Oracle is sprucing up its customer data platform, CX Unity, with a little machine learning.

The platform will now support real-time behavioral data collection and personalization capabilities through Infinity, Oracle’s digital streaming technology. Infinity captures web event, app and point-of-sale data to help brands build realistic representations of the customer journey.

That previously required repeated and rigorous A/B testing, said Rob Tarkoff, EVP and general manager of Oracle Cloud CX and Oracle Data Cloud.

“But by applying machine learning to that, you can come up with predictions and insights based on a holistic set of data, and it doesn’t require you to do one-off activities,” he said.

Marketers can use ML in this context to anticipate the best possible discount to offer at the beginning of a conversation with a customer, for example, or to reduce the number of touches required for a sale.

CX Unity launched in 2018, right around the time Salesforce and Adobe started ginning up their own CDPs. The marketing clouds have gone big on CDPs after a slow start and initial skepticism about the need for CDP technology.

But Oracle differentiates itself with what Tarkoff calls “a true one-platform story” – as opposed to “the fake news we hear from some of our competitors who only like to talk about a single platform.”

Take Salesforce, he said. Einstein, the AI layer within Salesforce’s platform, is built on Amazon Web Services; ExactTarget runs on Azure; and other parts of Salesforce are built on Salesforce’s own proprietary stack. But Oracle weaves AI into its offerings consistently across platforms, including its CDP, Tarkoff said.

“Our ambition, and a big one for Larry [Ellison], is to have a single platform orientation and to automate and engineer the front office processes as much as the backend office processes,” he said.

What that means in practice is capturing and applying signals regardless of where they come from.

Field service is a good example. Last year, Oracle introduced a Service Logistics Cloud that helps service departments collaborate, share data and manage their supply chains. Through a combination of Service Logistics and CX Unity, it would be possible to send a signal to a field technician if they need to reroute their schedule because a part they need for a job isn’t available until later in the day.

“While we do all of the marketing use cases that traditional CDPs are expected to do, like campaign suppression, personalization and real-time segmentation,” Tarkoff said, “our view is that customer intelligence expands way beyond marketing.”

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Tealium had added machine learning to Audience stream, its Customer Data Platform (CDP). Called Tealium Predict ML the company says it is designed to enable marketers to orchestrate real-time customer data, for instance by identifying buyers that are most likely to make a purchase or a segment most likely to churn.

The company said in a press statement that the software was designed to pair with its AudienceStream CDP to continuously anticipate customer behaviours using machine learning to power more effective audience segmentation for better customer engagement, conversion rates, and lifetime value.

By creating custom-tailored predictions in a matter of clicks, the company says marketers gain insights into the likelihood of customer behaviours as well as full visibility into the data used to generate them. According to Tealium, it is with these insights, brands can effectively and efficiently engage and delight the right customers while improving business outcomes.

Tealium’s believes its expansion into prediction and decisioning features will help it martech offer stand out among CDP providers, particularly in an environment amidst budget reductions and heightened emphasis on customer retention, where marketers are being asked to deliver greater results with fewer resources. Tealium says it machine-learning option was built to simplify the way marketing teams to inject the value of machine learning-powered insights into their existing tech stack.

Amin Foda, marketing infrastructure director at Monash University and Tealium client said the machine-learning addition into her existing marketing stack addressed the need to move beyond historical methods and shift toward machine learning and AI to predict the optimal audiences to go after.

“Using Tealium Predict ML helps us build real-time audience attributes based on their likelihood to take action and engage with them on the next best conversation across all identifiable channels at the right moments,” Foda said.

The Tealium Predict ML general availability launch includes a new feature – Behaviour Health Rating. According to the press statement, this feature allows users to view whether they have sufficient existing data to provide healthy or unhealthy machine learning insights on specific behaviours. Furthermore, based on the health rating, users can assess which behaviours to invest time in exploring now and which behaviours need to develop more mature data before including them in future models, the statement added.

“While more organisations are realising the value of data collection and insight activation each day, they’re also realising these projects can be extremely resource-intensive without the right solutions in place,” said Mike Anderson, Tealium founder and chief technology officer. “By offering a flexible machine learning product that’s integrated right into our CDP solution, we’re taking the complexity and burden out of the data collection equation, levelling the playing field for our customers and allowing them to really focus on how to activate the value of their data.”

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