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Salesforce Gets Some Machine Learning

Salesforce.com today added Predictive Decisions to its marketing cloud, a predictive analytics-based tool that it promises will increase conversion rates by recommending the delivery of timely and relevant content across all marketing channels.

“We did focus groups with a bunch of our marketing customers and they told us they still are dealing with four problems,” said Gordon Evans, VP of product marketing for Salesforce Marketing Cloud. “They can’t get all their data onto a single platform; they have a hard time taking action on the data they have in a meaningful time frame; they don’t know how to extend predictive analytics-based recommendations cross-channel; and they have difficulty integrating the process with customer service and sales.”

Predictive Decisions, claims Salesforce, answers these needs with the following features:

Collect beacon: Streams content updates and behavioral data into the cloud platform to constantly update user profiles and enable the making of predictive decisions. Company literature says that this enables marketers to move beyond websites in feeding time content, extending their reach to such areas as whitepapers and videos.

Workflow and automation: Calls on data from all channels to trigger relevant communications in real time.

Native predictive decisions: An improved user interface enables marketers to predict what offer, product, or graphic element, for instance, might increase conversion in drag-and-drop mode. Salesforce uses the example of a retailer, in a few clicks, being able to recommend the next most relevant piece of content to trigger a customer purchase.

“What had previously been manual processes, we’ve automated to drag and drop,” Evans said.

Salesforce’s marketing manager in charge of predictive intelligence, Meghann York-Meenan, says the guiding mission behind the launch of Predictive Decisions was finding a way to make complex predictive analytics technology more accessible. “I think the real story is that the channel marketer did not have real-time access to the data they needed to truly know their customers as individuals,” she commented.

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