How often do you get coupons via email (and snail mail) based on your previous purchases? How many times do you find ads popping up on your screen based on your previous searches or clicks?
AdWord ads find a way into the news articles we read, content appears on the sidelines of our social media platforms while we scroll, and shop sites offer us “people who bought Item X also bought Item Y” suggestions. In some cases we simply ignore them. In other cases, we look at our surroundings and wonder if there’s a camera recording our every move.
“How do they know that?” we ask. What is behind the veil? Is it the Great and Powerful Wizard of Oz offering us products based on what we want and need, or are these simple algorithms based on our previous searches and clicks?
Well, I’ll give you a hint: it’s not a wizard—it’s AI—and it’s learning a lot about consumers and what we like as individuals.
Don’t get me wrong; I like it when my searches are trying to be more helpful—putting my interests right in front of me to make my user experience more convenient and make me feel like I am being relevantly included.
But if I’m leaving the experience feeling like I need to look over my shoulder, then the content marketing intention has missed its purpose. Maybe that’s why the combination of analytics and authenticity are in higher demand.
AI and Predictive Content
What does “predictive content” mean? Simply enough, it predicts what type of content is shown to each individual to make the most of every interaction. Predictive content relies on the uses of machine learning (ML) and predictive analytics to automatically put the most relevant content in front of each person across web, mobile, and email channels.
For example, IBM Watson (you might know this dashing AI from Jeopardy) has entered the predictive content game, explaining that “personality insights predict personality characteristics, needs, and values through written text.” It claims to understand customer habits and preferences on an individual level—and at scale.
In a Forbes article, Steve Olenski explains that “In very little time, AI delivers incredible insights. That new knowledge makes us, as human marketers, look even better at knowing what customers want from their experiences.”
Olenski further explains, “There will come a point in time where an artificial intelligence (AI)/machine learning (ML) platform may actually know better. That’s because artificial intelligence can sift through more data and extract patterns that might take us years to detect.”
Sounds great, right? But before we throw a party for AI on predictive content, let’s take a step back and ask if that content is truly speaking to the audience.
Based on market research, marketing and AI are still trying to find a balance. In a 2016 B2B Gallup report, 71% of consumers are uninterested or disengaged and 60% of B2B customers just don’t care about brand relationships. Instead, they prefer authentic human interaction.
So AI may be filtering for relevant content, but is it really engaging the consumer?
Content for Content’s Sake Vs. Authenticity and the Human Touch
When creating content strategies, it’s good to have a plan, but that plan is not always going to reach everyone for whom it is relevant.
So how do we do that from the back end?
AI has always created a sense of imaginative possibilities, but now we’re seeing it used in more pragmatic ways. Maybe it’s a little too pragmatic—AI may be showing too much of itself, which can be a problem for consumers—not because it can be annoying, but because consumers are savvy.
Consumers develop immunities to marketing trends and, if AI is simply filling an analytical “content for content’s sake” requirement, consumers could become weary of the strategy.
When the strategy starts to show itself, can it really be considered a strategy anymore?
So, as AI and consumer relationships try to weave themselves together seamlessly, one thing keeps getting in the way: the consumer’s desire for authenticity.
It’s not about how often one sees content or how many followers people have on any given social network; it’s about the authenticity of the content and how brands engage with their followers in meaningful ways.
The aim is to create trust like they would expect from a friend. These days, content is most effective when it’s packaged in trust. And that is what marketers should be doing with the predictive content they use–building relationships without overt selling.
In a previous Kapost blog post, “What Every Lead Wants,” we explain:
“Whether you’re conscious of it or not, every piece of content slated in your editorial calendar—every email, blog post, landing page, or Tweet—creates an expectation in the minds of potential clients. That expectation develops into an intent. They begin to anticipate what’s next. It’s your job to make sure your content matches that anticipation, that intent.”
In other words, our content should be creating and nurturing a relationship. And that relationship has to deliver a positive outcome to stay strong.
As content marketers, it’s our job to create an authentic balance. We can talk a lot about executing on robust content marketing plans, producing compelling Tweetable moments that are easy to remember, creating videos, publishing blogs, and focusing on reaching and engaging with audiences in new ways.
But we also know content for content’s sake is not effective. It’s necessary to master the tightrope walk between AI and human relationships.
Where Do We Go from Here?
What many debates miss is that predictive content generated by AI may be put in front of appropriate readers, but they do not address how it actually speaks to them. How does it create the customer journey needed for conversions? How does it create trustworthiness in the service or product?
Kapost addresses this in a recent blog that offers insight into each stage of the customer journey. Thinking in stages not only helps create authentic relationships with customers. But if you’re using AI, it can also offer the necessary cadence needed to nurture each customer based on his or her journey.
Essential to governing the ideal customer journey is a strategic taxonomy: “a holistic look at the metadata fields that are used to tag content assets across the multiple software tools in your MarTech stack.”
Simply put, one of the first ways technology can help content marketers walk customers through their journey is by processing structured data to identify them by their stage. Those data points are critical as they offer topics and tones customers desire for each step.
So, content marketers are starting to iron out a happy medium between the benefits of AI’s speedy analytics and the necessity of trustworthy interaction. It’s what content marketing for the customer experience demands.
And content marketing strategies are taking more innovative approaches every day.