PRPL CS Content-Counting Template

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The following is a repost of a blog post that was originally made here, on Medium.

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I had a miserable time showing my work in math class. Usually, I used ā€œcommon sense,ā€ context, process of elimination, or guessed. I preferred English. If there was no real way to quantify an argument, how could you be wrong?

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So I latched onto information architecture (IA), a soft science, rather than, say, architecture, which lives on mathematical precision that literally makes or breaks a project. But the more I work with data, the more we measure, and the more we test, Iā€™ve suspected that maybe information can be quantified, too.

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Is content data? Not really. A few years ago, I would have eye-rolled at calculating content in order to find the ā€œrightā€ answer. But with IA-heavy projects such as redesigns for thousand-page websites, the scale of information begs for process. I decided to scope out an approach where we pretend content is data. Information can be corralled into topics, and I started counting how often each topic showed up in auditing and research. If certain topics leap out of the data, thatā€™s where your content strategy should start.

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Who should count content?

The basic answer would be content strategists. The real answer is anyone wrangling incredible amounts of web-based information. For the purposes of this article, itā€™s UX designers who already have a solid grip on user research, decent exposure to IA, and intermediate spreadsheet skills.

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As an exercise, what follows is a sample set of rationale and steps for a standard .edu website redesign.

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Why count content?

Actually, weā€™re counting ā€œconceptsā€ā€Šā€”ā€Šhypothetical content which we can infer from research and auditing. Each concept is a common theme, topic, object, or subject that succinctly represents user-facing information.

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Letā€™s say surveys indicate college students want to ā€œdecide what to major in.ā€ Relevant concepts on a site youā€™re planning might include ā€œcoursesā€ and ā€œfaculty.ā€ To count the global relative importance of faculty-related content versus course content, youā€™ll want to pin up all the research you have, then count how often ā€œcoursesā€ and ā€œfacultyā€ are suggested. From this example, you might resolve to feature faculty profiles higher on the ā€œAcademicsā€ landing page, or justify pulling detailed course descriptions into the CMS.

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The goal is to inform content strategy through data, rather than pre-existing assumptions, opinions, and beliefs about content.

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If you arenā€™t quantifying content in some way, youā€™ll notice patterns you wanted to find. Your biases will lead you straight to takeaways that validate what you already comped. When you start from a vision youā€™re emotionally attached to, too much is at stake to prove yourself wrong.

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Hopefully, this approach makes the data-to-content translation process less daunting and more manageable. It definitely worked for me. At worst, weā€™ll stop spending weeks collecting great, quantifiable, objective numbers, only to undermine the sterile purity of the data by letting our visions cherry-pick stats into stories. At best, weā€™ll weave content decisions from numbers into words, like a tiny chunk of a Rosetta Stone between math and English.

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Updated April 17, 2017 at 4:06 AM
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Tricia D'Antin

Director of Research & Product Strategy at Purple Rock Scissors. Runs on German beer and cookies.
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