Salesforce Consultant Changed Tableau-CRM-and-Einstein-Discovery-Consultant Questions

To improve dashboard performance in the face of growing datasets, the Einstein Analytics consultant can take the following actions:

• B: Distribute steps among separate pages: This reduces the load on any single page, enhancing performance by only loading data necessary for the view being accessed.
• D: Replace separate step filters with a global filter: This reduces the number of queries and processing needed as the global filter applies universally, streamlining data handling.
• E: Move calculations to a dataflow: Offloading calculations to a dataflow minimizes the processing required at runtime, which can significantly improve dashboard performance.

The Service Analytics Overview dashboard in Tableau CRM is designed to provide comprehensive insights into various aspects of service operations. It includes:

• Key metrics such as open cases, average time to close, first contact resolution, and customer satisfaction, offering a quick overview of service efficiency.
• Drill-down capabilities into more detailed dashboards like agent performance, channel review, and telephony metrics, enabling deeper analysis.
• Availability on both desktop and mobile platforms, ensuring accessibility across different devices.
• It serves as an excellent starting point for service analytics, providing a holistic view of the service department’s performance.

This dashboard is a central tool for managing and analyzing service operations effectively, helping organizations to optimize their customer service strategies. Reference: &type=5

In Einstein Discovery, it is not necessary to remove all potential collinear fields before executing the build story because:

• B: No. Although it is ideal to eliminate collinearity as soon as possible, Einstein will give a warning post-build and the ridge regression will prevent collinearity from over-fitting. Einstein Discovery includes mechanisms such as ridge regression to manage collinearity, which helps prevent the model from over-fitting even when collinear variables are present.