Consumers want more and more personalization in their products and services, and Adobe Systems Inc. is trying to meet those demands with its latest announcement.

The company has unveiled new capabilities for its cloud-based content and marketing solution Adobe Target, powered by Adobe Sensei, its machine learning and artificial intelligence platform.

The new capabilities will allow marketers to leverage Sensei’s machine learning abilities, giving them “the tools to optimize personalized experienced with one click, enhance customer recommendations and targeting precision, and automate the delivery of personalized offers,” Adobe says in a July 27 press release.

The foremost feature is Auto-Target, which automatically determines the best experience for each consumer and continuously optimizes those experiences over time by learning what does and does not resonate with them, the company explains.

Adobe quotes one beta tester in the financial services industry as saying, “Auto-Target allows us to do personalized A-B-C-D page level testing algorithmically. The advantage that we see over traditional testing is that the machine will take all variables and traits about a visitor into consideration – not just the traits and segments that we deem important.”

For example, this would allow a hotel chain to focus on advertising its tropical properties to a reward member based on the individual’s previous warm destinations bookings and mobile app engagement.

“This ensures customers receive only the most relevant experiences. As such, brands can go even further with their personalization efforts without having to worry or feel like they’re taking risks with their valuable traffic. Thanks to the backup policy feature, marketers can always be assured that no variation will do worse than their best control. The result is that the best experiences will only get better,” it adds.

Auto-Target is available today to all Adobe Target Premium users.

Harnessing the power of recommendations

Adobe Target will also be gaining an upgraded product recommendation feature based on natural language processing. Every digital interaction a consumer has with a brand is part of an ongoing dialogue, with each action a query or expression of intent, Adobe Sensei can interpret these queries and respond in a way that’s relevant to the consumer based on what it’s learned about them throughout the dialogue.

“An industry-first application, this technology uncovers the underlying intent of consumers’ behavior to better predict what content and products customers might want next,” the company explains. “For example, a retailer can see that a customer watched its video on eco-friendly laundry techniques and purchased compostable dryer sheets. It can then provide a tailored recommendation about eco-friendly detergent based on what was inferred from the customer’s previous actions. Previously, the algorithm would have offered up a laundry detergent recommendation based on detergents other people viewed.”

The new recommendation technology will be available in beta this Fall.

Automated delivery and more precise targeting

Adobe has also enhanced the decision power of Target so that marketers can automatically determine the right offer or advertisement – out of potentially hundreds of other offers – to ensure they are shown at the right moment for the right individual.

“For example, a financial services company uses Adobe Target’s self-learning models to serve up dynamic offers like mortgages, credit cards and online bill pay – all based on each individual’s previous browsing paths, account status, search terms and other factors,” the company says. “A new homeowner who has recently secured his first mortgage will have a very different online experience than a woman approaching retirement.”

And last but not least, Adobe has also unveiled better integration between Target and its Analytic Cloud so marketers can use behavioural analytics and audience data towards more precise targeting. With the new Experience Versions capability, marketers can target specific content areas as well. For example, a global cookware company would be able to develop targeted offers on its website based on the number of people who have bought a cast iron skillet in the last five months that would automatically update with correct languages or currency depending on the customer’s location.

“Our goal has always been to help our customers deliver incredibly spot-on experiences for their customers. And now, with these exciting advancements, we’re able to do even more, and help them move that personalization needle further than ever thought possible,” Adobe concludes.

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