It’s no secret that digital tools are changing the way we do business. As content-driven organizations work to compete against other forms of media and entertainment, they can leverage underutilized data to hone in on what content is performing well for key segments of their audience, how their audience prefers to consume content, and what they’d like to see next.
The future of your organization depends on your ability to use data.
Content has become one of the primary ways in which marketers try to reach prospects and customers online. Hundreds of millions of pieces of content are created daily - including published content, blog posts, and whitepapers, as well as multi-media content like photos, videos, webinars, and social media.
Given the vast scale of content being created - especially for content-led organizations like publishers, media companies, and associations - it can be difficult to measure the impact of individual pieces and categories of content. That’s why content intelligence is essential.
Content intelligence is defined as the use of technology to understand the impact and effectiveness of your content, and using that data to guide decision-making about existing and future content. You don’t have to guess what your readers will be interested in - because your data shows you what performs well with your high priority audiences.
With content intelligence, you receive valuable insights into your readers’ preferences and can get smarters about the way you create and deliver content to your audience.
3 Types of Data Analysis
When we talk about content intelligence technology, we’re usually talking about a combination of the following types of data analysis:
- Big Data: The process of analyzing large data sets to reveal patterns and associations in human behavior. People use big data for everything from seasonal sales projections to the stock market.
- Machine Learning: A branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Unlike Big Data, machine learning incorporates analysis of data sets with additional action to maximize its odds of achieving a specific goal.
- Natural Language Processing: A branch of artificial intelligence that specifically focuses on interactions between computers and human language. NLP serves the purpose of extracting contextual information and insights contained within the content, as well as categorizing and organizing the content itself.
When combined, these types of data analysis are the driving force in generating insights that help organizations improve the effectiveness of their content.
3 Pillars of Content Intelligence
Machine learning and natural language processing may sound like intimidating concepts - but mastering the art of content intelligence doesn't necessarily require you to become a data scientist. In fact, learning the basics of how to extract meaning from your content data can be as simple as finding the right tools to refine your strategy.
1. Data Collection
With the massive amounts of content being generated daily, there’s are massive streams of performance data available to answer questions like:
- Which content/pages are being visited?
- Which content is getting the most clicks?
- Which content gets the most views on social media?
- How long does a reader spend reading digital journals, on average?
- How many users clicked a link in an email to sign up for a conference?
With the move away from third-party cookies and towards first-party data strategies, it’s even more critical than ever to make sure that data is being collected with privacy regulation best-practices in mind.
2. Analysis:
Analytics tools such as Google Analytics can show an association or publisher how their content is performing - but these individual content metrics often don’t represent the full pictures of reader engagement.
Analysis truly begins when you’re able to marry the content-level data you’ve collected to a greater customer behavior context, answering deeper questions about the patterns and trends of your content and the readers engaging with them.
Things like page views and read depth, when matched with individual users, go beyond surface-level performance and can tell you a lot about how content is resonating with various segments of your audience at key moments in their journey.
- What topics are trending amongst C-suite members?
- What content pieces have generated the highest ROI?
- What content is most popular among your millennial readers?
- Which whitepapers are most likely to result in new member conversions?
- Which content is most effective at driving renewals?
3. Insight Generation:
While many publishers and media companies have long relied on BI tools to help them make sense of data, real content intelligence culminates in the generation of actionable insights - something that is often easier said than done.
Being able to draw true insights and act on data requires an understanding of the holistic influence and effectiveness of individual pieces and groups of content, in order to answer just two questions:
- How can we maximize and improve our existing content?
- What new content should we create?
With a clear view of your content, you can ensure that you’re delivering the right content to the right users, recommending the best content for individual users based on their individual preferences, and investing in the right content to drive results. The better your content intelligence, the better you can create and deliver more effective and more impactful content.
A CDP - “All Your Data in One Place”
How can we turn content-driven organizations into “data-powered” ones, where the integrated use of data feeds the decision-making process?
Hum is the only CDP (customer data platform) built with the needs of content-driven organizations in mind. Our next generation Customer Data Platform (CDP) was built to solve digital challenges faced by scholarly publishers, B2B media companies, and associations - and to help these organizations make content intelligence a part of how they run their business.
As our Chief Technology Officer, Niall Little, describes:
“Hum's CDP tracks data, just like a generic CDP would, but it has another layer. It also includes an association-specific activation engine to better grow and engage audiences. Essentially, CDPs take the need for a Data Scientist out of the equation. They make it easy for an organization to activate readers, members, subscribers, and partners via integrations with each piece of your existing tech stack.”
Learn more about Hum and why leading publishers and associations choose Hum to be the content intelligence MVP in their tech stack.