Category: marketing

The problem with content marketing

Blog-images-FINAL460X460_your-content-marketing-sucksA few weeks ago, I wrote in this space about “predictive” content marketing as the wave of the future. One of the sources cited in that post mentioned that most content marketers take a hit-or-miss approach that falls short of the mark 80 percent to 90 percent of the time. That source suggested that predictive content marketing would equip marketers with the tools they need to drop that failure rate tremendously. He attributed the misses to our lack of predictive marketing ability.

But since that post, I’ve been thinking there may be a more basic problem with much content marketing. And I think the problem boils down to one thing:

A lot of our content sucks.

I’m talking specifically about higher education content, although the same charge could be leveled against most organizations. A recent Business2Community post says essentially the same thing.

Our content doesn’t suck because it’s poorly written and sloppily edited (although that is sometimes the case). It sucks because it is more focused on the institution or business than on the customer or audience.

If we try to implement predictive content marketing without first making our content more focused on the customer and less focused on our institutions, then I predict our content marketing efforts will predictably fail.

Customer first, content second

Think about the kind of content a typical university churns out. Is it written to meet the needs of our audience, or is it written to make ourselves feel good?

Does much of our content focus on the accolades our universities, faculty, students and staff receive more than on what might be of interest to our audiences?

Are we more concerned with announcing the latest appointments and administrative reorganizations than on what prospective students, alumni, research supporters or other customers are interested in?

If so, then it’s no wonder our content marketing isn’t yielding the results we’d like to see.

Back in May of 2013, I wrote about the need to take an audience-centered approach to marketing first, then develop content tailored to that audience. (This wasn’t an original idea; I’d read about it here and also here.)

So, while predictive content marketing may help us out in the future, it won’t do any good if we don’t take a new look at our content. It boils down to the old advertising maxim to sell benefits, not features. For more on this, here’s a terrific blog post: Features tell, but benefits sell. (That post’s lede sums it up: “People have little interest in purchasing a bed; what they want is a good night’s sleep.”)

Jay Baer knows this and talks about it in detail in his book Youtility: Why Smart Marketing Is About Help Not Hype. The gist of Baer’s book is that organizations that offer content that helps people solve problems will be the winners in the new digital economy. Gini Dietrich knows this too, and she talks about it a lot in her book Spin Sucks: Communication and Reputation Management in the Digital Age. Both books are must-reads for content marketers.

I know that colleges and universities crank out a lot of great content. But we also crank out a lot of stuff that has little or marginal value to our customers or audiences. The latter might make the boss or an important faculty member happy, so you’ve got to balance those needs with your marketing goals and deal with it. The boss, the boss’s boss, the faculty member, etc. — those are important internal customers. But the more content we create that adds value to the customers that we are trying to attract and keep, the better chance we will have to make more of our content marketing stick instead of suck.

Image via Catalyst, Your Content Marketing Sucks.

Predictive content marketing, anyone?

computer-shopper-crystal-ballContent marketing as we know it is inefficient, subjective and ineffective.

That’s according to “research” conducted by an inbound-marketing company and written about in Forbes last fall.

(A caveat here: Always be suspicious of research conducted by a company with a self-interest in the topic and with a solution to the problem it uncovers. Such is the case with this report. But still, the thought it raises is intriguing, so please stick with me.)

According to the CEO of the inbound-marketing company that conduced the study, most content marketers don’t know what they’re doing. They’re taking the old-fashioned “spray and pray” or shotgun approach to sending stories out into the ether and hoping something sticks. These content marketers, according to the CEO, take a “subjective ‘hit or miss’ approach is a ‘miss’ 80 to 90 percent of the time.”

Furthermore, writes Forbes’ Mikal E. Belicove: “Only 10 to 20 percent of a company’s website content drives 90 percent of its web traffic, and only half a percent of a website’s content drives more than 50 percent of its traffic.”

Those numbers don’t sound very impressive, do they?

