At first glance, content marketing seems like something completely removed from data and predictive analytics. Content, it would appear, is an arena where the right-brained creatives still rule, unfettered by data or technology.
The truth is, however, that data and predictive analytics are a key part of an effective content marketing strategy. Data helps ensure that marketing content delivered to a prospect is as relevant as possible — whether that content is delivered through email, on a website, or via a social ad.
Bill Macaitis, who is now the CMO of Slack Technologies, believes content and data are closely related. As described in a book I co-wrote, The Big Data-Driven Business: How to Use Big Data to Win Customers, Beat Competitors and Boost Profits, Macaitis joined Zendesk in 2012 with the opportunity to build the exact kind of marketing department he wanted — from scratch, without silos or legacy systems. In creating his marketing department at Zendesk, Macaitis started with two teams: content and technology.
These teams were interrelated, because the technology (such as marketing automation systems and analytics tools) enabled Macaitis’ marketing team to understand what content was working and, just as important, what content wasn’t. Macaitis said his analytics team examined the content to discover, “Which [pieces of content] actually drove the most pipeline? Which ones helped accelerate the deal velocity? Which ones allowed us to get the giant, big deal that made our quarter? There are interesting lessons in it, and it’s not an arbitrary process or a philosophical call.”
The content that is most effective is relevant content. It is, in fact, an imperative that marketers serve their prospects relevant content. Data can help predict where a prospect is in the funnel and what content is necessary to move the prospect to the next stage of the funnel.
Relevance stands out simply because there is so much irrelevant content out there. A recent study by TrackMaven found that content production by marketers increased 78 percent between 2013 and 2014, but content engagement declined by 60 percent in the same timeframe.
Even the statistics about how the visitors we’ve attracted to our websites and how prospects share their email addresses indicate that most of the content we’re creating is not relevant. How could it be when 95 percent of the visitors to our websites bounce without any indication of interest? Or when only 20 percent of those who share their email address are actually opening our emails?
Data can help us with these distressing statistics, by helping to ensure that our content is what a prospect wants or needs. Amazon is the best-in-class when it comes to using data to deliver relevant content to its customers. Using data built on a combination of products we’ve ordered before, products we’ve researched, and products that customers like us have purchased, Amazon predicts what we’re going to buy next. (In my case, because I have a one-year-old baby at home, Amazon knows I’m probably there to order diapers).
All companies, no matter the size of their budgets, should strive to be more like Amazon in using data to predict what product its customers might be ready to buy – or at least in predicting what piece of content might help take one step closer to buying that product. Big data and predictive analytics may sound like they’re tools reserved for the Fortune 500, but data can be used in some very simple ways by companies of any size and in any industry to boost their content marketing performance.
For instance, if a prospect in your database clicks through on an email about a specific product, you could identify that action as predictive of interest in purchasing that product. In response, your marketing automation system could automatically send a follow up email with a video product demo.
Another example in which data can predict potential buying interest is when an anonymous visitor to your website, who has not yet shared an email address, could be identified as doing early-stage research on your company’s products and services. In that case, you could use this data to serve that anonymous prospect display or social advertising offering a whitepaper sharing insights on your industry.
Finally, geo-location data from a mobile phone might indicate a person has parked their car in a lot near your retail store. You could send a message to that phone offering a parking discount if they make a purchase at your store.
The examples are endless, and the tools are available for even the smallest of businesses to use big data and predictive analytics to serve relevant content, to drive prospects through the funnel, and to generate revenue.
To dive deeper into the concepts of big data and predictive analytics, join me and representatives from Mintigo and SiriusDecisions in this webinar titled “How to Be a Data-Driven Marketing Powerhouse with Predictive Analytics & Big Data”. Click here to learn more.