# OKR Writing Guide: Templates, Examples, and Anti-Pattern Checklist ## Part 1: OKR Worksheet Template ### Step 1: Write Your Objective **Objective Template:** ``` [Verb] [Desired business/customer outcome] [by/through] [mechanism or constraint] ``` **Examples:** - Lock in daily habit loops among power users - Reduce onboarding friction for first-time users - Shift customer spend from fixed to usage-based models - Own the SMB market for workflow automation **Guidelines:** - 5-10 words maximum - Should inspire and be memorable - Use strong verbs: "increase," "decrease," "shift," "own," "reduce," "expand," not "improve" or "enhance" - Should describe outcome, not activity ### Step 2: Define Your Key Results **Key Result Template:** ``` [Metric name]: Move from [current state] to [target state] by [date] ``` **Examples:** - 30-day retention: Increase from 52% to 65% by Q2 end - Time to first success: Reduce from 45 min to under 20 min by Q2 end - Expansion revenue: Grow from $12k/month to $25k/month by Q2 end - Session frequency: Increase from 3.2 sessions/week to 5 sessions/week by Q2 end **Guidelines:** - 3-4 key results per objective maximum - Each KR should be independently measurable (no "and") - Should describe outcome, not deliverable - Should require cross-functional effort or organizational focus - Metric must be something that changed in customer/business behavior ### Step 3: Apply the Behavior Change Test **For each key result, answer:** 1. If we hit this KR, what will customers/users actually do differently? 2. Can we measure this objectively? 3. Is this outcome or output? 4. Will achievement of this KR require hard decisions or trade-offs? **If you can't answer all four clearly, rewrite the KR.** ### Step 4: Define Measurement Plan For each KR, document: ``` Metric: [Name] Definition: [Exactly how we calculate this] Data source: [Where this lives, Mixpanel, Amplitude, Stripe, etc.] Current state: [Today's number] Target: [Q-end number] Confidence: [High/Medium/Low on whether we can influence this] Owner: [Who's responsible for this KR] Dependencies: [What has to happen first?] ``` --- ## Part 2: Real OKR Examples by Category ### Category 1: Activation (Free to Paid / Signup to First Success) #### Example 1A: B2B SaaS (Time to Value Focus) ``` OBJECTIVE: Get new users to their first win in under 30 minutes Key Result: Reduce time from signup to first successful action from 45 min to under 25 min Metric: Onboarding completion time (from signup to first task created) Current: 45 min average Target: 25 min average Owner: Product (onboarding experience) Data source: Mixpanel onboarding funnel Key Result: Increase percentage of signups who complete first success scenario in first session from 28% to 50% Metric: First session success rate (% of signups who reach milestone X in same session they signed up) Current: 28% Target: 50% Owner: Product (user research, onboarding flow) Data source: Amplitude or Mixpanel Key Result: Achieve 85% satisfaction rating from users on their onboarding experience (vs. current 62%) Metric: Post-onboarding NPS or CSAT (asking "How easy was it to get started?") Current: 62% Target: 85% Owner: Product (customer voice), Support (feedback collection) Data source: Intercom or SurveyMonkey post-onboarding survey ``` #### Example 1B: Freemium SaaS (Free-to-Paid Conversion) ``` OBJECTIVE: Convert power-users-in-waiting before they leave Key Result: Increase free trial conversion rate from 18% to 28% Metric: Trial users who convert to paid / Total trial users started Current: 18% Target: 28% Owner: Product (upgrade experience), GTM (pricing strategy) Data source: Stripe + analytics integration Key Result: Increase percentage of users who reach "power user threshold" (5+ features used) from 31% to 50% Metric: Users who use 5+ distinct features in first 7 days / Total activated users Current: 31% Target: 50% Owner: Product (feature discovery, UX) Data source: Amplitude feature adoption Key Result: Reduce time from signup to "conversion-ready" signal from 12 days to 6 days Metric: Days from signup to using 3+ key features Current: 12 days Target: 6 days Owner: Product (engagement loops), GTM (nurture messaging) Data source: Amplitude cohort analysis ``` ### Category 2: Retention (Activation to Sticky Behavior) #### Example 2A: Daily Active Product (Habit Building) ``` OBJECTIVE: Lock in daily habit loops for power users