Infinite Personalization: The AI Marketing Opportunity

What if marketers could create a highly personalized communications plan for every individual in a database? And not just by personalizing the content, but also the cadence, channel, layout, offers, and more of each communication. Even more, what if this plan could adapt in real-time to meet each customer’s evolving attributes and preferences?

Not too long ago this seemed like a preposterous aspiration, even for the largest marketing teams. In fact, for decades communications and marketing professionals have labored through an intensive annual process for crafting an organizational marketing plan. As mass marketing capabilities evolved to allow for content tailoring to unique audiences, so did communications plans with customer persona-led segmentation. However, the ability to achieve individualized hyper-personalization of communications is now possible with AI marketing tools.

Though it’s not just AI marketing capabilities that makes this a reality. It’s a combination of efforts that include deeper data curation and content adaptivity. AI is the high-octane performance personalization engine that drives this capability at scale. In this article we’ll explain the four key steps for putting infinite personalization into practice and spotlight several best practice examples in the market already today.

Step 1: Create Data Fusion to Curate Customer Insights

When it comes to applying data analysis to campaigns, context is critical to hone personalization efforts. Consider the scenario of a retail brand app user that only uses their app to locate items in their stores by seeing which aisle to go to for the item and get brand recommendations based on shopper reviews. In traditional analytical views this customer is likely a low priority for app marketing efforts as they aren’t making direct purchases through the channel. Yet, brands that have considered this unique behavior as an engagement signal in their CDP or CRM system can leverage specific use case context for differentiated messaging to maximize store visit outcomes.

This is an example of one of the many insights that can be unlocked by The Home Depot and their use of a real-time CDP for optimization of their experience design efforts. As stated in a recent Adobe case study on this capability development, The Home Depot “Realized customers’ shopping behaviors are not solely focused on shopping a single department or season…When they’re focused on a project, they can be inspired to look beyond an initial search whether they’re online or in store.” As a result, they can offer up relevant and timely recommendations at each stop in their shopping experience.

Similar contextual insights can be derived from the fusion of multiple data sources into a singular view of customer activities. These sources can include content engagement reporting, purchasing history, profile preferences, and more. Then weighting can be applied to those key signals to determine the ideal blend of messages and the right offers to deliver to each individual.

?Step 2: Put Adaptive Content into Practice for Enriched Personalization

Achieving deep personalization in communications requires the ability to recognize and adapt content to evolving customer preferences. This is where AI has taken content personalization to the next level. Not only has predictive AI expanded traditional data model views to leverage real-time insights, but generative AI has allowed for unique content versioning for every individual. This is the at the core of the infinite personalization possibility to seamlessly deliver completely unique content to every individual at every new interaction.??

As an example, Formula 1 (F1) has tapped into their connected fan ecosystem to leverage generative AI for hyper-personalized content experiences. Through their use of Salesforce’s Einstein GPT platform they can now speak directly to the most relevant engagement points for each fan, at any specific moment. This can range from live racing event attendance, casual play in their F1 racing video game, streaming of their popular Netflix series, and much more.

Today, major customer marketing platforms can deliver copy and creative content generation at scale in a matter of minutes. Imagine going from the Mad Libs style of dynamic content insertion for content personalization to a truly blank page approach. This could mean that these generative content experiences can evolve past the traditional email template to deliver uniquely crafted content that best meets the preferences for layout length and styling of each known consumer. With the rapid advancement of generative AI creative tools all of this is completely possible today.

Step 3: Individualize the Communications Delivery Cadence

Knowing how and when to deliver a message is often as important as determining what to say. This has often been the downfall of marketing communication efforts as poor cadence management has led to an abundance of unengaged communications or oversaturation of key messages. Now, with predictive AI campaign capabilities, we can take the guess work out of communications delivery to precisely align the frequency, send time, and prioritized channel to each customer’s demonstrated preferences.

One of the most beneficial capabilities is engagement frequency. This tool proactively sets the pace of communications for each recipient to the cadence that will drive the highest engagement level, preventing over and undersaturation of messages. Additionally, with send time optimization marketers can leverage AI to deliver communications on the specific day of week and hour of day where each recipient is most likely to engage. Lastly, predictive campaign AI can also decide the best channel split to deliver key messages on for each contact by measuring historical and recent channel engagement.

Lands’ End is a brand that was able to fully optimize their communication cadence using these AI-driven campaign tools with their partner, Movable Ink. In a case study, Lands’ End Chief Innovation Officer, Sarah Rasmusen, states “We were over-messaging with multiple blasts a day without really knowing how to extract ourselves from that pattern. But the Da Vinci solution is that missing piece for us, being able to maximize the relevancy of content to an individual subscriber.”

Keep in mind that the use of these tools is best geared toward relational content delivery as it may hinder the performance of mass sends for limited time offers. Though, with the possibility of infinite personalization now at our fingertips, the old technique of blast communications and blanket offers should be phased out for enriched personalized content engagement opportunities. Thus, the future of personalization will involve a fully individualized communications cadence for every customer through AI decisioning and automation capabilities.

?Step 4: Take Split-Testing to the Nth degree

Split-testing analysis has been a great way to fine tune marketing content attributes to lift engagement and conversion rates. Brands that have successfully deployed multivariate testing have been able to fast-track their optimization efforts even further. Yet, with each test result an either-or decision is the only possibility, leaving those in the minority group of a test to must adapt to the preferences of the majority.

Like other personalization efforts, predictive AI tools can now apply changes from split-testing down to the individual. So rather than testing across entire contact groups or smaller cohorts, A/B testing can be run at the individual-level. It can also be scaled to go far beyond a series of tests to consider an unlimited number of testing scenarios with an A/B/n approach. The most incredible aspect of this advancement is that the changes can be made instantaneously to fuel content adaptivity.

The resulting outcomes from the robust testing efforts are impressive. The major customer relationship platform, Braze, reported a 7.5% increase in open rates from campaigns that use personalized variants over normal winning A/B scenarios in campaigns. Even better, those singular personalized campaign variants can be connected to larger journey personalization to increase sales in Braze and other major CRM platforms today.

If You Can Dream it, You Can Do It

Every customer interaction is an opportunity to deliver new value in the customer relationship. Now, with properly connected data and trained AI, the possibilities to enhance individual customer communication experiences are truly limitless. Whether it’s enhanced in-store shopping experiences with personalized recommendations like The Home Depot delivers, perfectly timed emails for every recipient like Lands’ End, or adaptive content based on rapidly changing preferences like F1 executes, the idea of segmentation should start to be sunset in marketing tactics. The primary skill that AI marketing tools lack is imagination, so marketers can dream bigger about greater personalization experiences for their customers and then leverage the provided 4-step process to make them come to fruition.