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Answer: Develop a predictive model to forecast potential customer churn based on patterns in feedback, complemented by a time series plot to visualize trends and identify critical periods requiring intervention.
Option B is the most comprehensive solution as it leverages predictive analytics to forecast customer churn, providing a forward-looking insight that is crucial for preemptive action. The use of a time series plot further enhances this by allowing the tracking of trends over time, making it easier to identify when interventions might be most needed. While options A, C, and D offer valuable insights, they primarily focus on descriptive or diagnostic analytics, which, although useful, do not fully meet the requirement of integrating both prescriptive and predictive analytics to suggest actionable future strategies.
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As a Microsoft Fabric Analytics Engineer Associate, you are tasked with integrating prescriptive and predictive analytics into a visual report to analyze a dataset of customer feedback. The solution must not only predict future trends but also suggest actionable insights to improve customer satisfaction. Given the constraints of cost-effectiveness and scalability, which of the following approaches would BEST meet these requirements? (Choose one option)
A
Implement sentiment analysis to categorize feedback into positive, negative, or neutral sentiments, and display the distribution using a pie chart to quickly identify overall customer sentiment trends.
B
Develop a predictive model to forecast potential customer churn based on patterns in feedback, complemented by a time series plot to visualize trends and identify critical periods requiring intervention.
C
Conduct a root cause analysis to pinpoint specific issues leading to negative feedback, using a bar chart to highlight the most common complaints for immediate action.
D
Apply clustering techniques to group customers with similar feedback profiles, visualizing these segments in a scatter plot to tailor personalized engagement strategies.