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Answer: Apply association rule mining to identify frequently co-occurring feedback themes, such as 'poor Wi-Fi' and 'slow check-in process', to uncover hidden relationships between different service aspects.
Option D is the most appropriate answer because association rule mining is a powerful technique for uncovering hidden relationships between different items or themes in a dataset. By identifying frequently co-occurring feedback themes, such as 'poor Wi-Fi' and 'slow check-in process', the hotel chain can gain insights into underlying issues that may be affecting customer satisfaction in a more interconnected way than what is revealed by average sentiment scores or cohort analysis alone. This method allows for a more comprehensive exploratory analysis that can inform targeted and strategic improvements to hotel operations and services.
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As a Microsoft Fabric Analytics Engineer Associate, you are tasked with performing exploratory analytics on a dataset of customer feedback for a hotel chain to uncover insights that could improve customer satisfaction. The dataset includes feedback on various aspects such as room cleanliness, customer service, and amenities. You need to choose the best approach that not only identifies key issues but also reveals underlying patterns that could inform strategic improvements. Which of the following methods would you choose to achieve this goal? (Choose one correct answer)
A
Calculate the average sentiment score for each feedback category, such as room cleanliness or customer service, and visualize the results using a bar chart to highlight areas with the lowest scores.
B
Perform a cohort analysis to compare feedback patterns across different customer segments, such as business travelers or families, to identify if certain groups are more dissatisfied than others.
C
Use sequence analysis to identify common complaint patterns over time, such as issues reported during check-in leading to dissatisfaction with room quality, and visualize these patterns using a flow diagram.
D
Apply association rule mining to identify frequently co-occurring feedback themes, such as 'poor Wi-Fi' and 'slow check-in process', to uncover hidden relationships between different service aspects.