Web Analytics for Content Planning

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Web analytics can do more than report progress: it can inform process.

We commonly associate web analytics (including Google Analytics) with web governance and measurement, using it to evaluate content performance and user interaction post website launch. But web analytics can do more than report progress: it can inform process.

A lot of content work happens before we have a governance plan in place, including content research and discovery. We need to understand business objectives and website goals, who our users are and what they need, as well as what content we have and how it measures up in terms of quality.

Typically, we gain these insights through stakeholder and user research and qualitative content analysis. However, web analytics can offer tremendous quantitative insights into the content planning process, such as:

  • Informing content stakeholder interviews and user research
  • Validating assumptions
  • Evaluating the impact of content problems
  • Prioritizing content problems and recommendations

Let’s explore how web analytics can be used for research and discovery by tackling some of the big, early content planning questions.

What Are We Trying to Achieve?

How do we prioritize objectives to inform our content plan and validate assumptions?

Web projects often kick off with internal stakeholders describing their objectives — what they want to accomplish with their website. Then comes the work of the strategist to consolidate this feedback and rewrite it as appropriate project objectives.

But how do we prioritize objectives to inform our content plan and validate assumptions? Web analytics can help.

The following is a sample project objective:

Objective: Describe student life culture so prospective students feel comfortable applying online without visiting campus.

As with all analytics insights, it helps to rephrase the objective in the form of a question. It’s hard to find answers without questions. The more specific the questions, the more relevant the (web analytics) answers.

Does student-life content encourage prospective students to apply online?

We can segment student-life content and compare visits with conversions to visits without conversions (visitors who apply vs. visitors who don’t). What content do converting visitors most commonly view? Perhaps student-life content doesn’t impact online conversions as much as expected. Maybe career services should be the content priority.

What student-life content encourages prospective students to apply online?

Evaluate what student-life content has the highest conversion rate. In other words, what student-life content is most often viewed by visitors who apply online? In addition to content topics, consider content types and mediums. Perhaps video has a greater impact. Or, maybe on mobile devices, student demographics are more desirable.

What student-life topics are most important to prospective student applicants?

We can segment visits with conversions and evaluate student-life search queries — internal searches as well as organic search engine keywords. What are the most frequent unique queries? Focus on query topics (rather than on specific content) that lead to conversions as well as those that don’t. Just because the content doesn’t convert a visitor doesn’t mean the related topic isn’t important to them.

Who Are Our Users and What Do They Want?

How do we augment qualitative user research to make insights more comprehensive?

Unlike internal stakeholders, web users (your audiences) are not typically at the ready to inform you about what they want. You have to be proactive to gain this insight. Through use cases, personas and user interviews, you gain insights needed to imagine your users. However, as the user experience consultancy User Intelligence describes, these tactics have some inherent limitations:

  • "Data usually from small numbers"
  • "Most methods take a snapshot in time"
  • "Difficulty to capture some behavior"
  • "Setting sometimes artificial (e.g. lab tests)"

So, how do we augment qualitative user research to make insights more comprehensive?

Referring site traffic

Traffic from referring sites offers context not found with direct traffic. Are visitors coming from social media sites or affiliates? Are there referring sites that are new? The topics associated with referring websites provide insight into the type of content those visitors are interested in. This may provide insight into an emerging audience.

Search engine traffic

Traffic from search engines offers valuable context by indicating the topics your users are looking for through their search terms. Look past the popular branded search terms that likely contain your organization name. What are the popular topics of interest? What are the new topics of interest?

Look for uncommon search terms that may challenge your assumptions about what your users care about. You may discover that your users have more diverse interests than your qualitative user research suggests.

Traffic by geographic location

Segmenting traffic by geographic location allows you to understand where in the world users are coming from. Perhaps you have an emerging out-of-state or international audience. Compare with qualitative insights or dig deeper with other metrics to better understand why.

Traffic by technology

Understanding how users find and access your content provides insight into how they use it. How do these numbers compare to user interview feedback? Perhaps mobile usage or Flash accessibility is a bigger trend than you thought.

What Content Do We Have and Is It Quality?

Web analytics can help identify and evaluate the impact of content problems.

A content audit is a critical early step in the content planning process. It helps us identify what content exists and whether it meets business and user needs. A preliminary quality assessment can be achieved through a ROT content analysis or a more comprehensive evaluation to assess if content is appropriate, useful and usable.

Content audits uncover many problems, including content that is:

  • Redundant
  • Outdated
  • Missing or hard-to-find
  • Confusing

So, after you’ve identified potential content problems, how do you prioritize them to develop content recommendations? Web analytics can help identify and evaluate the impact of content problems.

Redundant content

Redundant content does not always exist because web owners are sloppy. Sometimes it exists in the name of findability. Understanding how and where users are seeking information allows you to organize content by search behaviors.

Compare page views of redundant content along with site search and navigation behavior to learn where people expect to find certain topics. Align these insights with user research to validate or falsify assumptions.

Outdated content

Outdated content does not just mean the copyright in the footer hasn’t been updated; it means content is no longer useful or relevant. These are subjective evaluations, but they can be supported by quantitative analysis.

Compare content to previous peak performance by evaluating page views and search traffic. Have page views steadily decreased over time? How about search traffic? Have people stopped searching for tips on configuring their Palm Pilot to connect to the campus network? (I hope so!)

Missing content

Missing content can take many forms, including broken links, unavailable topics and absent meta tags. Web analytics can help uncover these problems and offer insight into how many users are impacted.

Look at 404 error page views, unsuccessful site searches and “content by title” (to find which pages have missing titles and how popular the pages are).

Confusing content

Confusing content means users don’t know how to comprehend or use the information. Web analytics can help discover potentially confusing content through engagement metrics such as bounce rate, time on page and exit rate. While none of these metrics can detail content comprehension, they may support or challenge qualitative research assumptions.

Web Analytics: Untapped Potential

Web analytics has a lot of potential outside the use of more traditional content governance and measurement. It can provide substantial insight for content planning. The best way to figure out how web analytics can help is by asking questions related to the task at hand — not just at the end of a content project but at the beginning and throughout.

As I mentioned in my recent 2011 CS Forum talk, content strategists are uniquely qualified to make great use of web analytics. We are asking all the pertinent questions to make web analytics useful for discovery, planning and measurement.

I’m curious to hear how others use web analytics in early content planning. If you have some examples, please share them with us!

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About Rick Allen

Rick Allen has worked in higher education for over twelve years, helping to shape web communications and content strategy. As principal of ePublish Media, Inc, a web publishing and content strategy consultancy in Boston, Mass., Rick works with knowledge-centric organizations to create and sustain effective web content. Keep going »


  1. That’s some “ninja” type thinking there, Rick. Well done! Great advice!

  2. Dedrick Sprick says:

    This was excellent overall. I really enjoyed the What Are We Trying to Achieve section that gave specific insight into how analytics can be applied to content strategy around the student application process. Thanks for posting this.

  3. Kate Johnson says:

    An excellent post. Content strategists talk about measurement in general across the board, but you rarely find well-thought-out, specific examples like these. There are endless amounts of data available to us, but it takes a lot of thought to figure out how to wrangle it to get actionable information about content.

  4. Eddie VanArsdall says:

    Very useful information, Rick. Thank you for sharing it.

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