GCP Cloud Digital Leader Practice Question

Your company already uses Looker dashboards to summarize last quarter's sales. Marketing now wants to proactively identify which current customers are likely to churn so they can send retention offers. Why does this requirement call for machine learning instead of traditional business intelligence?

Machine learning is primarily used to visualize trends, while business intelligence is designed to forecast future customer behavior.
Machine learning eliminates the need for any historical data, whereas business intelligence depends on past records.
Machine learning can learn patterns from historical churn data to generate individual predictions, whereas business intelligence mainly summarizes past events without creating predictive models.
Machine learning dashboards refresh data in real time, while business intelligence tools update on fixed schedules.

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Answer Description

Business intelligence and data analytics mainly provide descriptive or diagnostic insights about what has happened or is happening, often through reports and visualizations. Predicting a future outcome-such as the probability that an individual customer will churn-requires a model that can learn patterns from historical examples and generalize them to new, unseen cases. Machine learning algorithms supply this predictive capability by training on labeled data (customers who did or did not churn) and outputting probabilistic predictions, something dashboards and standard BI queries cannot do on their own. Faster refresh rates, the absence of historical data, or greater emphasis on visualization do not explain the fundamental difference between BI and ML.

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