As marketers, we are becoming increasingly data-focused. It is likely that the next generation of CMOs will not just be creatives, but also data scientists standing in the control room of their organisation, with dashboards of live data flowing in from sales, marketing, and customer service activity.

This goes beyond transaction and conversion data, to include details of interactions with brand-authored content, as well as user-generated content and sharing of content on social networks.

So how can content analytics allow you to build detailed customer profiles, analyse customer feedback for trends, and personalise content and product propositions?

Digital marketing channels bring with them a huge volume of content, whether published or monitored. As a result, understanding and leveraging digital content effectively has become strategically important, especially as investment is made in content marketing to attract and engage an interested audience.

Currently, the majority is unstructured: daily tweets and Facebook updates, blog posts about issues of interest to the audience, perhaps a curation of relevant sources, email newsletters with lifestyle articles and product offers, customer reviews and testimonials, product and service specifications, FAQs and other customer support material.

Gone are the days of creating a single message and delivering it through a couple of advertising channels. Although content and proposition personalisation are vital methods of managing millions of relationships concurrently, however this places an even heavier burden on creating, curating and reusing content. 

And of course, all this is just the content that you and your partners are creating. Beyond all that, your customers are blogging, tweeting, facebooking, posting... and we all know we should be listening.

So with content volumes going through the roof, (as Eric Schmidt recently stated, "every two days now we create as much information as we did from the dawn of civilization up until 2003"), how can one understand it, measure it, and generate insight from it? Well firstly…

What is “Content Analytics”?

It is the process of structuring previously unstructured content, by extracting new information. If you like, it is simply ‘measuring content’.

I am a human being. (Hopefully) that is self-evident, but beyond this basic fact, measuring me can help understand me more deeply. If you measured my height, you would find I extend to a, not altogether impressive, XYZ cm (5 foot 10 inches for those oldies amongst us).


If you recorded and listened to me talking for a day, you would start to identify key phrases and words that help describe me: my interests, likes and characteristics.  

Content analytics is conducting a similar measurement process on content. And the output is metadata that describes that content. Most content has some metadata, for example, the author, date created, and length.

Many terms are used to describe different approaches, for example you have probably come across text mining, semantic analysis, concept extraction, sentiment analysis and popularity metrics.

But the bottom line is that each adds additional descriptors about a piece of content. The extracted information can include topics, people, places, companies and concepts in the content, sentiment towards aspects of the content, and the language of that content. 

How can it be used in marketing?

You are probably using content analytics in some way already. Social Media Monitoring tools like Radian6 use sentiment analysis to help determine reputation, and sentiment towards market issues. But content analytics can go far beyond just listening more effectively.

For example:

Semantic web analytics

It’s good to know the pages a customer has viewed on your site. But if you know the concepts, people, places, and other topics being discussed in those pages, you can determine the interests and intent of a customer, and visualise each customer’s profile with tag-clouds that describe their interests based on what they have viewed and shared.

Social media data mining 

It’s great to have 1m people following you on Twitter. But if you can analyse their tweets to find the topics, sentiments, locations of their tweets, and of the links they have shared, you can build up a much more detailed picture of your followers.

This not only improves how you engage with them, but also provides vital insight for wider marketing activity.

Customer feedback analysis 

It’s great to have 10,000 customer reviews. But let’s face it: no one is ever going to read them all. Analysing customer feedback as it arrives can help identify trends in product and service reputation and faults very quickly.

Content reuse and curation 

It’s great to write every new bit of content from scratch, but often you are reinventing the wheel. By analysing your own archive, and that of partner organisations and other external sources, content can be reused and curated effectively, thereby reducing the average cost per article. 

Content segmentation and personalisation 

It’s great to have an audience that engage with the communications and content you create. But if you know what each piece of content is about, you can personalise and segment content effectively, so that each customer receives a proposition or piece of content that is relevant.

The future

As marketing channels become increasingly digitised, the benefits of content analytics become more easily obtainable. And as the volumes of content that each company deals with increases, the technological imperative becomes greater.

We are a far way off from perfect natural language processing, which understands content just like a human. But by starting to build metadata around content it becomes more understandable by search engines, content delivery, and analytics systems.

An understanding of content, means it can be measured, and the data-focused marketers amongst us would agree that measurement is the start of creating sustainable improvement.

Andrew Davies

Published 16 August, 2011 by Andrew Davies

Andrew Davies is the CMO and co-founder of Idio: a demand orchestration platform that learns from each buyer interaction to improve engagement and accelerate demand at large B2B enterprises.

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Comments (6)

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Renée Mellow, Thoora

Andrew - the information available through content analytics is no doubt exciting, but I wanted your take on how to find the metrics that are the most useful. What stats do you find most relevant in your business for content or Twitter for example?

Appreciate your insight.
Many thanks.
Renée Mellow, Thoora

about 7 years ago

Andrew Davies

Andrew Davies, Director at Idio

Hi Renee,

Well firstly we use it for insight - what are the topics that are trending amongst your customers and fans right now? What are the topics that are trending across the content you are curating/monitoring/filtering? This informs not only what the brand should be talking about, but also alerts to known customer interests and concerns.

We mostly use content analytics to infer topic interests to each individual customer (based on what they read, share and write). We connect this back to core brand CRM to enable the brand to use detailed preference and behaviour data for all future campaigns.

So for example on Twitter, we can tell the brand what each follower's content preferences are (favourite topics, news source preference, political bias, favourite sport etc etc etc). This can be used to personalise content and product propositions, as well as providing aggregate level customer insight.

about 7 years ago


Molly Flanagan

In the world of text analytics there are varying levels of detail, or accuracy that should be considered. A cursory review of key words (brand, product, celebrity, lifestyle) with a generic positive or negative descriptor will provide one level of interest. Connect these words with the rules of native language speakers... further define how the positive and negative words are used within the context of the writing... and you have a much deeper (and accurate) read on your audience. But it doesn't stop there. Further drill-down can reveal additional customer insight, which can then be used not only by your CSM team, but your marketing and product development folks too.

about 7 years ago


UK Suppliers

Nice post Andrew. You defined it very well and smartly that how to analyze content in a smart and effective way. I believe on rechecking and review of content twice or more time after writing it. Keep in mind that isn`t there any thing that is against the nature and behaviors of people? It should be without discrimination and based on real facts and figures. In fact it should be any problem solving issue to retain customer in a nice way.

almost 7 years ago


Renée Mellow, Thoora

Thanks for the response Andrew. Monitoring Twitter followers' trends and sharing patterns clearly provides great insight. Any tools or tips on this? I am impressed that you dive down to individual followers. Is that sort of measurement used for more targeted sales tactics or simply as a gauge on the performance of content you are posting?

almost 7 years ago


Gautam Tandon

Good article Andrew.

Regarding your comment :: "We are a far way off from perfect natural language processing, which understands content just like a human."

>> I think we'll always be far way off from that concept even if we have "perfect natural language processing" because even every human processes information differently. Your "inference" could be different from "mine"; And perhaps some other "more intelligent person" might interpret it in even totally different manner that could be totally way off from what we had in mind...

One of the future innovations I can think of in content analytics is the ability to generate customized interferences. In other words, the same content might be churned to give you what you are looking for and might be churned to give me what I am looking for. But then again, that's a far fetched idea again.


almost 7 years ago

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