As a digital marketer, I’m all for tools that make my job easier. Some of my go-to’s are Google Analytics and Hotjar which, together, paint a nice picture of what content drives traffic and how they engage with it. Although, they only give us so much context into how well the actual content caters to individual visitor needs.
It got me wondering: How much of our content is actually useful?
Enter: content intelligence.
Episerver (now Optimizely) is currently offering a free version of its Content Intelligence product which helps teams quickly understand if their site content meets the real-time needs of their visitors. I had the chance to implement it and walk through the data, so I wanted to share what I’ve learned and my thoughts so far.
Here’s how it works: it evaluates pages on your site and determines what topics are on the page and how important they are. Then, it identifies the topics associated with a given piece of content. At the same time, it tracks visitors on your site and builds a buyer’s intent profile based on the topics and pages they visit or complete actions on.
Now, for the fun part.
I’m always trying to predict what piece of content will be most helpful to our audiences. After four weeks of collecting data, Content intelligence made it easy to understand and bring together content auditing, buyer personas, website analytics, channel optimization, and more into a single view.
Below, we see a small snapshot of our most engaging pages and pieces of content. Here, we can see how many times each piece has been viewed, the percent of total site engagement a piece of content makes up, and the average pages per session once they’ve viewed that content.
Let’s look at one of our best performing blogs: How to Turn Your Website into a Progressive Web App (with code examples)
Looking at its content profile, we can see what topics appear and are most popular with a word cloud and relevancy list. This can help give an idea for how well our target keywords are incorporated and what other topics may be present. These topics are based on Natural Language processing of the content’s HTML and further assessed against pre-generated topics.
Here’s this post’s word cloud and relevancy list:
To support that hypothesis, I look at topic performance:
This can seem overwhelming and cluttered at first, but each blue dot is a topic. The X axis is the unique interactions for a topic, and the Y axis is the volume of content on that topic. We see that ‘JSON’ has a larger number of unique impressions, but only makes mention in 11 pieces of content. Maybe we should consider creating more content related to JSON.
As another example, I like to write about C2’s company culture and employment opportunities. Looking at this, ‘Employment’ has a low number of unique impressions, and already 28 pieces of content about it. I know to scale back on this topic and focus content creation efforts elsewhere.
This is just one example for how content intelligence takes some of the guesswork out of what types of topics and content to focus on. It can also help us understand how each channel performs, what type and format of content perform best, and how much content is actually used or viewed. This can help teams make stronger decisions around content strategy or prioritizing the creation of specific pieces of content to close the gap between what visitors want and what currently exists.
Not only does content intelligence help marketers audit and analyze, it can influence the optimization of what content reaches individuals interested in similar topics through machine learning-based content recommendations. This is what we’re currently working on implementing, but I wanted to share our plans for moving forward and other actionable ways we can leverage data.
For each site visitor, a profile is created that collects any topics they are interested in and goals they may have completed that we set up, for example, if they signed up for the newsletter or looked at a page that shows sales intent. This allows us to understand how our content is consumed and by whom. We can, then, look at these converting profiles next to our persona customer journeys so we know how to adjust our strategy to get others to do the same and more often.
Using past engagement from individual profile data and machine learning, individualized content recommendations present what it thinks is the next best piece of content to show. We can also control what content is approved or hidden from being recommended to users based on specific pages, types of content, and so on. I already anticipate this making it easier to identify and measure the relevancy and impact of our content, including what is still missing that we need to create to support our targeted audience.
Being a B2B marketer, we typically have bigger gaps in data, more complex sales cycles, and longer-term nurturing requirements. Regardless of platform, content intelligence can help take the manual effort and guesswork out of personalization with tools to help marketers assess content, set goals, and autonomously personalize content on a site for each individual visitor.
As the customer journey increasingly becomes more digitally focused, content intelligence can equip teams with a greater understanding of how to create and maintain high-value content and messages that align and adapt to customer needs. It’s no longer about creating more content, rather, creating intentional content that alleviate and support a user through the entire buyers’ journey.
Look forward to my follow-up article but, in the meantime, check out and sign up for Episerver’s (Optimizely) free version of Content Intelligence. Remember, anyone can use it!