We are in a pivotal time with B2B content, when automation meets AI meets analytics, customization is nearly table stakes, and content marketers are in an ever-hotter hot seat. When I began developing a vision report on the future of content last year, ChatGPT was not yet a thing, and content intelligence was defined by most as tracking basic content consumption data. Many interviews and technology innovations later, the published report, The Future Of B2B Content: A New Paradigm Requires Bold Bets And Experimentation, reveals the increasing expectations for a content engine that delivers a great customer experience. The report focuses on the moves that marketers will need to make to build that engine. It’s no small task — those “bold bets” may bring on cold sweats for marketers who take on the challenges. But hey, being at the forefront of change isn’t supposed to be easy.
I described the key precepts of this future state in a previous blog post, The Quantum Physics Of Future-Ready B2B Content. To net it out, the content engine of the future will deliver the intelligence that enables marketers to provide the contextualized experiences their audiences require. This means that the promise of “right content, right person, right time, right place” finally achieves fruition.
In this blog, I’ll dig into the cold sweats part of the equation: How do marketers actually set themselves up to make the bold bets? While there may not be a perfect formula, one thing is for sure: It’s going to take experimentation, and lots of it.
Experimentation Readiness: Powering The Machine
Delivering content that meets the moment means serving each audience member what they uniquely need for their buyer journey, when they need it. Consider the backdrop to this requirement: It means understanding what’s working and what isn’t, creating easily customizable content, automating content delivery, and fine-tuning the experience based on the stream of interaction signals and data collected. While much of this schema is rooted in technology — AI, automation, and analytics working together to build the content intelligence — it’s also about people and skills. Modular content is key to customization, so content creators must develop modular minds to plan and produce content components that can be assembled by machines. Effective activation of content modules depends upon more granular metadata tagging. This starts with humans developing the tagging structure and then teaching AI-driven systems to scale and manage a universal taxonomy. Alongside this work is the need for team members to develop engineering skills so that they can translate content strategy into programming, optimizing AI algorithms and automation rules to orchestrate experiences. The goal is a future fit content engine that is effective with far less human intervention.
The Experimentation Journey: One Micro-Interaction At A Time
Content engine transformation requires an ongoing commitment over the long haul, but experimentation can begin as soon as you have hit key milestones. These include: a body of tagged content for a target audience; the technology in place to collect signals and deliver some level of personalization; and the ability to track and analyze engagement in real- or near-real time. This is a step beyond A/B testing in email marketing, but the philosophy is similar. Capture a small percentage of your audience and deliver content using different sets of parameters in either inbound or outbound scenarios. For example, use an industry segment personalization construct — hospitality — versus a subsegment — restaurants — to determine whether the additional segmentation and related content customization drives better engagement. Depending upon the result, you could tune further to see if additional customization delivers even greater engagement — say, content specific to restaurants in coastal settings versus mountain settings. The point is to keep experimenting, monitoring, and measuring micro-interaction to achieve the best combinations of interactions and content customization.
“Your customers aren’t going to come out and tell you exactly what they want,” said Deane Barker, global director of content management at Optimizely. “But we have what we need to figure it out from the data. Try something, fail, try something else, and pay attention to what works and what doesn’t.”
From Experimentation To Evaluation
Developing this culture of experimentation will be key to achieving and scaling success with responsive, contextual experiences. Every campaign or tactical program should pilot several experiments that put new technology and skills to work to break new ground. Along with experimentation, marketers will also apply a new mindset to evaluate success. “AI will be capable of managing hundreds of thousands of experience permutations, auto-optimizing based on how an audience member interacts,” said James Kessinger, COO and CMO at Hushly. “To understand performance and tune the AI, we’ll look at the relationship between sets of corollaries and engagement results. The days of looking at asset views and downloads will give way to a completely new dimension of performance insight.”
For the full vision story, read the report, available to Forrester clients and for purchase. Join us at Forrester’s B2B Summit North America in Austin, June 5–7, for more content on the future of content and more on B2B.