actions to take. While the ultimate goal is to act (the retention offer), the analyst's primary and immediate task is to identify the at-risk customers (prediction), which is a necessary step before a specific action can be prescribed.
Inferential analytics is incorrect because it involves using a data sample to make generalizations or test hypotheses about a larger population (e.g., testing if there is a statistically significant difference in churn rates between two customer segments). The request is to create a model to score individual customers, not to test a general population hypothesis.
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How does Predictive Analytics determine which customers are likely to cancel their subscriptions?
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How is Predictive Analytics different from Descriptive and Prescriptive Analytics?
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What is the role of similar customer data in Predictive Analytics?
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