Tableau CRM

Salesforce’s new term for Tableau CRM is CRM Analytics

Tableau CRM has been rebranded “Salesforce CRM Analytics” by Salesforce, which has also incorporated new functionality and dashboards.

Vertical connectors, Slack apps, and a search analytics tool will be available later this year.

CRM-native tools such as a Slack integration that makes it simple to share, discuss, and make decisions based on analytics visualizations; Predictions in Slack, which aggregates machine learning sales predictions from Salesforce Reports in Slack; and Search Insights, which makes dashboards and data sets potentially discoverable to a wider range of Salesforce users, are among the features coming in the Summer ’22 release, which will roll out to various Salesforce users in waves in June.

Einstein Analytics was the original name for Salesforce CRM Analytics, which was rebranded by Tableau CRM in late 2020.

Other products, such as the Salesforce Customer Data Platform, have been renamed in fast succession. It follows in the footsteps of previous products that have been rebranded to reflect product functions rather than acquired firms’ branding, such as Tableau or Datorama.

According to a business email claimed by Umair Rauf, Salesforce product management vice president, the rebranding makes it apparent to users that Salesforce CRM Analytics are natively created for them.

“Augmented analytics,” according to Gartner, refers to prebuilt, purpose-defined machine learning analytics embedded inside applications, as opposed to off-the-shelf data modeling packages, which require significantly more modification and coding to set up and utilize. According to Gartner researcher Austin Kronz, features like Salesforce CRM Analytics give end-users the keys to machine learning using low-code or no-code capabilities without involving data scientists.

According to Kranz, a tailored approach to machine learning might generate usable business insights that would otherwise take a long time to extract from a traditional machine collaboration among an organization’s data experts in a Jupyter Notebook. Getting to insights that help accomplish company goals can require a lot of iterations on those projects.

“You can apply machine learning to anything,” Kronz explained. “The trouble is, just because something is statistically relevant doesn’t mean it’s business relevant.” “Einstein was supposed to make machine learning accessible to the general public; I believe that’s where they’ve had success.”




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