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Salesforce Widens Predictive Analytics For Marketing Cloud

In addition to content recommendations for its brands’ audiences, the CRM provider can now anticipate omnichannel engagement opportunities.

Salesforce's Meghann York
Salesforce’s Meghann York

The introduction of Salesforce Marketing Cloud Predictive Journeys sees the customer relationship management giant offering its retail and other clients ways of forecasting the kinds of products and messages shoppers are more likely to respond to and engage with.

While predictive analytics tools are increasingly being used to connect online-to-offline retail marketing services, Salesforce previously relegated such insights for helping businesses program content recommendations, noted Meghann York, director of Product Marketing for the Salesforce Marketing Cloud, the San Francisco company’s primary software platform.

The new functions, including “Predictive Scores” and “Predictive Audiences,” are part of the Marketing Cloud Predictive Journeys initiative, which allows Salesforce’s clients to fuse CRM and marketing analytics with contextual data, such as web browsing activity and email engagement.

What Predictive Scores and Predictive Audiences expands on is the ability for brands to analyze customer engagement and predict the best next step in along the purchase path, York said.

“The Marketing Cloud has been focusing on the Customer Journey for two years,” York said. “And we’ve focused the whole marketing and product strategy to cover the whole journey, as opposed to focusing on ‘siloed’ interactions, in terms what brings shoppers and marketers together. By expanding our predictive tools from content to encompassing a whole range of audience behaviors, we’ve taken a big next step.”

The tools are being rolled out just at the beginning of the holiday shopping season and are meant to give businesses the chance to match the speed at which customers demand information and interaction with brands.

The broader build-out of predictive analytics is also intended to reflect the varying levels and times that consumers are most open to receive certain kinds of information about products and services. In other words, while some marketers might want to focus on, say, timing their email offers to get a shopper into a store at just the right moment, others will want to know when is the best period to reach someone during a person’s research and product discovery phase.

For example, York mentioned the flash sales shopping portal Rue La La as a client with some issues balancing email signups and subsequent follow-through by consumers.

“Rue La La found that they would be able to get a lot of people to sign up for their emails, but many just make one purchase and activity would drop off,” York said. “That’s a challenge for many brands and retailers: How do you take someone from being a one time customer to a lifetime customer? These new predictive analysis tools are designed to help marketers do that.”

Just 36 percent of “customer experience leaders” say that their companies always track what happens during customer interactions, York said, citing Forrester numbers.

“While there are customer experience tools in existence today, they are a bit like looking at a map at home before leaving for a trip,” she added. “At one time, having a map was the best way to navigate, but the static data can’t deliver the optimal route. It can’t adapt to events that happen along the journey, like traffic jams or a road closure, to bring a person to their destination in the best way possible.”

The predictive analytics program is expected to remain in beta through the first half of 2016, York said, and it will ultimately be able to be purchased in specific additions within the Marketing Cloud package.

About The Author
David Kaplan David Kaplan @davidakaplan

A New York City-based journalist for over 20 years, David Kaplan is managing editor of GeoMarketing.com. A former editor and reporter at AdExchanger, paidContent, Adweek and MediaPost.