predictive-content--personalization-ai

Predictive Content and Personalization With AI

Segmentation taken to the extreme eventually creates segments of one. This personalization allows for dramatically more effective campaigns. It allows you to send content and offers that are uniquely optimized for every individual person.

This is called the segment of one (or audience of one), denoting a hyper-personalized outreach where the message is uniquely modified and optimized for each person. This would certainly make marketing more relevant!

In fact, personalization is the opposite of segmentation. With segmentation, we create groups of people with similar attributes and send the same messages to all of them. By definition, this is not personalized for each individual, even with micro-segmentation.

With personalization, we want each message to be truly unique. It is actually adjusted and optimized for each individual using prediction algorithms. There are already many vendors offering AI-based personalization systems.

The majority of these tools can personalize the content on web pages or email content. They do this by using machine learning algorithms to predict what customers want and deliver relevant content or product recommendations even before they ask.

The “Personalization in Shopping” report notes that “Shopper spend soars with personalization. Purchases where a recommendation was clicked saw a 10% higher average order value, and the per-visit spend of a shopper who clicks a recommendation is five times higher.”

The report further states, “Providing your customers with a personalized shopping experience is now the cost of entry to retail.” It reports that 50% of customers say they are likely to switch vendor if a company doesn’t foresee their needs, and 58% of consumers say technology has considerably altered their expectations of what customer experience companies shall give them.

Personalization is now a requirement for any company wanting to stay with the times. I think we can conclude that it isn’t optional anymore, but rather a pre-requisite to matching your competitors.

However, it isn’t just about recommending content, products or offers. It is about creating customer experiences that build engagement and drive retention. It’s about appearing less robotic and being more personal and relevant.

Personalization will be huge in the marketing industry. In fact, the future of marketing technology and the vendor landscape might well be an arms race for better personalization.

Many companies have developed AI-based technology that uses user behavior and content analysis to deliver the content that is most likely to resonate with a particular person. Most of them offer solutions for the personalization of web page and email content.

Some go beyond that to support additional communication channels. A few prominent companies in this space include Zeta (previously BoomTrain), Adobe Marketing Cloud, Emarsys, and Perzonalization.

I asked Lindsay Tjepkema, Global Head of Content for Emarsys, to explain their product. She noted that “Emarsys is a marketing cloud which provides AI based personalization across email, SMS, mobile, web, offline and IoT devices.”

The platform consolidates all web, mobile, email, and purchase information into a unified customer profile. This profile includes preferences, behavior trends, predicted behaviors, propensities, and affinities. The unified profile is the platform’s foundation—the “single source of truth”—which enables hyper-personalization across all channels.

Tjepkema continues: “As the real-time interactions scale, the need for content increases exponentially. Marketers will have to offload the technology tasks such as identifying segments, crafting journeys, and creating campaigns. They will have to concentrate purely on creating content and training machines on marketing strategies.”

Other companies avoid developing their own proprietary AI by building their products on the back of existing engines. OpenTopic takes this route, as their tool is built on top of IBM’s AI engine, Watson. The computing power of Watson is used to predict the most engaging assets that guide individuals through the customer journey.

In addition to the more traditional content recommendation functionalities, Dynamic Yield, Klevu, PureClarity, and Similar add personalization to search results as well. Dependent on what you have done before, you will get different search results on an e-commerce site, compared to your neighbor searching for the same thing.

I spoke with Mike Mallazzo, the head of content at Dynamic Yield. He points out that in its annual “What’s Hot in Digital Commerce” report, Gartner cited personalization as the number one strategic investment for brands in 2017.

McKinsey, he said, refers to digital personalization at scale as “marketing’s holy grail,” and the Boston Consulting Group predicts that personalization will push an $800 billion revenue shift to the 15% of brands that get it right in the next five years.

The message from the marketplace is clear: personalization is no longer just nice to have; it is the single most important strategy for boosting revenue and brand affinity online. Expect predictive content and hyper-personalization to be a major deal going forward.

Malazzo also discussed what helps set his company’s product apart: “Marketers can turn control over to the machines or insert guardrails in the form of merchandising rules to control which recommendations unit each visitor sees. For example, a high-end fashion brand may want our AI to recommend the products delivering the most revenue but may not want to place certain brands next to each other. In Dynamic Yield, it is possible to set this condition, allowing the machines to go to work within controls set by the marketer.”