Predictive analytics to the rescue

The solution? Something called predictive analytics, a method of data-mining to determine what consumers will like, based on their purchasing history, reading preferences and myriad other data variables. It’s akin to predictive personalization, which as anyone who’s ever purchased from Amazon knows, has been around for a while.

But the idea of predicting what types of marketing content will connect with people is apparently still a young and unproven discipline. (Update: After publishing this post, I learned via @KarineJoly of a 14-week Higher Ed Experts course on this very subject coming up this fall.)  The aforementioned CEO was hyping a new predictive analytics tool for content marketing, but a quick check of the company’s website shows no sign of the tool, just a request to “check back soon.”

So we aren’t in the marketing world version of Minority Report just yet, and no brand (to my knowledge, at least) has developed a pre-cog to perfectly predict what you will read or watch.

Gaining traction

No one yet knows just how well predictive analytics will work. Will we buy 100 percent of the stuff marketed to us 100 percent of the time? Will marketers be able to accurately predict our needs, wants and desires so accurately by using sophisticated data-mining and -analytics techniques?

Who knows? But the idea is gaining traction.

In Advertising Analytics 2.0, an article in the March 2013 Harvard Business Review, Wes Nichols suggests a shift toward more sophisticated, data-driven approaches to content marketing. “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,” he writes. “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.”

In this new world, marketers who stick with traditional analytics 1.0 measurement approaches do so at their peril. Those methods, which look backward a few times a year to correlate sales with a few dozen variables, are dangerously outdated. Many of the world’s biggest multinationals are now deploying analytics 2.0, a set of capabilities that can chew through terabytes of data and hundreds of variables in real time. It allows these companies to create an ultra-high-definition picture of their marketing performance, run scenarios, and change ad strategies on the fly. Enabled by recent exponential leaps in computing power, cloud-based analytics, and cheap data storage, these predictive tools measure the interaction of advertising across media and sales channels, and they identify precisely how exogenous variables (including the broader economy, competitive offerings, and even the weather) affect ad performance. The resulting analyses, put simply, reveal what really works. With these data-driven insights, companies can often maintain their existing budgets yet achieve improvements of 10% to 30% (sometimes more) in marketing performance.

College and university marketing budgets, which are non-existent by multinational conglomerate standards, have little capacity for the kind of computing horsepower needed to “chew through terabytes of data.” But maybe we could partner with our computer science departments and develop a way to leverage the educational experience of our students with the practical needs of data mining for marketing.

A recent post by Mars Cyrillo, product and marketing director at CI&T, notes that even the most sophisticated approaches to what he calls “adaptive” marketing remain primitive and run on rule-based algorithms. (Think of Amazon’s “People who bought [the product you just purchased] also bough [a list of products Amazon would love for you to purchase],” and you get the idea.) But as you know from your own online purchases, the approach doesn’t always work. “Humans.” he writes, “are just too complex to fit basic rules.”

Planning for the predictive future

So not even Amazon has perfected predictive marketing.

Over the next five years or so, Cyrillo predicts, “Building applications that close the gap in a seamless way is where the greatest Digital Marketing opportunities lie.” He suggests we start planning for the predictive content marketing future now by “building experiments now on how to close this gap in some of your landing pages, built with responsive design in order to fit nearly ‘any screen.'”

Also: “Give more flexibility to your users so that they can interact more, and consider bringing your social posts to your website so that they are blended with your other content, and then collect more data. Then look at what you are getting with a different lens, or prism.”

This white paper (PDF) from Ricoh — the copy machine company — also offers seven steps for using predictive modeling in your content marketing strategy. (I discovered this white paper in my online quest for more information about predictive content marketing. But I’m not in the market for a new photocopier, Ricoh, so if there was some sort of predictive algorithm at work to make your white paper findable to your target market, it didn’t pay off this time. I guess we’re still in the hit-or-miss phase of content marketing.)