Data is increasingly available, accessible and decipherable. You probably use it every day to help make personal decisions: tracking calories on your iPhone, browsing recommended movies based on what you’ve already viewed, seeing advertisements on your laptop and smartphone based on your browsing behavior and more.
These insights are based on data collected from you -- and they are used to predict actions you might take in the future.
Using predictive content analytics to select and refine relevant topics
In much the same way, predictive analytics enable content creators to use data to help them reach a particular audience. By mining data on numerous consumer preferences, including consumers’ purchasing history, reading preferences and browsing history, writers and editors would be able to figure out what type of content a target audience will find most valuable.
“Seismic shifts in both technology and consumer behavior during the past decade have produced a granular, virtually infinite record of every action consumers take online,” Wes Nichols explained in the Harvard Business Review. “Add to that the oceans of data from DVRs and digital set-top boxes, retail checkout, credit card transactions, call center logs and myriad other sources, and you find that marketers now have access to a previously unimaginable trove of information about what consumers see and do. The opportunity is clear.”
One important aspect of analytics is figuring out why people are visiting your site, and what content encourages them to stay.
Research at one website showed that content analytics generated 77% more pageviews than content produced without analytics, meaning that predictive analysis can be leveraged to help companies create content that will more effectively grab their consumers’ attention. That’s because understanding who clicks on what content, or how long they stay there, can help content marketers create content that resonates with their audience.
The effective use of content analytics can also dramatically improve a company’s ROI, often yielding improvements of up to 30% in marketing performance.
So what is stopping digital publishers from applying the principles of predictive analytics to their audience to determine what content might resonate best?
The challenge of actionability in predictive analytics
In a post for Medium, Parse.ly CTO Andrew Montalenti draws parallels between the predictive analytics models developed for financial markets and the media industry.
“Content measurement is similar to market measurement: fast-moving time series ticks, loaded with important metadata, full of peaks and troughs [. . .] millions of correlations exist between trends in the content ecosystem. Traditional Web analytics products are the Wall Street equivalent of the raw market feed.”
The challenge, of course, being that monetization is much more complicated in media companies. And actionability is not clearly defined for digital publishers: Data has mostly provided a rear-view mirror perspective.
That’s why predictive analytics is so difficult in the media industry versus other areas. But Montalenti paints an interesting picture: What if we turned every editor and writer into a content trader?
“Imagine: a content trading desk. Content suggestions stream on the screen. Based on topical trends, editors can make ‘buy’ or ‘sell’ decisions. ‘Buy’ means invest in content creation / licensing and grant it appropriate seed traffic. ‘Sell’ means starve it of attention and let it disappear from view.”
Using analytics to gauge the impact of the content you are creating
To improve the ROI of your content marketing strategy, it is critical to understand what is working—and what isn’t. You need to understand what content is resonating most with your target audience and what metrics matter in the realm of digital publishing; then you must use those metrics to refine your strategy.
You can use comments, likes, shares, tweets, inbound links and more to measure engagement, views, downloads, social chatter and referral links that will help you to quantify brand awareness. Using analytics to measure how your content is received by your intended audience should always be a priority.
As companies continue to improve their ability to extract increasingly precise and meaningful insights from big data, the importance of using analytics to craft an effective content marketing strategy will only increase. The uses of data are constantly expanding and improving, from engaging with consumers using real-time data to effectively leveraging audience insights by using audience reading paths.
And while nothing is stopping digital publishers from applying the principles of predictive analytics to their audience to determine what content might resonate best, the challenge is to make the data immediately actionable.
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