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Get Smart About What Intelligent Content Really Means

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The world of content marketing is finally catching up to the importance of metadata ("data about data").

If you work with any kind of asset, you will have encountered metadata on many occasions. Examples include file type, file name, and date modified. All those annotations around your marketing files—descriptors that can be understood by you and your computer—relay instructive information about the content of the content.

"Intelligent content" is a term increasingly used in content marketing parlance to refer to content enriched by metadata. The industry definition is one provided by organization expert and author Ann Rockley, who as early as 2010, described intelligent content as "structurally rich and semantically aware, and is therefore automatically discoverable, reusable, reconfigurable and adaptable."

In Rockley's paradigm, content metadata powers better...

  • Content structure and standardization. Metadata provides a guide to users during content creation and acts as reference in the lifecycle of content. This leads to efficiency and standardization of content creation and management process.
  • Content retrieval, reuse, and revision. Once structure and metadata are established, content can be retrieved easily. If both structure and metadata follows a detailed taxonomy, then even specific content within large amounts of content can be retrieved quickly.

This is useful from a content management perspective, but it is also curiously limiting.

Far from just using metadata to organize and find content internally, metadata also has an integral part to play in enabling B2B content marketers to learn more about their buyers, optimize their B2B content strategy, and match content to buyers as they proceed through the purchase funnel.

Using Metadata to Learn About Your Buyers

Content analytics—the practice of using automation to analyze content and enrich it with metadata—has broadened the types of metadata that can be added to your content.

Content analytics uses Natural Language Processing to read the words in a piece of text. Much like a human scanning a page, content analytics can pick out and understand nouns, verbs, and adjectives. This, in turn, means that as well as file type and file name, content analytics can extend to identifying people, places, sentiments, concepts, and products—and will annotate your content with any of these topics if they are mentioned.

Moreover, the same content metadata that content analytics uses to describe your content can be used to describe the buyers that interact with that piece of content.

As an example, consider what MarketingProfs could learn about you based on the metadata of this article. The company might surmise that you are interested in "intelligent content," "Financial Times," "John O'Donovan," and "content management."

That assumption could be accurate...or false. One interaction with a single piece of content is not enough to build up an accurate profile of you. However, if your reading arc around MarketingProfs was tracked over a short period of time, we might learn very quickly that you're regularly consuming content about content curation and personalization.

This information would be useful for MarketingProfs at both an individual and aggregate audience level: informing editorial strategy ("We know this is what interests our audience") and content audits ("There is a gap in our coverage on this topic").

Similarly, B2B organizations that use content to nurture their leads and prospects over prolonged sales cycles have an incredible opportunity to make their content not just engage but also provide valuable buyer insight.

Intelligent Content for B2Bs

Prospect self-education is taking more and more of the B2B (and high-value B2C) purchase journey. B2B buyers are 57% of the way to a buying decision before they are willing to talk to a sales rep, according to a recent study by CEB.

For companies using content marketing to attract, engage, and convert prospects, content metadata puts them in an advantageous position. They can collect information about each prospect every time the prospect consumes a piece of information about the company.

The metadata added to vendor content—such as blogs, whitepapers, and PDFs—can be used to understand and gauge prospect interests. Consider how useful it would be for a salesperson to not just receive a lead with the person's contact details and a marketing automation-generated activity score but also with the person's most current interests. Often these "interests" are needs or pain points that could be exploited by salespeople for more successful sales calls.

This is the kind of insight available for B2B organizations that use intelligent content—not just for content management (structure, standardization, reuse, retrieval, etc.) but also for the sales intelligence generated from each prospect's unique content consumption patterns. No wonder that John O'Donovan, CTO of the Financial Times, explained that "the metadata around your content is as important as the content itself."

* * *

The conventional view of intelligent content is guilty of being too being content-centric rather than buyer-centric. Metadata has multiples uses inside the enterprise, but it excels once it is liberated from a content management-only mentality and used to track inbound and outbound interactions with marketing content as well.

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Andrew Davies is a co-founder and the CMO of idio, a content intelligence platform that helps leading content marketers maximize the value of their content marketing.

LinkedIn: Andrew Davies

Twitter: @andjdavies

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  • by Alicia C. Fri Aug 7, 2015 via mobile

    Just making sure I understand correctly... So the metadata a company uses will help them gauge prospect interests? Is this measured through the prospects engagement?

  • by Andrew Davies Mon Aug 10, 2015 via web

    Hi Alicia,

    Yes - the same metadata that is used to describe the topics of a piece of content, can be used to describe anyone who reads that content. This has to be done with 'Content Intelligence' technology (see:, for example). In this case, the metadata - the content topics - are a proxy for 'interests'.

    To your second point, yes - this is measured by engagement with content. The more that you engage with content that contains a particular topic (for example, "Content marketing"), the more likelihood we can say that you're interested in that topic.

    Andrew Davies
    CMO and cofounder at idio.

  • by Steve Peck Mon Aug 10, 2015 via web

    Thought provoking article. Follow up question to Alicia. Does this approach take into account the actual engagement with the content beyond the initial click. i.e. If I were to consume an full piece of content and find value in it 'cover to cover' would I be treated the same way as someone who clicked into the content and only got a little bit in before finding it wasn't relevant?

    Suppose this could work itself out over time with multiple engagements, but curious how effective this approach is for high value leads with limited engagement across only one or two content pieces%3E

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