metrics

How to Set OKRs for Product Teams Without the Performative Theater

OKRs fail because teams optimize for the score instead of the outcome. The core skill is writing OKRs that align incentives and remain true when the business changes.

Timoté Geimer · · 14 min read

The Core Answer

OKRs work when you write Objectives that reflect what customers and the business actually need, and Key Results that measure whether you’ve delivered value (not whether you’ve optimized the scoring system). The structural error most teams make is writing KRs that are too easy to hit through gaming or effort, rather than outcome-driven. A defensible OKR pair looks like: Objective: “Users can deliver value in half the time” → Key Result: “Time-to-first-insight decreases from 4 hours to 90 minutes (measured on user cohort)” rather than “Objective: Improve onboarding” → “KR: 50% of users complete onboarding in first session.” The second set can be hit by re-ordering form fields. The first requires actual product work. The discipline is writing OKRs so specific that the business can’t ignore a failure to hit them, and so tied to user/customer value that hitting them is worth the work.

Why Most OKR Programs Fail

Teams treat OKRs as annual performance theater rather than strategic navigation tools. This happens because OKRs are typically:

Too vague. Objective: “Improve user engagement.” Key Result: “Increase DAU by 15%.” This can be achieved by 10 different product changes, none of which might matter. The team optimizes for the metric rather than the outcome. Nobody knows if engagement improvement means customer value or churn reduction.

Gamed. Smart teams learn to write OKRs they can hit. They aim low, move the goalpost quietly, or measure the thing that’s easiest to move rather than the thing that matters. By Q3, the OKR program becomes a forecasting exercise: teams estimate what they’ll accomplish, then call it a “commitment.”

Disconnected from user behavior. KRs like “Reduce bug count by 30%” or “Increase code test coverage to 85%” measure process health, not customer value. There’s no mechanism forcing the business question: “Does this KR actually change anything for users?”

Written top-down with no feedback loop. Leadership sets the OKRs; teams execute. When business conditions change (market shift, competitive threat, customer feedback), OKRs don’t adapt. Teams stay locked into quarter-old assumptions.

The Anatomy of a Defensible OKR

A high-quality OKR has three properties:

1. Objective is stated in outcome terms, not activity terms.

Weak: “Increase feature adoption.” Strong: “Reduce time for users to derive value from advanced features.”

The weak objective tells you what to do (push adoption), not what you’re trying to achieve (faster time to value). The strong one constrains the solution space. It says: whatever you ship, it needs to make it easier for users to get value. That could be better UX, better documentation, better defaults, or a guided workflow. You don’t know which yet.

2. Key Results are specific enough that you can know whether you’ve succeeded.

Weak: “Increase feature adoption by 20%.” Strong: “Reduce median time from signup to first use of advanced features from 6 weeks to 2 weeks (measured on cohorts of 100+ users).”

The weak KR can be hit by lowering your definition of “adoption” (calling it adoption if they click on it, even if they don’t use it). The strong KR forces precision: you have a specific metric, a specific target, and a specific population being measured.

3. Key Results reflect user/customer value, not internal effort.

Weak: “Complete implementation of feature X by June 30.” Strong: “Customers report 40% reduction in manual work for workflow Y (measured via NPS follow-up question and usage data).”

The weak KR measures whether you shipped something. The strong one measures whether the customer got value from it. The difference is enormous. Many features get shipped and never used.

How to Write OKRs That Don’t Get Gamed

The core discipline is making it harder to game the metric than to achieve the outcome.

Start with the customer behavior you want to change:

  • What are users doing today that is painful?
  • What would it mean if we solved that pain? (behavior shift, time reduction, capability expansion)
  • How would we know if users were actually experiencing less pain?

Then design the KR backward from that signal.

Example:

  • Customer behavior: Sales reps spend 4 hours per day on manual CRM entry instead of customer conversation.
  • Success condition: Sales reps spend 1 hour per day on entry (and conversation time increases accordingly).
  • Measurable signal: Average time-in-field-entry per rep per week decreases from 20 hours to 5 hours (measured on reps who’ve used the system for 4+ weeks, excluding training cohort).

