A new predictive marketing service from Salesforce will provide contextual, real-time data on customer behavior and engagement, such as from clickstreams.
Global CRM company Salesforce has announced the launch of its latest predictive marketing product, dubbed Salesforce Marketing Cloud Predictive Journeys. The new offering, announced in a Nov 18 release, will allow marketers to access data from web streams and other real-time sources to get valuable insights on customer behavior including email engagement and web browsing activity. It also offers two new data products:
Predictive Scores: This service uses data analytics to gauge the likelihood of a customer to engage with a particular marketing campaign—for example, open an email, make a purchase, or take advantage of a web promotion. It also provides a dashboard that aggregates customers into audience segments with their individual engagement scores.
Predictive Audiences: Gives marketers the ability to unify customer data and create highly targeted segments that allow for more effective campaigns for engagement or re-engagement.
Both services are in beta and expected to become available in Q1 of 2016.
“Rearview analytics — understanding how campaigns performed, how customer segments responded — is not sufficient to drive decisions that affect in-the-moment customer interactions,” wrote Rusty Warner, principal analyst, Forrester Research in the October 2015 report, “Combine Systems of Insight and Engagement for Contextual Marketing.”
RTInsights Take: The combination of data from customer-relationship management systems—such as past order amounts, types, time since last transaction, and purchases in certain categories—with real-time data from clickstreams and shopping cart activity can make for powerful predictive applications. One application is the ability to push real-time incentives (price discounts, free shipping) targeted at certain customers. Another application is to present the customer with a “next best offer” in a certain product category. Such applications, however, at least require a data processing architecture that can process real-time transactions, integrate it with a CRM, and advanced predictive analytics algorithms that can tie various data points together.
Read more: real-time marketing archives.
Check out our most-read content:
Frontiers in Artificial Intelligence for the IoT: White Paper
Real-Time Retail: Why Uniqlo Employees Use Handhelds
Value of Real-Time Data Is Blowing in the Wind
IoT Architectures for Edge Analytics
Liked this article? Share it with your colleagues!
