You’re trying to evaluate whether there’s real, measurable interest in a feature you’re considering for your product.

Most PMs waste weeks analyzing the wrong signal. You build elaborate dashboards tracking user behavior, only to launch a feature that nobody uses. Or worse—you dismiss an opportunity because your data told you there was no interest, when the real problem was how you measured interest in the first place.

Your "interest" metric is probably lying to you

Here's why: Translating raw behavior into interpretable signals of user interest requires navigating fundamental tradeoffs. Make the wrong choices, and you'll either overestimate demand (wasting engineering resources) or underestimate it (missing transformative opportunities).

Let's fix that.

Here's a case study

Imagine you're a product manager for a browser. You want to know if there's real user interest in a "Restore Tabs from Last Session" feature—something that automatically brings back the tabs a user had open before closing their browser.

Your first instinct might be: "I'll label anyone who opens their browsing history after starting a new browser session as interested." Seems reasonable. But dig deeper and you'll find this definition is riddled with problems:

  • What about users who check history to find a page they accidentally closed days ago? (false positive)
  • What about users who already use session restore extensions, so their use case for visiting History is entirely different? (false negative)
  • What about missing out on users who simply leave their browser open 24/7 to avoid losing tabs? (false negative)

This is where label design gets interesting—and consequential.

Understand your organization before solutioning

Before you define "interest" understand the organizational context you are operating in. Most product teams find themselves in one of two scenarios:

  1. You’re evaluating whether to invest: You're in exploration mode, weighing different paths or testing early hypotheses. Your questions:
    • What’s the total addressable market?
    • How much do users care about this feature?
    • Which user segments would benefit most? Your goal: Size the opportunity and build conviction (or kill the idea early).
  2. Leadership has decided to invest in this path: The feature is green-lit—maybe from a visionary exec or competitive pressure. You're not questioning whether, but who and how much. Your goal: Target the right users and sequence the rollout intelligently.

The scenario dictates which tradeoffs matter most. In Scenario 1, you need directionally correct signals fast. In Scenario 2, you need precision to avoid wasting your launch budget on the wrong segments.

Four Critical Trade-offs

1. Precision vs. Recall

This is the foundational tension in any labeling system.

  • Precision-focused approach: Only label users who are very likely interested. For example: Label users who opened History within 5 minutes of a new session and have 10+ tabs open regularly
    • ✅ Target the right users, don't waste resources.
    • ❌ Miss many potentially interested users (false negatives).
    • Ask yourself: What if some users reopen their history for reasons unrelated to restoring tabs — like finding a page they accidentally closed days ago? (→ false positives, overestimating interest) When to optimize for precision: Late-stage launches, resource-constrained teams, monetization features where targeting matters
  • Recall: Cast a wide net.
    • ✅ Don't miss potential adopters.
    • ❌ Waste resources on uninterested users that may dilute your experiments. -Ask yourself: What if others use session restore extensions or leave the browser open all the time — so they never open History, even though they’d love this feature? (→ false negatives, underestimating interest) When to optimize for recall: Early exploration, low-cost features, platform capabilities that benefit from network effects.

2. Simplicity vs. Accuracy

Simple definitions are easy to explain, track, and debug. Complex models capture nuance but become black boxes.

  • Simplicity: Simple definitions are easy to explain, track, and debug. Complex models capture nuance but become black boxes.

    • ✅ Easy to comprehend, communicate, and track over time.
    • ✅ Less prone to overfitting
    • ❌ Misses complex user behaviors patterns.
    • Ask yourself: What if a single behavior (opening History) isn't enough - should we combine it recently closed prior Window? When to use: Initial exploration, communicating with executives, building your first MVP

  • Accuracy: Define a complex model with many features and sophisticated criteria.

    • ✅ More accurate predictions.
    • ❌ Hard to interpret, maintain, debug.
    • ❌ May overfit to historical data.
    • Ask yourself: What if this model is too complex to communicate with stakeholders? When to use: After validating simple approaches, when marginal accuracy gains justify complexity costs

  • 💡 Low effort high impact move: Start simple. Only add complexity when you can answer "yes" to all three:

    • The simple version has been validated and shows promise
    • You have reliable data infrastructure for all required signals
    • The incremental accuracy gain justifies the maintenance cost

2. Current vs Future Potential

The most interested users might not look interested yet.

  • Current behavior approach: Label based on what users actually do today

    • ✅ Easy to measure with available data.
    • ❌ Biased toward existing power users.

  • Future potential approach: Label based on context and jobs-to-be-done, even if current behavior doesn't show it

    • ✅ Captures untapped markets.
    • ❌ Difficult to measure with available user behavior data therefore can be more speculative.

  • 💡 Low effort high impact move: Start with collecting existing data as your baseline and then combine with qualitative analysis to paint a cohesive picture.

3. Static vs Dynamic Labels

User behavior isn't constant. Students need tab management during finals week but not during summer break. Remote workers need it during project sprints but not during vacation.

  • Static approach: Compute labels once, treat as constant

    • ✅ Faster and easier to implement.
    • ✅ Sufficient for early stage evaluation of user interest.
    • ❌ Misses on user behavior changes over time. For example in context of recommendation algorithms static labels can become outdated quickly.

  • Dynamic approach: Recompute interest labels on a recurring basis.

    • ✅ Captures evolving user behavior.
    • ❌ Difficult and costly to implement and maintain.
    • ❌ Harder to debug ("why did this label change?")
    • 💰 Low effort high impact move: Start with static labels and move to dynamic labels later.

  • 💡 Low effort high impact move: Start with collecting static labels to gain initial insights. Decide later if it makes sense to invest in building dynamic labels.

Other Considerations for Building Trust in Your Data

Even with perfect label design, your analysis can mislead you. Guard against these common pitfalls:

Use the Right Data Sources

If your feature is desktop-only, analyzing mobile users is noise. Sounds obvious, but it happens constantly.

Balance Depth Against Speed

You can always spend more time collecting more accurate data. The question is whether the incremental accuracy is worth the delay.

Validate with Experimentation

The ultimate test of your labels: Do users you labeled as "interested" actually adopt the feature?

Your Labels Are Hypotheses, Not Truth

The most dangerous assumption in product analytics is treating your labels as ground truth. They're not. They're your best guess at translating messy human behavior into clean signal.

Stay humble. Validate early. Iterate often.

And remember: A simple label that you trust is often more valuable than a complex model you don't understand.