This KR is hard to game because:

  • It’s specific to an existing workflow and population
  • It measures behavior change, not feature adoption
  • It excludes the training period (people who just started use it differently)
  • You’d have to actually solve the user’s problem to hit it

Avoid metrics that move easily without user value:

Bad KRs move when you game the metric. Good ones move when you deliver value.

Examples of gameable KRs:

  • “Increase MAU by 30%” (sign up users and don’t charge them yet)
  • “Increase time-in-app by 25%” (add friction, dark patterns, autoplay)
  • “Improve NPS by 10 points” (just survey happy customers, not churned ones)

Examples of harder-to-game KRs:

  • “Reduce churn rate from 8% to 5% month-over-month (measured on cohort that has been with us 6+ months)” (requires retention, not signup)
  • “Revenue per engaged user increases from $50 to $75” (requires actual monetization leverage, not user volume)
  • “Support ticket volume decreases from 300 to 200 per week while NPS stays stable or improves” (you can’t reduce tickets by abandoning users)

Common Mistakes in OKR Design

Mistake 1: Confusing leading and lagging indicators. “Increase feature usage” is a leading indicator (it happens first). “Customer retention improves” is the lagging outcome you actually want. If you set OKRs on leading indicators, you measure activity, not impact. Set OKRs on lagging indicators (outcomes), use leading indicators as diagnostic tools.

Mistake 2: Setting too many OKRs. Three to four OKRs per team is the hard ceiling. More than that, and you’re not prioritizing; you’re listing everything you want to do. Teams with 8-10 OKRs hit them all or miss them all—the OKRs aren’t driving behavior, they’re just documentation.

Mistake 3: OKRs that are “just the roadmap.” If your OKRs are “Ship Feature A, Ship Feature B, Ship Feature C,” they’re not OKRs. They’re a feature list. OKRs are outcome-focused. Features are input. Reframe: “Ship Feature A because it will reduce user onboarding time by X%” or “Ship Features A and B together to increase workflow efficiency.” Connect the feature to the outcome.

Mistake 4: Setting OKRs without understanding current state. “Increase retention by 20%” means nothing if you don’t know what current retention is, how it’s measured, or what segments you’re looking at. Before setting OKRs, audit your metrics infrastructure: What do we actually measure? What can we measure? Do we trust the data? Most teams don’t ask this.

How to Apply This

Before setting OKRs:

  1. Audit your metrics infrastructure. What customer behaviors can you actually measure (retention, usage depth, revenue per user, support volume, etc.)? What’s your data confidence level?

  2. Map product opportunities to customer pain points. For each major pain point, ask: “What would it mean if we solved this? How would behavior change? How would we know?”

  3. Set the baseline. For each potential KR, measure it today. You cannot set a credible goal without knowing where you’re starting from.

During OKR setting:

  1. Write Objectives in outcome language, not activity language. (“Reduce friction in X workflow” not “Increase X feature adoption.”)

  2. For each Objective, write 1-3 Key Results that measure whether you’ve achieved the outcome. Make them so specific that you’ll know conclusively whether you’ve succeeded.

  3. Test each KR for gameability: “Could our team hit this KR without delivering user value?” If yes, rewrite it.

  4. Share OKRs with customers or customer-facing team members. Ask: “Does this resonate? Is this the right problem to solve?” Use their feedback to refine.

  5. Limit to three to four OKRs per team. Anything more is noise.

During execution:

  1. Track KRs weekly, not just at the end of the quarter. If a KR is off-track at week 4 of a 13-week quarter, you have time to adjust.

  2. If business conditions change, re-baseline. You’re not trying to hit an arbitrary number; you’re trying to deliver value. If the market shifts or a competitor moves, your OKRs should reflect that.

  3. At the end of the quarter, measure truthfully. If you missed a KR, the question is “Why?” (execution, assumption, priority shift), not “How do we redefine success?”

The Bottom Line

OKRs work when they connect team effort directly to user or customer value, and when they’re specific enough that gaming requires delivering actual impact. The discipline is writing Objectives that reflect real customer problems, Key Results that measure whether you’ve solved them, and having the rigor to re-baseline when conditions change rather than pretending last quarter’s OKRs still matter. Organizations that sustain growth are the ones that run OKRs as a navigation tool (telling you if you’re moving toward value) rather than as performance theater (scoring points and moving on).