'CRM Analytics' Salesforce's new name for Tableau CRM

Salesforce has renamed Tableau CRM "Salesforce CRM Analytics," and along the way added new features and dashboards. Coming later this year will be vertical-specific integrations, Slack apps and a search analytics tool.

Features coming in the Summer '22 release, which will roll out to various Salesforce users in waves in June, include 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 with natural language queries.

Salesforce CRM Analytics started out life as Einstein Analytics, which was renamed Tableau CRM in late 2020.

The renaming follows in quick succession with other products, including Salesforce Customer Data Platform. It follows precedents where other products have been renamed to reflect product functions, instead of branding of acquired companies such as Tableau or Datorama.

The rebranding makes it clear to users that Salesforce CRM Analytics are natively built for them, the company said in an email attributed to Umair Rauf, Salesforce product management vice president.

Gartner calls such prebuilt, purpose-defined machine learning analytics embedded into applications -- as opposed to off-the-shelf data modeling packages that require far more customization and code to set up and use -- "augmented analytics." Features such as Salesforce CRM Analytics give end users the keys to machine learning with low-code or no-code tools, without involving data scientists, said Gartner analyst Austin Kronz.

Such a targeted approach to machine learning can also yield usable business insights that can take a very long time to extract from a traditional machine collaboration among an organization's data experts in a Jupyter Notebook, for example, Kranz said. Those projects can take a lot of iterations to get to insights that help meet business goals.

"You can apply machine learning to anything -- the problem is, just because it's statistically relevant doesn't mean it's business relevant," Kronz said. "Einstein was meant to make machine learning accessible to consumers; I think that's where they've seen their success."