Bias isn't easy to see when you're aren't specifically looking for it. Perhaps that's its key negative trait: it obscures itself. After all, we don't know what we don't know — but it's easy to assume we know everything we need to.
Take, for instance, the "toilet-flush" thought experiment. People were asked to rate their understanding of how a toilet works. Overwhelmingly, participants chose a high rating, one that reflected that, yes, they had encountered many toilets in their day, and certainly understand this everyday object. But when shown how a toilet actually works — the mechanics of it — those same people realized they aren't toilet experts at all. They just knew how to flush.
The same is often true of how the complicated mechanics of data bias can clog up the value of seemingly simple data sets. You think you know how to use and interpret data because you see marketing data all the time. Data is data. It's supposed to be how you avoid typical gut-feel marketing biases. At the very least, you've certainly had some relationship with data. But that doesn't mean you're ready to carefully sidestep bias in content topic ideation.
For clarity, data biases are generally any behavioral or operational patterns that obscure the accuracy, clarity, and/or utility of data. Two of the most recognizable or common types of data biases are the confirmation and availability biases. The former lets you see what you want to see. The latter lets you assume your data (or the most available, accessible data) is the most relevant data there is.
Here, we'll tell you why you need to dig into bias more deeply, how you can avoid it, and how these new notions can help you achieve awareness stage success.
The Inner Circle
The issue with bias, as noted at the beginning of this post, is that, in order to spot it, you need a level of metacognition: You need to think about what you're thinking about when you look at your data.
When you look at your data, ask yourself: is this a world you're seeing as it is, or is this the world you've created or influenced? Is everything that's happening with your content happening with reference to only untouched control variables, or are you looking at variables you've actively controlled?
Informing your health IT awareness stage topic ideas from past efforts is not a bad idea or unfounded, but it is a surefire way to introduce bias. If you want to be a data-driven content marketer, you must consider your audience database, understand your data on subscribers, get a sense of who's interacting with what content, learn what types of posts perform well (according to the metrics already set), and the like.
But all of these pieces of data are intrinsic to your organization's perspective. All of these pieces can also include biases — in what you've chosen to define for metrics, in the contacts already interacting with your messages and being influenced by those messages and in the actual topics you've already produced. All of these things, though, need to be viewed for what they really are — pieces of context that sit inside your circle of perception and insight.
What's happening outside of that circle? And what's the impact if you ignore all of that?
Awareness stage data-driven content is a clear example of why these circles of perspective and the kinds of bias you have around them are so important to examine.
When you look at the so-called awareness you've generated, within your focal point, within your view as a marketer, you know what's working, but oftentimes, "what's working" is what's working in your existing database, or among your current followers or connections.
But good awareness building should always widen your circle of influence.
You want to find things that bring in net-new people and help them to connect emotionally with your content. You build net-new topics to broaden your sphere of reach. If you're only looking at what you know and what you want, you'll always confirm what you've always known. Consequently, you'll always do what you've always done. Bias limits and confines you to only what you already know; it doesn't allow you to learn what you aren't already aware of.
Perhaps, for example, you know you want your health IT content to target a given demographic. If you view performance reports and find that your content is being seen by readers outside of that demographic, you might conclude your content strategy has failed — that the topic didn't resonate with your target. But what if you instead viewed those who weren't in your original sphere of influence as individuals who allowed you to extend your content's reach? These unexpected or unwanted individuals may actually even be in-roads to reaching the buyer collective.
Creating great awareness stage data-driven content should be about widening your sphere of influence, not forcing it to become narrower.
Shift Your Perspective
So, how can you change what you're looking at, without introducing new bias?
It's important to create a level of engagement with your customers or prospects in uncontrolled environments, that is, without a defined agenda. If you're talking to your prospects to understand them and learn about their world, that can help, to a degree, but you carry bias into those conversations, as well. The bias can be at the lowest level: you want to know what you want to know. You're asking about purchase process, daily habits, etc., but all in order to reach your objective, which is to learn the answers as they relate to what you're marketing. In these instances, you're going to hear whatever you want to hear.
Instead, get to know what matters most to your prospects and customers. Consider allowing them to tell you what's happening in their environment, on their terms, without any agenda of your own except to learn. Then figure out how your world (or your product's world) connects to those pains and that environment.
As you shift your approach to gathering data, you'll start to map content topics instead of projecting them, effectively eliminating bias by finding more organic inroads.
Data-Driven Topic Ideation
Let's imagine you're actively avoiding data bias as you create new content topics for the awareness stage. Now, it's time to soak up the knowledge.
In order to produce the best data-driven content for generating awareness, focus on experimentation. Create theories and hypotheses. For example, your hypothesis might be that, in producing topic X for a buyer fitting buyer profile Y, you should see the behaviors that indicate Z. So you produce asset X for profile Y, and track exactly what happened. If there's a high propensity for Z as an outcome, then you've defined a formula for success. But if that outcome doesn't happen, then it's time to focus on what's actually happening: Is profile "A" taking this more seriously than profile Y? Is the content generally off the mark? Should your target be wider? More narrow?
Always have questions about what is happening and what you can do about it versus what you want to happen. That approach allows you to operate for your true buyers. In eliminating bias, you open yourself up to operate along the lines of what is "truer" or "more real" about your market, rather than along the lines of perceived truth. You won't be held to business objectives that aren't informed by the input of the buyers you want to reach.
Topic ideation should be through the lens of your buyers' data telling you what to do. The type of topics you choose to cover should be the types that drive engagement with desired buyers (taking into account both behavioral and demographic data). The kinds of assets and messaging you market should be aligned with what works; it either needs to fit the formula for success, or it needs to act as a test if, as in many cases, said formula doesn't yet exist.
Ultimately, avoiding data bias in your topic ideation process comes down to understanding where you are today, and what simple tests you should create in order to learn more. Don't boil the ocean. Just boil a cup at a time, and get a sense of what occurs. You'll make salt — and you'll learn how to do it again and again.
For more research on Healthcare Information and Technology content marketing best practices, download our latest ebook: 7 Research-Backed Best Practices for Healthcare IT Content Marketing.