Applying Marketing Data to Content Development | (un)Common Logic

Quality content that answers customer questions at the right time can have a significant impact on conversions and revenue, so the ROI of applying data to content can be very high.

First, let’s begin with a working definition of content marketing:

Content marketing done well is creating narratives and experiences that connect with your buyers where they are.

Separating this definition of content marketing into its main components reveals outcomes for the process:

• Creating narratives
• Creating relevant experiences
• Connecting with your buyers
• Meeting your buyers where they are

So now let’s apply the 3-step framework to these outcomes.

Step 1: Look and Listen

Break your desired outcomes into a series of single-focus questions that can be answered through data. Using your own insight and that of your team, form a hypothesis to test the aspect specified in the question.

Creating narratives requires content insights:

• What messages are best suited to different stages along the customer journey?
• Is the storytelling balanced with conversion-oriented signals?
• Are benefits or features more prominent in content?
• Is the educational style appropriate to the audience and industry?
• Is the content consistent with brand qualities?

Creating relevant experiences requires engagement insights:

• Which pieces of content have attracted the highest volume of attention?
• Which pieces of content have attracted the most engagement?
• Which pieces of content have prompted sustained user engagement?
• What content formats tend to attract the most attention, engagement, and sustained engagement?

Connecting with your buyers requires buyer insights:

• What trends are most relevant to our buyers’ industries right now?
• What terms are our buyers searching for?
• What topics come up most often in our buyers’ social media posts?

Meeting your buyers where they are requires buying cycle insights:

• How much time do our buyers spend in the buying cycle before making a purchase? What stage consumes the most hours for them?
• At what stage are our buyers most likely to abandon the buying cycle?
• What questions do they have throughout the buying cycle? What questions do they still have near the end of the buying cycle?

And competitive insights should be a priority throughout the process:

• How do the messages, storytelling, style, and consistency of our content compare to our competitors’ content?
• Which of our competitors’ content pieces have received the most attention, engagement, and sustained engagement?
• How well does our competitors’ content speak to our buyers’ current concerns and industry trends?
• Do our competitors anticipate and answer buyers’ questions throughout the cycle, and does their online experience encourage conversions/purchases?

Step 2: Analyze and Learn

Develop a plan to test each hypothesis individually. Before testing, make sure you’re tracking the relevant metrics consistently and accurately. Gather the resulting data and analyze the results.

Content insights:

These substance-based insights are often best attained through professional analysis, with new technologies adding a machine-driven component. Remember that qualitative data is still useful data, as long as the source is trusted and trustworthy.

Engagement insights:

Attention volume can be measured through traffic, comments, shares, and social engagements. Yes, we’ve identified these as vanity metrics, but here they’re used as a component of the data-gathering process rather than an end result unto themselves.

Engagement metrics include length of time spent on the page and response to conversion signals, while sustained engagement is often measured by how many other pages were viewed during visits that started with the content.

Buyer insights:

Sources for trend data would include volume by region of online discussions about the topic, while search query data can provide information on popular search terms. Social listening can provide both quantitative data and qualitative analysis, depending on your choice of partner for that function.

Buying cycle insights:

This data can come from both quantitative and qualitative sources. On-site search data can reveal what questions buyers have throughout the buying cycle, while your sales team can tell you what questions buyers still have before they complete the cycle. You can also ask your sales team to include a question or two about how buyers perceived the buying cycle, including their time investment and their overall impression of the experience.

Step 3: Act and Iterate

Use the gathered data to inform your next questions and tests. Remember that incremental improvements can make the most difference, especially in a testing environment that’s constantly changing.
Once you’ve tested hypotheses, gathered data and analyzed the results, it’s time to put those learnings into action. Using the insights that you’ve gathered, you could:

• Map your buying cycle/customer journey
• Match your content assets to each stage in the cycle
• Identify your strongest-performing assets to prioritize for optimization
• Create content to fill gaps in the cycle and create a plan to test and optimize these new assets
• Keep improving your content through testing and iterating
• Organize the optimized assets into campaigns that cover the entire buying cycle – then begin testing these campaigns

We hope you’ll give this framework a try and apply it to your content and data. Keep your expectations reasonable: while you might receive some groundbreaking insight, it’s more likely that you’ll merely get useful information to inform small changes in your content. And that’s actually perfect, because small changes can affect your results without confusing or alienating your audience.

Next week, we’ll take it a bit easy for Thanksgiving and treat you to some true tales of our data sea monsters on the loose.