If you’re a marketer, you almost certainly already use analytics every day. The most common, and most obvious, way to use analytics as a marketer is to understand what brings visitors to your site, and what activities they engage in while they’re there. However, there is a second use of analytics, and that is to use scoring algorithms to identify potential conversions, and use that data to identify what content will be most effective for the consumer. This is called predictive content analytics.
What is Predictive Content Analytics?
Predictive content analytics is a relatively new approach to content marketing in which the supply of content is customized to match the demand. As content marketers, there is a constant struggle to produce content that consumers will actually – well, consume. The answer in the past has been to influence consumers in order to better shape demand, and the other approach (and typically, the domain of market research) aims to tailor content to more closely match demand. Predictive content analytics streamlines the tailoring of content, taking it out of the hands of market researchers and into the hands of the content marketer.
Why Use Predictive Content Analytics?
To understand the benefits of Predictive Content Analytics, consider an example situation: you’re a content marketer consistently producing content, but then your traditional analytics show that only 5% or so of the content you produce is responsible for over 80% of your website’s interactions. In other words, 95% of your content is failing to generate any results at all. If this sounds familiar to you, there’s a reason: it’s very typical of most content marketing efforts.
So, as a business owner, how do you justify the expense of a content marketing strategy that is 95% useless? Simple: the content that does produce conversions makes up for the cost of the content that doesn’t. That’s the power of content marketing.
Imagine if you were able to get a higher percentage of your content to perform with that kind of efficiency. Imagine you could get all of it to perform that way. This is the power of predictive content.
How Predictive Content Analytics Works
So, all of this may sound well and good, but how does it work? When developing a predictive content strategy, it is important to first understand where your current strategy is failing. Why is it that 95% of your content is failing to draw interactions? The answer, of course, is because all content is developed, more or less, based on educated guesses as to the habits and interests of consumers.
Using predictive analytics allows you to move out of the area of trial-and-error keyword research and content development, and get right to better results.
Predictive Analytics vs. Descriptive Analytics – Key Differences
In marketing, and content marketing in particular, we rely heavily on hindsight to do what we do. Predictive analytics, rather than looking in the past, builds out a map of prospect interests right now and iterates those interests into the future.
Predictive content works by collecting data based upon what consumers are actually reading and interacting with right now. Once that data is compiled, it can be predictively modeled, as long as you have access to predictive content analytics. Predictive analytics systems take this data, and then take a look at your content repository, along with the content repositories of your competitors. Then, for every piece of content, the analytics system builds a topic composite, defined by a cloud of keywords extracted from the content, consisting of the primary topics, peripheral topics, and associated topics that give that particular piece of content its unique character.
This allows you to do something you’ve not been able to do with traditional analytics: look forward rather than backward. With this composite, you can construct interest profiles on consumption patterns, and as consumers interact more content, those profiles actually evolve.
In this way, by performing simultaneous surveys of industry topics along with profiling their inter-relations and keeping track of interactions as always, the predictive content marketer can track user behavior pertaining to specific topics, and use it to build a projective content strategy. This allows you to predict – in a measurable way – what content will capture the interest of consumers moving forward in a way that is constantly evolving.
The Brass Tacks of Predictive Content Analytics
So far, our discussion of predictive content analytics, fascinating though it may have been, is a little on the conceptual side. Let’s talk about some real ways you can leverage predictive content analytics in your own content marketing practices, once you begin compiling interest data for both your own and competitors’ content.
- Personalized Content Experience: By utilizing predictive content analytics along with simple tracking cookies, you can do something magical: display different content to different readers, based upon that reader’s unique interest profile.
- Competitor analysis: By considering not only your own content but also competitors’ content in your interest profile, you can get a clearer picture of where those competitors stand in the content supply-demand race.
- Anticipate Trends: Using predictive analytics, you can keep a closer eye on what industry thought leaders are writing about – allowing you to keep you finger more firmly on the pulse of your industry.
When it comes to crafting a successful marketing strategy, analytics is an important tool: but by allowing hindsight-laden traditional analytics to dictate your practices, you could end up living in – and marketing to – the past. Predictive content analytics enables you to take a more real-time approach to development, make more accurate and informed decisions, and develop a deeper understanding of your own industry as a whole.
Have you used predictive content analytics? If so, would love to hear your ideas in the comments. If you’re looking for help getting predictive content analytics set up, reach out and our team would be glad to help! And, remember to follow Visible Factors on Twitter.