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Adobe Target gets personalization power boost through Sensei AI capabilities

Adobe Target gets personalization power boost through Sensei AI capabilities 

The San Jose, Calif.- company is rolling out a new feature called Auto Target, which uses Sensei – Adobe’s AI and ML platform – aims to provide users a “one-click personalization” experience. Adobe is aiming the Auto Target feature at large corporations such as retailers, technology companies and mobile carriers that rely on outward-facing Web sites which generate large online traffic.

Other updates to Adobe Target are:

Recommendations through natural language processing – Available this fall will be an out-of-the-box algorithm which uses natural language processing to predict content, offers and product preferences.

Automated one-on-one offers –  This about the possibilities of serving up dynamic offers like mortgages, credit cards and online bill pay—all based on a customer’s previous browsing paths, account status, search terms and other factors.

Adobe Analytics Cloud – Tighter integration between Adobe Target and Adobe Analytics Cloud, enables marketers to use behavioral analytics and audience data to inform deeper segmentation.

Kevin Lindsay, director of product marketing for Adobe Target, believes it’s the ideal tool for closing the “personalization gap.”

“Customers set a very high bar with regards to customization and personalization – and they also want relevant content at the right time and the right channel,” he said in a recent interview with IT in Canada.

320 Adobe Auto-Target 1

The problem is, there is no perfect experience. Every individual is different.

“What you frequently end up with is serving up the lowest common denominator,” Lindsay explained. “But what if the Web site you see on your screen is different from the one the other person see?”

What Auto Target does is use Ai and machine learning to automatically determine the best experience for each consumer and continuously optimizes those experiences over time as the consumer takes additional actions, he said.

Using machine learning, the system is able to analyze user’s online responses, clicks and other inputs which are collected over a period of time. It is then able to deliver relevant information to users at the time they need it.

“For instance, a hotel chain can feature its tropical properties and content for a reward member, knowing the individual prefers to travel to warm destinations based on bookings and mobile app engagement,” said Lindsay. “The end result is higher engagement and increased loyalty.”

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