What should an analyst focus on when designing a model in SAP Analytics Cloud to analyze customer behavior?

What should an analyst focus on when designing a model in SAP Analytics Cloud to analyze customer behavior?

• A. Defining hierarchies, setting up data blending, and creating calculated dimensions
• B. Utilizing data actions, performing anomaly detection, and establishing linked models
• C. Implementing data enrichment, constructing time series analysis, and applying data privacy rules
• D. Establishing cross-model security and utilizing regression analysis

Answer: (A)

When designing a model in SAP Analytics Cloud to analyze customer behavior, focusing on defining hierarchies, setting up data blending, and creating calculated dimensions is crucial for capturing the multi-faceted aspects of customer interactions and behaviors. Defining hierarchies allows analysts to structure data in a way that reflects the relationships and categories relevant to customers, such as geographic locations, customer segments, or product categories. This hierarchical organization aids in more meaningful visualizations and deeper insights into customer patterns. Setting up data blending is essential for integrating data from various sources, which enhances the analysis by providing a comprehensive view of customer behavior. For instance, blending transactional data with demographic information provides a richer context for understanding customer actions and preferences. Creating calculated dimensions enables analysts to derive new insights from existing data by adding metrics or classifications that are not included in the raw data. This can lead to the generation of key performance indicators or custom segments that are vital for a more granular analysis of customer behavior. While the other options contain valuable techniques and tools, they focus on different aspects of analytics that may not be foundational in the initial model design phase for customer behavior. For instance, utilizing data actions and performing anomaly detection are essential for operational analysis but are secondary to the foundational layout of customer-centric data

When designing a model in SAP Analytics Cloud to analyze customer behavior, focusing on defining hierarchies, setting up data blending, and creating calculated dimensions is crucial for capturing the multi-faceted aspects of customer interactions and behaviors.

Defining hierarchies allows analysts to structure data in a way that reflects the relationships and categories relevant to customers, such as geographic locations, customer segments, or product categories. This hierarchical organization aids in more meaningful visualizations and deeper insights into customer patterns.

Setting up data blending is essential for integrating data from various sources, which enhances the analysis by providing a comprehensive view of customer behavior. For instance, blending transactional data with demographic information provides a richer context for understanding customer actions and preferences.

Creating calculated dimensions enables analysts to derive new insights from existing data by adding metrics or classifications that are not included in the raw data. This can lead to the generation of key performance indicators or custom segments that are vital for a more granular analysis of customer behavior.

While the other options contain valuable techniques and tools, they focus on different aspects of analytics that may not be foundational in the initial model design phase for customer behavior. For instance, utilizing data actions and performing anomaly detection are essential for operational analysis but are secondary to the foundational layout of customer-centric data