Salesforce is today enhancing its predictive powers by announcing Predictive Journeys for its Marketing Cloud.
The company’s platform had previously offered predictive recommendations about content for users, like suggesting which graphic or white paper would best be served next to a prospect.
In Customer Journeys — Salesforce’s name for the path from prospect to regular customer — marketers had used analytics to manually make their best judgments about when and how to engage, induce and convince would-be customers.
Now, the new Predictive Scores and Predictive Audiences widen the platform’s ability to judge where a customer is in the Journey and to forecast what is needed next. Taken together, they assemble what the company calls Predictive Journeys.
In its announcement, Salesforce compared it to the difference between, on the one hand, manually following a map, and on the other, being guided dynamically on your route to account for sudden developments like a traffic jam or a closed road.
Meghann York, Director of Product Marketing, told me that the goal of the new predictiveness is better “engagement across the platform.”
Predictive Scores provides a score for each user, based on their likelihood to engage. This can reflect the platform’s assessment of the user’s likelihood to open a particular email, to unsubscribe from an email list or to make a purchase. Companies like Infer, Mintigo and 6sense specialize in scoring leads, and this new version now expands Salesforce’s implementation.
Salesforce describes Predictive Audiences as “a smart audience segmentation tool.” It uses Predictive Scores and other factors to group users by audience segments, so that specific actions can be directed toward those users in the hopes of promoting or discouraging the direction the user is heading.
So, for instance, if the Score indicates someone is ready to unsubscribe from an email list, that person is moved into the Audiences segment of similar users, all of whom might get a special email encouraging them to remain.
Segmentation, of course, is a common feature of marketing platforms, and here Salesforce is enhancing it with expanded forecasting powers in the hopes of changing customers’ and potential customers’ futures.
York noted that, previously, a marketer might have noticed users who haven’t made a purchase or opened emails in two months, and thus utilized a “a working hypothesis” that the users were likely to unsubscribe from the email list. Said users could then have been added to a segment of users on a customer path with actions designed to keep them around.
Now, she said, machine learning is employed to make those inferences and action choices for the marketer.
“Let data science do the heavy lifting,” she said.
Predictive Scores and Predictive Audiences are being released in a public beta. York noted there were no stats yet about how much of a difference they make, but she added that the previous predictive recommendations generated, on average, a 25 percent lift in engagement from users.
Predictive capabilities are everywhere in digital marketing these days. But she said Salesforce’s platform stands out because, well, the others don’t have Salesforce’s platform to build on, which includes customer service and sales, as well as marketing.
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