Key Result: Increase 30-day retention for activated users from 58% to 72% Metric: Users active on day 30 / Users activated in cohort (Day 1 active) Current: 58% Target: 72% Owner: Product (engagement design) Data source: Amplitude cohort retention Key Result: Increase average session frequency from 3.8 sessions/week to 5.2 sessions/week Metric: Total sessions / Active users / 7 (for weekly average) Current: 3.8 Target: 5.2 Owner: Product (engagement loops, notifications) Data source: Mixpanel or Amplitude event tracking Key Result: Decrease day-7 churn for high-intent users from 15% to under 8% Metric: Users with 5+ sessions in first 7 days who don't return on day 8+ Current: 15% churn Target: 8% churn Owner: Product (aha moment reinforcement) Data source: Custom cohort analysis in analytics tool ``` #### Example 2B: B2B Retention (Power User Stickiness) ``` OBJECTIVE: Become the collaboration hub for distributed teams Key Result: Increase account expansion rate (accounts using 2+ key features) from 42% to 65% Metric: Accounts using power user features / Total active accounts Current: 42% Target: 65% Owner: Product (feature adoption), CS (adoption support) Data source: Mixpanel account-level analytics Key Result: Reduce monthly churn for accounts with power-user behavior to under 2% Metric: Accounts with 10+ logins/month that churn in month N Current: 4.2% Target: 2% Owner: CS (support), Product (retention features) Data source: Custom segment in analytics Key Result: Increase seat expansion per account from 4.2 seats to 6.8 seats for retained accounts Metric: Average seats per account (for accounts active for 6+ months) Current: 4.2 Target: 6.8 Owner: Product (multi-team features), CS (expansion) Data source: Stripe + CRM ``` ### Category 3: Revenue (Monetization and Expansion) #### Example 3A: Expansion Revenue (Upsell and Cross-Sell) ``` OBJECTIVE: Shift customer spend from fixed pricing to usage-based expansion Key Result: Increase net MRR growth from expansion revenue from $8k/month to $18k/month Metric: MRR from accounts that existed prior quarter / Total new MRR Current: $8k/month Target: $18k/month Owner: Product (usage-based features), GTM (expansion motion) Data source: Stripe + ChartMogul Key Result: Increase percentage of enterprise customers spending under $5k annually who upgrade to $10k+ within 6 months from 12% to 30% Metric: Accounts in cohort at year end spending 2x+ their starting rate Current: 12% Target: 30% Owner: CS (land-expand motion), Product (usage tracking) Data source: CRM + Stripe Key Result: Increase ARPU from $180 to $240 for power user segment Metric: Annual revenue per account / Number of active accounts (power user tier) Current: $180 Target: $240 Owner: Product (premium features), GTM (pricing) Data source: Stripe or accounting system ``` #### Example 3B: Enterprise Revenue (Land and Expand) ``` OBJECTIVE: Build predictable enterprise revenue through adoption-driven expansion Key Result: Land 8 new enterprise customers (TCV greater than $50k) by Q-end Metric: Signed contracts $50k+ TCV Current: [Count this quarter to date] Target: 8 Owner: Sales, Product (enterprise features) Data source: Salesforce Key Result: Increase net revenue retention to 115% for existing enterprise accounts Metric: (Revenue from existing accounts at end of period - Churn) / Revenue at start of period Current: [103%] Target: 115% Owner: CS (adoption), Product (roadmap prioritization for enterprise) Data source: Accounting system Key Result: Reduce time to $10k ARR for new enterprise customers from 5 months to 3 months Metric: Median time from contract signature to $10k ARR across cohort Current: 5 months Target: 3 months Owner: CS (onboarding), Product (value realization) Data source: CRM + Stripe ``` --- ## Part 3: Anti-Pattern Checklist ### Critical Anti-Patterns (If you see these, rewrite) - [ ] **Output Disguised as Outcome** ("Ship dashboard redesign," "Build 3 integrations") - Fix: Describe what changes in user/business behavior, not what gets built - [ ] **Unmeasurable Objective** ("Improve product quality," "Better UX," "Enhance collaboration") - Fix: Define the metric. What specifically improves and how do you measure it? - [ ] **Too Many OKRs** (More than 5 objectives or 15+ key results per function) - Fix: Ruthlessly prioritize. Choose what matters most this quarter. - [ ] **No Target/Baseline** ("Increase retention" without