The CRM Analytics consultant at Universal Containers has set data syncs and recipe runs back to back. However, they notice that the data syncs and recipe run jobs fail repeatedly. Upon investigation,
they realize the data syncs and recipes are tightly coupled which leads to too many runs being queued and eventually being canceled.
How should the consultant resolve this issue?
The CRM Analytics consultant at Cloud Kicks is asked to a dashboard displaying Opportunities data on the account's record page. The dashboard should display only opportunity data related to the current account viewed.
How should the consultant accomplish this?
Universal Containers' CRM Analytics team is building a dashboard with two widgets, and the queries use different datasets.
1. List widget associated to the query "Type_2" and grouped by the dimension "Type" (multi-selection)
2. Donut chart widget associated to the query "Query_pie_3" and grouped by the dimension "Type"
The team wants any selection in the List widget to filter the Donut chart and vice vers
a. Users should be able to choose more than one Type (multi-selection).
What is the recommended way to accomplish the required filtering?
Cloud Kicks (CK) wants to use CRM Analytics to analyze trends of its sales pipeline in order to accelerate the company's sales process. To do so, CK needs to know the average time an opportunity
spends in each stage. The data can be found in the Opportunity History object, but the value is not pre-calculated in Salesforce, so a consultant recommends using a recipe to calculate it.
How should the consultant use a recipe to calculate the average time an opportunity spends in each stage?
A CRM Analytics consultant is reviewing results from an Einstein Discovery story with a business user. They agree with the findings but notice that none of the fields used in the story have a correlation value greater than 4%. The client is now concerned that the model
may not be good enough to deploy.
Which action should the consultant take?
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