Cloud Kicks needs a CRM Analytics consultant to install the Appointment Analytics App. After installation, they realize the wrong field was picked and the app did not have access to a newly created field that should be used instead of the old one.
What is the first step the consultant should take to prevent erroneous dataset/dashboard creation?
If the wrong field is selected in the initial setup of an app or dataset, it is important to stop any data processing activities (like recipe executions) to prevent erroneous data from being loaded into datasets and dashboards. In this case, stopping the recipe from running in Data Manager is the correct first step. Once the recipe is stopped, the consultant can update the field selection or make other necessary corrections before restarting the process.
The CRM Analytics consultant at AW Computing is designing dashboard. They want to add a rate between two metrics and display it in a new chart. They are finding that the new chart seems to prejudice the design of other charts.
What should the consultant do to resolve this?
Universal Containers has a dashboard for sales managers that want to visualize their win rate.
Which chart type should the consultant use to keep track of targets?
A company wants to create a timeline chart to visualize the evolution of its Closed Won opportunities.
What are the required parameters to build a lens that displays output similar to the image shown?
To create a timeline chart similar to the one shown, the following parameters are typically required:
1 Measure: This could be the count of Closed Won opportunities or any other relevant metric that needs to be tracked over time.
1 Grouping by a Date Field: This is essential to plot the timeline effectively. The date field would typically be the close date of the opportunities.
Additional Groupings: Depending on the complexity and the detail needed, additional groupings can be added. For example, grouping by region or product line can provide more insights into the timeline. If trellis is used, it allows for the creation of multiple smaller charts within the main chart, each representing a slice of data based on the additional groupings.
This setup helps visualize the evolution of Closed Won opportunities over time, making it easy to spot trends, seasonal patterns, or other relevant insights.
Universal Containers (UC) is looking to create a dashboard for whitespace analysis. UC wants to view a particular customer and see what similar customers have bought.
Which recipe transformation is helpful for the consultant to use while creating the dataset?
