How to Leverage Usage Intelligence Data for an Effective Content Strategy

Guest post by Keith Fenech.

Top-performing B2B content marketers put the information needs of their audiences ahead of the company’s sales and promotional message. That’s one of the key findings from the ninth annual report from the Content Marketing Institute and MarketingProfs: B2B Content Marketing 2019: Benchmarks, Budgets, and Trends—North America report.

Drilling further into the report, it’s interesting to note how marketers are researching their audiences, and the key gaps within that process. Nearly 75 percent of those surveyed named sales team feedback and website analytics as their top content planning and research tools, with less than half saying they talked to their customers.

Within that gap lies an opportunity to have a different kind of customer conversation, one that is driven by data. Software usage intelligence can be used to richly inform three areas of content strategy that will be absolutely vital for content marketing teams who want to put their audiences first in 2019.

Personalization

As if it wasn’t already tough to scale content strategy to meet the needs of different buying and customer personas, we’re now being told that analyzing and updating customer personas annually may not be enough, according to a Skyword article on the 5 Trends in Technology Content Marketing to Watch in 2019. “Several large brands I work with are now migrating to quarterly audience assessments, especially at the business unit level,” the author writes.

The challenge with marketing to personas is that those personas evolve over the journey of use, and the content delivered must evolve to maintain engagement. But it’s often difficult for marketing to track user journeys and personalize content, due to reliance on stats that don’t tell us much about the individual.

Quarterly audience assessments become a whole lot easier when there is a continuous feedback loop with the user (with the added benefit of usage intelligence data being that is anonymous). Access to runtime and feature analysis that we can segment by a number of parameters allows us to ask really interesting questions that drive really interesting content creation:

• How long are users on average spending with the new feature?
• Where are they running into issues?
• Does usage differ by region or computing infrastructure?

There are so many questions we can ask and answer to more deeply understand our audiences and their needs.

Storytelling

The brand journalism trend has given way to a more nuanced marching order of content departments—tell good stories. This can mean so many things, but something often glossed over in this particular strategy is the power of data in good storytelling.

Is a new feature in the upgrade being adopted at a much higher rate than the older version? Are users spending less time with the new feature to accomplish a process than they did with the older one? Are there usage patterns or best practices that are revealed in the data by industry, company size or region?

Consider the value of content that tells users, “Version x.1 is being adopted at a 20 percent higher rate than version x,” or, “Super users do this to optimize x process,” or “Here are some best practices in using x feature in manufacturing industries.”

To get even more eye-catching content, pull usage intelligence information from customers while they’re using the software. With a powerful combination of in-app messaging and software usage analytics, marketing professionals can present relevant questions to users in the context of the application and features that want to know more about—and target those questions to exactly the type of users you’re looking to reach.

Cross-team collaboration

Consider that stat mentioned at the top of this piece—that more than three-fourths of the research on audience needs is coming from the sales team. Perhaps product management and engineering might like a say there, maybe even some other departments (like customer service).

Perhaps (gasp) these silos exist even within your marketing team. When we start from the product itself, including the expertise of all stakeholders in product development and delivery becomes much easier because it’s based in data, not in opinion.

Say a few noisy accounts and users are pushing back on a plan to sunset functionality at use in their shops. Let’s look at the hundreds or thousands that are not using the legacy functionality, and start messaging a point of data. “Join the hundreds of users who are ready for version x.1,” and develop educational content and campaigns from there.

What’s more, different people with different expertise see different things in the data.

A scenario, such as users spending more time with a feature than was expected, might be apparent to product development but not to marketing. However, the information is hugely valuable in creating educational content that drive product engagement.

Data from Hubspot shows that five percent of the content we generate drives 90 percent of user engagement, and some 50 percent of content is going completely unused. Our users demand better—and usage intelligence can help us deliver that.

Keith Fenech is VP, Software Analytics at Revulytics and was the co-founder and CEO of Trackerbird Software Analytics before the company was acquired by Revulytics in 2016. Following the acquisition, Keith joined the Revulytics team and is now responsible for the strategic direction and growth of the Usage Analytics business within the company.