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How Analytics Can Help Marketers Reuse Content to Boost Sales
As content marketing becomes important, aging content becomes a concern. Here are some ways of using analytic reporting to develop content ideas and to align content with customers in the sales cycle.
Managers approach digital measurement as if its techniques are shiny and new. Much of the media and solutions seen on a digital screen these days are new. I know the marketing community was not speaking of chatbots back when I started in analytics nearly 9 years ago.
But content and its associated topics do not remain shiny and new. Media ages, as does its potential value to the user of that content. To keep marketing media in tip-top shape, marketing managers must review how to upgrade media to keep up with consumers in the sales cycle.
The key to upgrading content lies in analytics. Analytics provide metrics indicating where consumers best view your content. I spoke about a few tips at this year's Content Marketing World in Cleveland. I highlighted features that marketing managers and analysts can quickly derive from their analytics reports.
One idea I mentioned in the presentation is examining secondary metrics on page reports to discover evergreen content. If pages are ranked by sessions, many times analysts will focus on the session results displaying the top pages but ignore pages with potential -- the ones with fewer "hits" but a higher engagement metrics (time in session and pages per session).
In fact, if you were to graph your top 10 posts, you'd likely find ideas in your next 10, and even the 10 after that -- a long tail forms where some ideas may not be at the top of a list, but they appear on the reports consistently as you compare the rankings over time. The budding interest on these secondary and third groupings signal potential evergreen topics that can be expanded with follow up posts, infographics, or videos. That approach aided the retained visitor traffic and ad revenue for one of my earliest clients.
Analysts can also use site search and affinity reports to see what topics are of interest to visitors. The ideas can give additional indications of what topics causes visitors to come to a site and to leave. These indicators can be additional content topic sources.
Analysts can examine site search and affinity reports for various time periods to note how consistent these trends are, and how customer channels and media choices change over time. As a starting point, I suggested comparisons among 30, 60, and 90 day periods, as well as bookmarking important dates of expos, campaigns, and events to see if those activities brought a significant rise in site visitors.
These can also help in determining how predictive learning can be applied to content. Increasingly machine learning is at the heart of recommendation engines from chatbots to home devices such as Google Home and Amazon Echo. These platforms in various instances include online material, so the more task-related the content is, the better chance it will be part of a smart device query result.
The interest in predictive analytics has also raised interest in applying R programming (and Python) for text analysis. A number of packages now exist for ingesting social media posts and tweets, permitting sentiment analysis on commentary, as well as establishing data that can be used for advanced techniques such as regressions and clustering. The advanced techniques raise complexity in conducting analysis, but they also provide more value with nuanced segments and ideas highlighted, rather than segments based on report default settings (i.e. visitors who arrived to a site via referral traffic, search traffic, etc.)
With these tactics in mind, managers should be open to the kinds of digital channels that draw interest to content. The type of sources that seem right, from an initial effort to adjusting long-time campaigns, can vary. The most established campaign tactics, such as email and paid search, can provide value.
Meanwhile some opportunities for content marketing are just now gaining ground, such as image search. People are discovering products and services through the pictures they share online.
The major search engines and social media platforms have started to respond to growing image interest. Google has advanced image search to help users discover more information about products and services. Unique images from content can be pinned on Pinterest boards with links back to the content itself. In fact Ben Silbermann, Pinterest Founder and CEO said on a CNBC interview that "a lot of the future of search is going to be about pictures instead of keywords." So while sharing content and remarking on posts in social media is essential, marketing managers should not position an entire engagement strategy solely on likes, shares, and comments.
Content has to be adjusted to the rising number of consumer micro-moments by providing right-time information. Content usage is rising, too. Content Marketing Institute notes that larger investments in content from B2B and B2C marketers are on the horizon.
But like I said at the beginning, media and its potential value to the user ages. Using analytics to reveal consequential interest from past content can help revitalize content, reinvigorate the brand, and ultimately provide the right connection to the micro-moments in a customer's life.
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