Techniques

Four lenses for surfacing situations from collected signals.

Collecting produces signals. Techniques is where you read them for situations.

A signal on its own shows what happened. A lens shows whether the same conditions keep producing the same result. Each of the four lenses in Situations Techniques approaches that question from a different angle, revealing recurring patterns that individual metrics often hide.

None of the four lenses is better than the others. They ask different questions, surface different layers of context, and are most useful at different points in the work. The strongest situation reads come from pairing at least two lenses and looking for where their findings converge.

The Four Lenses

<table xmlns="http://www.w3.org/1999/xhtml" style="min-width: 509px;"><colgroup><col style="min-width: 25px;"><col style="width: 200px;"><col style="width: 148px;"><col style="width: 136px;"></colgroup><tbody><tr><td colspan="1" rowspan="1"><p><strong>Lens</strong></p></td><td colspan="1" rowspan="1" colwidth="200"><p><strong>Core Question</strong></p></td><td colspan="1" rowspan="1" colwidth="148"><p><strong>Best Used When</strong></p></td><td colspan="1" rowspan="1" colwidth="136"><p><strong>What It Reveals</strong></p></td></tr><tr><td colspan="1" rowspan="1"><p>By Type</p></td><td colspan="1" rowspan="1" colwidth="200"><p>Are users feeling, doing, and performing in ways that reinforce each other — or pulling in different directions?</p></td><td colspan="1" rowspan="1" colwidth="148"><p>You have signals from multiple sources and need to check whether they tell a consistent story.</p></td><td colspan="1" rowspan="1" colwidth="136"><p>Mismatches between perception, behavior, and efficiency.</p></td></tr><tr><td colspan="1" rowspan="1"><p>By Time</p></td><td colspan="1" rowspan="1" colwidth="200"><p>Are early signals predicting what actually happens later — or pointing in the wrong direction?</p></td><td colspan="1" rowspan="1" colwidth="148"><p>You need to distinguish friction that is temporary from conditions that are structural.</p></td><td colspan="1" rowspan="1" colwidth="136"><p>Whether a situation is emerging or already baked in.</p></td></tr><tr><td colspan="1" rowspan="1"><p>By Stage</p></td><td colspan="1" rowspan="1" colwidth="200"><p>Is the situation showing up in testing, in production, or in both — and does the signal hold across stages?</p></td><td colspan="1" rowspan="1" colwidth="148"><p>You want to know if a situation is validated or only visible in controlled conditions.</p></td><td colspan="1" rowspan="1" colwidth="136"><p>How confident you can be that the situation is real at scale.</p></td></tr><tr><td colspan="1" rowspan="1"><p>By Engagement</p></td><td colspan="1" rowspan="1" colwidth="200"><p>Are users connecting with the product at the level of need, behavior, and sustained activity — or breaking down at one of those layers?</p></td><td colspan="1" rowspan="1" colwidth="148"><p>You are seeing adoption or retention signals that do not explain themselves.</p></td><td colspan="1" rowspan="1" colwidth="136"><p>Where in the engagement progression the situation is creating friction.</p></td></tr></tbody></table>

How to Choose a Lens

Start with the question your team is actually asking, not the data you happen to have. Each lens is designed to answer a specific kind of question about why a situation is producing the signals it is.

• Use By Type when your signals feel contradictory. High satisfaction alongside low completion, or strong performance alongside weak retention. By Type reveals whether users feel one thing, do another, and experience a third — which is almost always the shape of a real situation.

• Use By Time when you need to know whether a situation is getting better or worse, or whether an early signal is a reliable predictor of a later outcome. Leading and lagging indicators together show trajectory, not just state.

• Use By Stage when you have signals from testing but are not sure they reflect what will happen in production, or when you have analytics data but no predictive or proxy signal to explain it. By Stage tells you how much confidence to carry into a situation statement.

• Use By Engagement when adoption or retention is the problem but the source is unclear. By Engagement separates situations that break at the point of relevance (does this meet a real need) from those that break at the point of habit (do users keep coming back).

Pairing Lenses

A single lens surfaces a layer. Two lenses surfacing the same recurring condition creates a situation worth naming.

Some pairings work particularly well together:

<table xmlns="http://www.w3.org/1999/xhtml" style="min-width: 453px;"><colgroup><col style="min-width: 25px;"><col style="width: 428px;"></colgroup><tbody><tr><td colspan="1" rowspan="1"><p><strong>Pairing</strong></p></td><td colspan="1" rowspan="1" colwidth="428"><p><strong>What It Reveals Together</strong></p></td></tr><tr><td colspan="1" rowspan="1"><p>By Type + By Time</p></td><td colspan="1" rowspan="1" colwidth="428"><p>Whether a mismatch between perception and behavior is a temporary learning curve or a structural condition that does not resolve.</p></td></tr><tr><td colspan="1" rowspan="1"><p>By Stage + By Type</p></td><td colspan="1" rowspan="1" colwidth="428"><p>Whether a signal seen in testing holds in production, and whether it appears across all three metric types or only one.</p></td></tr><tr><td colspan="1" rowspan="1"><p>By Engagement + By Time</p></td><td colspan="1" rowspan="1" colwidth="428"><p>Where in the engagement progression a situation creates friction, and whether that friction is early-stage or entrenched.</p></td></tr><tr><td colspan="1" rowspan="1"><p>By Type + By Engagement</p></td><td colspan="1" rowspan="1" colwidth="428"><p>Whether users who say they value the product actually use it, and whether those who use it keep coming back.</p></td></tr></tbody></table>

What a Strong Situation Read Looks Like

A situation is ready to name when at least two lenses point to the same recurring condition across different users, sessions, or audience segments.

For example: By Type shows high comprehension but low completion. By Time shows that early prototype signals predicted the drop-off accurately. The situation is not a usability problem. Users understand the interface. They are stopping for a different reason, one that the surrounding context can explain.

That convergence is what moves a signal from an observation to a situation. And a named situation is what Measure can actually test.

Proof in Practice

A team working on a SaaS onboarding flow had strong leading indicators from prototype testing: high desirability, consistent task completion in beta. They nearly moved to launch.

Running By Time alongside By Engagement revealed the gap. Lagging indicators showed adoption plateauing within two weeks. By Engagement showed needs-based metrics holding but activity-based metrics dropping sharply after the first session.

The situation was not that users disliked the product. They liked it in the moment but never built it into their workflow. That is a different situation than a usability failure, and it requires a different design response.

Two lenses. One situation. A decision that would have been missed with either lens alone.

Moving Through the Lenses

Each lens is a separate section with its own explanation, examples, and checklist.

• By Type covers attitudinal, behavioral, and performance signals, and how mismatches between them reveal the shape of a situation.

• By Time covers leading and lagging indicators, and how pairing them shows whether a situation is emerging or already established.

• By Stage covers predictive, proxy, and analytics signals, and how the stage a signal comes from changes how much confidence it carries.

• By Engagement covers needs-based, behavior-based, and activity-based metrics, and how the layer where engagement breaks points to the underlying situation.