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Posted 16 August 2011 11:05am by Andrew Davies with 6 comments
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…
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.
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.
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 is co-founder and Director of idio and a guest blogger on Econsultancy.
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