You are building a Power BI report to analyze customer segments.
You need to identify customer segments dynamically based on the Bounce Rate across dimensions such as source, geography, and demographics. The solution must minimize analysis effort.
Which type of visualization should you use?
To identify customer segments dynamically based on the Bounce Rate across multiple dimensions such as source, geography, and demographics, while minimizing analysis effort, the 'key influencers' visualization is the most suitable option. The key influencers visual helps to automatically identify and display the most significant factors impacting a specific measure, in this case, the Bounce Rate. It simplifies the analysis process by dynamically showing how different dimensions influence the Bounce Rate, thereby requiring minimal manual effort and enabling quick insights into customer segments.
Answer is A The decomposition tree visual in Power BI lets you visualize data across multiple dimensions. It automatically aggregates data and enables drilling down into your dimensions in any order. The key influencers visual helps you understand the factors that drive a metric you're interested in. It analyzes your data, ranks the factors that matter, and displays them as key influencers. *Key influencers does not visualize data across dimensions*
agreed
If we need to minimize the analysis effort then key influencers is the answer (Explain Bounce Rate by dimensions listed). Decomposition tree requires more effort; as per microsoft: "This tool is valuable for ad hoc exploration and conducting root cause analysis."
Questions like this is one reason why you may never see someone scoring 1000 in the exam
YOU ARE SO RIGHT!! These reading tests are so tiring...
The question asks" Dynamically"... so, decomposition tree is more suitable.
The answer is decomposition tree. The key difference between the Decomposition Tree and Key Influencers visualization is in the approach to analysis. While the Key Influencers visualization focuses on identifying the key factors that are influencing a metric, the Decomposition Tree allows you to dynamically analyze and explore multiple dimensions such as source, geography, and demographics to identify customer segments based on Bounce Rate. The Decomposition Tree allows you to drill down into the data and see how different dimensions are contributing to the Bounce Rate. This makes it easier to identify patterns and customer segments based on the selected dimensions. So, while Key Influencers can help identify segments that are contributing to a metric, the Decomposition Tree is better suited for dynamically analyzing and exploring multiple dimensions to identify customer segments based on Bounce Rate.
The Key Influencers visual also has the option to show top segments. These will show which selections are made in the elements of the different attributes in the analyze by well.
for multimple dimessions
Analyze dynamically with minimal analysis effort => Key Influencers
This question was on the exam today 9/12/2023
I vote for Key Influencers. This is strong powerful and use minimum effort.
ChatGPT: Key Influencers (option D) is a powerful visualization that helps identify patterns and factors that influence a specific measure. In this case, it will allow you to understand which dimensions (source, geography, demographics) have the most significant impact on the Bounce Rate. By analyzing key influencers, you can dynamically identify customer segments that have higher or lower Bounce Rates based on various dimensions. Using the Key Influencers visualization will minimize the analysis effort, as it will automatically identify the most influential dimensions impacting the Bounce Rate without the need for complex manual analysis. This will help you quickly pinpoint customer segments that require attention or further investigation.
Because of the term "dynamically", I choose A - Decomposition Tree here. But I am not 100% sure.
A is the right answer
A - Decomposition Tree
https://learn.microsoft.com/en-us/power-bi/visuals/power-bi-visualization-types-for-reports-and-q-and-a As said on website under Decomposition tree : "The decomposition tree visual lets you visualize data across multiple dimensions."
A decomposition tree would be the most suitable visualization for this scenario. It allows you to dynamically segment and analyze the data based on different dimensions, such as source, geography, and demographics, while also providing insights into the factors that contribute to high bounce rates. With a decomposition tree, you can quickly identify the segments with the highest bounce rates and drill down into the underlying factors to understand the root cause of the issue, without requiring significant analysis effort. No confusion, and no need to discuss further
makes sense