In analyzing customer behavior patterns with SAP Analytics Cloud, what design strategies should an analyst adopt?

The correct answer focuses on several key design strategies that are crucial for effective analysis of customer behavior patterns. Defining hierarchies allows analysts to structure and categorize data in a way that reflects the natural relationships between different dimensions, which is important for deepening insights into customer behavior. Hierarchies enable users to drill down into data, facilitating better understanding of trends and patterns at various levels of granularity.

Setting up data blending involves combining data from multiple sources into a unified view, which is essential when analyzing customer behavior, as it provides a comprehensive perspective by integrating diverse data points. This can lead to more meaningful insights and the ability to identify correlations across different datasets.

Creating calculated dimensions further enriches the analysis by allowing analysts to derive new insights from existing data. Calculated dimensions enable the modification or creation of new attributes based on existing metrics, enhancing the ability to segment and analyze customer behaviors more effectively.

These strategies collectively contribute to a more in-depth analysis of customer behavior by providing structured, integrated, and enriched data, which is critical for identifying trends and making data-driven decisions.