The new AI frontier: Hyperpersonalized automated advertising

Hyperpersonalized content

Advertisers know ads are most successful when they promote the right product at the right time to the right customer. However, getting the combination of product, timing, customer and channel correct is incredibly difficult. Advertisers have long sought after the goal of hyperpersonalization, where individual promotions can be tailored and targeted to individual people at the right time, in the right format and through the right channel that will meet an immediate need and result in a greater chance of conversion.

With traditional approaches to advertising -- print ads, TV commercials or billboards -- it's impossible to create highly personalized ads, as the audience is bound to be relatively diverse. The advent of IoT and mobile technology, combined with big data gleaned from customer interactions, gives advertisers the opportunity to target their customers through incredibly personal and timely information about customer behaviors. Through the combination of big data and AI-powered predictive analytics, advertisers are now able to get a comprehensive profile of target customers to better understand their comprehensive buying behaviors and preferred channels of interaction.

AI monetizes the concept of hyperpersonalization. Using the aforementioned customer profile data alongside machine learning models trained on individual audience member's behavior, AI technologies can create AI-enabled automated advertisements. With this robust profile data, advertisers are able to better understand the needs and wants of individual consumers, enabling them to get a more complete view of the customer or prospect.

Recommendation engines

Amazon and Netflix helped to pioneer and popularize personalized recommendation systems, where relevant products or services are recommended to a user within the context of their experience to maximize the chances of additional sales. Users are given recommendations for items or shows based on previous watching or purchasing behavior, creating a more custom experience.

Powered by big data and customer behavior patterns and enhanced by AI-powered machine learning technologies, recommendation engines have taken off. AI-powered recommendation systems track user behaviors -- both what is interacted with and what gets limited engagement. These intelligent systems are learning what an individual customer is interested in but also finding what other customers similar to them are interested in, in order to map a similar trajectory of interest. When machine learning-enabled recommendation systems find groupings of customers that share similar behaviors, marketers can stop manually creating segmentation buckets of crudely lumped customers and focus on effective automated advertising that doesn't interfere with the customer experience of a platform.

Product placement

AI assists brands with advertising online efficiently at scale. Building upon the robust customer profile data, as well as patterns learned from general interactions in different media channels, AI systems are able to determine the most relevant ad to place in a particular channel based on recent behavior. The concept of retargeting -- where a search for something in one site or channel results in a placement of an ad in a completely different website or mobile experience -- has gained traction. For example, if you're planning a camping trip and recently searched multiple times for "camping tents," then ads featuring tents or similar products will start to appear. This is empowering advertisers to target specific needs in a time-sensitive manner.

To automate things further, AI technologies are now able to automatically create editorial content or stitch together the right image with the right messaging for display ads. This automated advertising saves companies a lot of time creating ads, freeing up employees to focus their time on high-value activities, such as planning more sophisticated and customized campaigns.

In addition to better ad placement, programmatic ad buying has changed the face of online advertising. Software and machines are now automatically buying and placing ads in various channels instead of humans manually performing that task. This cuts down buying time and reduces costs. This approach to ad procurement has become so popular that, by 2020, eMarketer predicts almost 90% of all mobile display ads will transact programmatically.

Conversational interfaces

An area of advertising that's still largely unexplored is advertising through voice assistants. Between mobile phones containing assistants, such as Apple's Siri, and devices like Google Home or Amazon Alexa, ads using AI-powered voice assistants could be exceptionally effective. The market still needs to work out best practices of voice advertisement and trust with consumers. Brands will need to make sure paid advertisements don't disrupt the overall user experience. However, just like with digital ads, voice ads can utilize user profiles coupled with AI technologies to deliver relevant, timely, valuable ads.

Future concerns

The combination of hyperpersonalization with programmatic ad buying and automated advertising content creation is creating a world where advertising will increasingly follow and be crafted for consumers based on what they're doing. This is both empowering and somewhat concerning for users that are starting to push back on what online platforms are doing with their data. People are already concerned with how much information the online and mobile platform vendors have and the safety and use of data. The increasing use of AI will exacerbate those concerns as people see the results of their online activity reflected in promotional campaigns.

While it's hard to say how much individuals will push back over the use of their data for promotional purposes, it's clear that AI technologies are already proving they have the ability to greatly enhance the overall ad experience and the future of the digital advertising industry.