Business today is digital, so content is critical. At the 2016 World Economic Forum, Accenture CEO Pierre Nanterme asserted, “Digital is the main reason just over half of the companies on the Fortune 500 have disappeared since the year 2000.” In other words, companies became extinct because they could not adapt their business successfully for the digital era. Businesses are recognizing that digital is do or die — and a big part of the “do” is content. IDC estimates that by 2019, companies will spend $2 trillion on digital transformation, and 25% of that spend will focus on information (content) transformation.

Companies measure what they value, so as they start to recognize the increased importance of information and content to their digital businesses, companies are starting to measure it. What does this mean for you? A big opportunity to do any or all of the following:

  • Gain more insight about your content effectiveness, which can help you make it more impactful.
  • Position your content as the lifeblood of digital business, from marketing and sales to product or service experience to support.
  • Connect your content to impact on every phase of the customer or user experience.
  • Gain the ability to predict (or at least reasonably estimate) content impact.
  • Gain data to drive content automation including personalization, dynamic delivery, and natural language generation (content creation).
  • Motivating people, from executives to stakeholders to potential contributors, to support your content approach.

So, how do you take advantage of this opportunity? My answer might surprise you. After advising a range of midsize, large, and enterprise companies as well as conducting independent research with Content Science, I find the answer does not start with the measurements or the data. The answer starts instead with an approach inspired by business intelligence. I like to call this approach content intelligence. Let’s walk through a framework and its implications for our mindset so you can make the most of your opportunity — and help your company avoid becoming the next victim of digital transformation.

The Content Intelligence Framework

Content intelligence represents the systems and software that transform content data and business data into actionable insights for content strategy and tactics with impact.

As you can see in figure 1, key elements of this system are collecting appropriate data from a variety of sources, analyzing that data in light of your content goals, and finally interpreting and acting on that data to understand effectiveness, return on investment, and more.

content measurement

Figure 1: A framework for a content intelligence system

This framework requires a number of changes to how many companies tackle measuring content. I want to cover three shifts that will be especially helpful to you as content strategists, content marketers, or other content professionals.

Shift 1: From Quality Mindset to Effectiveness Mindset

In Content Science’s research with nearly 200 content professionals (including professionals who support marketing), we found that the majority reported measuring content in terms of metrics related to quality, such as volume (number of content products completed), timeliness, delivery completion, defects, or revisions required.

However, the professionals who reported that their content teams or efforts were thriving also reported that they measure content in terms of effectiveness, such as impact on customer behavior or perceptions, and ROI.

content measurement

Figure 2: Fewer content teams extend measurement to effectiveness and
ROI metrics, but those teams report more success.

In reviewing open text responses and conducting follow-up interviews, we learned that teams focused on content effectiveness and ROI created a positive cycle, or upward spiral, where measurements informed better content decisions and more ambitious content goals, leading in turn to more success. As Morningstar’s Director of Content Marketing AnnMarie Gray explains,

“You should always be raising the bar for yourself. Nowadays everything has become so data-driven, which allows us to see what’s working, what content is driving results — but the value of that is what you do with it.”

What you do with your measurements leads to our next change, process.

Shift 2: From Drowning in Data to Asking Questions

If you’re not a data guru or a numbers fan, then I have good news for you. You don’t have to be either to plan your approach to content measurement. In fact, my friend and data guru Alan Segal, who also is the vice president of audience development and analytics at CNN, recommends starting with questions.

“Going through the data mining process and asking the right questions is the hardest part. With seemingly unlimited data and finite time, narrowing the scope to yield meaningful answers is critical.”

If you’re pressed about where to start, start here: What does success look like? As Tina Kiester of Teladoc noted in our interview, “If you don’t know what success looks like, you can’t work towards it.”

You do love numbers? Well, starting with questions will save you a tremendous amount of time and hassle instead of wading through a sea of data. Once you decide on the questions, you can then focus on which data sources  — and ultimately measurements — will help you get answers.

You will want to cover basic questions, such as “Did our content attract and engage qualified leads?” You can then look at a range of data, as suggested in Figure 1, with a focus on answering those questions and decide which measurements give the best indication of answers.

You also have the opportunity to ask questions to help unveil the full business value of your content, such as

  • Do customers use our support content as they shop or buy?
  • Does the content accelerate the shopping or buying process?
  • Does our content influence people to trust us more?
  • Does that trust impact whether people buy from us or recommend us?

So, the process needs to start with asking the right questions. How does it end? Ideally, with a decision. Don’t get stuck in analysis paralysis. If you collect data and never take time to interpret what it means and act on it, you might as well have no data at all. Most commonly, you will decide either to continue as is, to optimize performance, to predict the impact of a decision, or to investigate a problem or opportunity.

Shift 3: From One Data Source to Data Ecosystem

Your web analytics tool is like a Magic 8 Ball. It can give you some useful answers, but not all the answers. Now, even if you recognize this, the tricky part is getting your IT department (even if it’s a department of one) to recognize this. Old school IT thinking aspires to have a single platform act as a panacea. New thinking recognizes we live in a fragmented digital world and have to use a combination of platforms and tools adapted to different functions. Another way of saying that is ecosystem. (For some great resources about digital ecosystems, I highly recommend Gartner’s Digital Business Ecosystems and the Platform Economy.)

So let’s take a closer look at what this means for your content data. As the content intelligence framework suggests, you need a range of data sources and tools to access those sources. You need not only a Magic 8 Ball but also a Ouija Board and a Magic Mirror, if you will. So, as you plan what data you need to answer your content questions, you can start to plan your content data ecosystem. It likely will include a combination of platforms and smaller applications such as:

  • Website and other channel analytics.
  • Content management or content marketing platform analytics.
  • Optimization / testing analytics.
  • Sentiment tools or applications.
  • Voice of customer tools or applications.

And those platforms and apps will need to play nicely together, such as integrate each other’s data or integrate into a separate reporting tool, with APIs and plugins.

For example, FedEx has brought together a variety of data to assess the effectiveness of its content marketing strategy and efforts on an ongoing basis. Drew Bailey describes it like this:

“We created an aligned measurement methodology that would enable us to measure the effectiveness of all our ‘go to market’ tactics in support of the overall strategy and content marketing approach. The methodology includes both digital and offline components. In addition, we have trained our content leads on basic analytics tool features so they can be empowered to measure their own program effectiveness and provide insightful feedback to management about their respective programs.”

I love that Drew considered content intelligence at the same time he formed a content marketing strategy. For more about his approach, check out this article.

In closing, as you help your company transform into a truly digital business mediated by content, make the most of your opportunity to gain new insight and show business value. Let go of the old ways of measuring, embrace a fresh approach with content intelligence, and you won’t merely survive digital transformation, you (and your content) will thrive.

The Author

Colleen Jones is the founder and CEO of Content Science, a growing content intelligence and strategy company based in Atlanta GA. Content Science owns Content Science Review, Content Science Academy, and the content effectiveness software ContentWRX.  Colleen regularly consults with executives and practitioners to improve their strategy and processes for content. She shares insights and guidance from her experience regularly on Content Science Review, at events around the world, and in highly rated books such as Clout: The Art and Science of Influential Web Content.

Follow Colleen on Twitter at @leenjones or on LinkedIn.

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