Analytics apps don't tell you much about usage behavior. You might be able to see how many users performed an event, or how many times they did it. But none of the analytics packages out there are good at showing you how often people do things. Are they using to-dos once a week? Every day? Only signing into the app once a month but happily paying for years?
Time matters. You can't understand usage without time.
Here I describe an approach for defining new information architectures for large organizational websites managed by many stakeholder groups.
Broadly speaking, there are four general phases to the approach:
Auditing. Begin by immersing yourself in existing content and encourage stakeholders to adopt a critical, audience-minded perspective of their content.
Diagramming. Work with stakeholders to develop new conceptual categories that better serve audiences and organizational direction.
Elaborating. Think through content in detail and test new categories against specific instances and edge cases.
Producing. Prepare content teams for production using a shared database of new sitemap pages and editorial considerations that you’ve developed incrementally.
Sometimes there’s a Heuristic That Almost Always Works, like “this technology won’t change everything” or “there won’t be a hurricane tomorrow”.
And sometimes the rare exceptions are so important to spot that we charge experts with the task. But the heuristics are so hard to beat that the experts themselves might be tempted to secretly rely on them, while publicly pretending to use more subtle forms of expertise.
…Maybe this is because the experts are stupid and lazy. Or maybe it’s social pressure: failure because you didn’t follow a well-known heuristic that even a rock can get right is more humiliating than failure because you didn’t predict a subtle phenomenon that nobody else predicted either. Or maybe it’s because false positives are more common (albeit less important) than false negatives, and so over any “reasonable” timescale the people who never give false positives look more accurate and get selected for.