saying from X to Y) - Fix: Always specify current state and target state with numbers. - [ ] **Outcome You Don't Actually Control** ("Increase market share," "Win more enterprise deals") - Fix: Choose outcomes your team can influence. Maybe "Increase enterprise feature adoption" instead. - [ ] **KR Doesn't Require Hard Decisions** (Should cost you something to hit it) - Fix: If you can hit all your KRs by doing what you were going to do anyway, they're too easy. ### Measurement Anti-Patterns - [ ] **Metric with no data source** ("Increase customer happiness", where's the data?) - Fix: Name the tool. Specify exactly how you calculate it. - [ ] **Lagging indicator only** (Churn rate measured 30 days after quarter ends) - Fix: Include leading indicators you can track weekly (activation rate, feature adoption, NPS). - [ ] **Percentages without context** ("Increase conversion by 15%") - Fix: Convert percentages to specific numbers for clarity. - [ ] **Vanity metrics** (Total signups, total page views, raw DAU) - Fix: Use metrics that describe business health (retention, activation rate, revenue per cohort). ### Organizational Anti-Patterns - [ ] **Top-down OKRs with no input from teams** (Leadership writes them in a vacuum) - Fix: Team input is critical. They understand constraints and opportunities. Facilitate, don't dictate. - [ ] **Completely siloed OKRs** (No dependency mapping, functions working at cross-purposes) - Fix: Map dependencies. Where is Product blocking GTM? Where is GTM blocking Product? - [ ] **OKRs used as performance reviews** (Hitting OKRs ties to bonuses/promotions) - Fix: OKRs are learning tools, not scorecards. If people are sandbagging or sandblasting, they're too tied to compensation. - [ ] **Zero mid-quarter adjustment** ("We're locked in for 12 weeks no matter what") - Fix: Review at week 6-7. If something is clearly impossible or irrelevant, adjust. - [ ] **No retrospective** (Quarter ends, OKRs written for next quarter, no reflection) - Fix: Spend an hour. What did we learn? What surprised us? Apply it to next quarter. --- ## Part 4: OKR Scoring and Grading Rubric ### How to Score at Quarter End **Scoring scale:** - 1.0 = Achieved 100% - 0.7 = Achieved 70% - 0.5 = Achieved 50% - 0.3 = Achieved 30% - 0.0 = Achieved 0% **Overall OKR health:** - 0.7 to 1.0 per KR = Healthy calibration (average 0.75 across all KRs is target) - Under 0.5 average = You were too ambitious OR you had execution problems OR your assumptions were wrong - 1.0 across the board = You sandbagged ### Mid-Quarter Check-In Template (Week 6-7) For each key result, answer: ``` KR: [Key result name and target] Current state: [Today's number] Pace: [On track / At risk / Off track] Confidence we hit target: [High / Medium / Low] Why: [If not on track, what's the blocker?] Action: [Do we adjust the KR? Do we add resources? Do we descope?] ``` **Example:** ``` KR: Increase 30-day retention from 52% to 65% Current state: 55% (Week 6) Pace: On track (we're at 55%, need 65%, we're at 85% of target with 50% of time remaining) Confidence: Medium (dependent on churn-mitigation feature shipping on time) Why: Feature shipped late. We're running the experiment now, but won't have full cycle of data until week 10. Action: Keep the KR. Negotiate a 62% target instead of 65% if we need to descope engineering work. ``` ### Quarter-End Retrospective Template (Week 13) ``` 1. RESULTS - How many KRs did we hit? [X out of Y] - What was our average score? [0.72 out of 1.0] 2. ANALYSIS - Which KRs did we nail? Why? - Which KRs did we miss? Why? - Were we too ambitious? Too conservative? Right-sized? - Did we learn anything that changes our next quarter? 3. LEARNINGS FOR NEXT QUARTER - What should we do more of? - What should we do less of? - What assumptions were wrong? - What surprised us? 4. CALIBRATION ADJUSTMENT - How ambitious should Q-next be? Same level? Higher? Lower? - Do we need better leading indicators to track earlier? - Do we need different metrics to measure what actually matters? ``` --- ## Part 5: Quick Reference Card ### OKR Format at a Glance ``` OBJECTIVE: [Outcome you want to achieve] Key Result 1: [Metric] from [current] to [target] Key Result 2: [Metric] from [current] to [target] Key Result 3: [Metric] from [current] to [target] ``` ### The Behavior Change Test (In One Sentence) If the KR is achieved, will a customer/user/business decision-maker actually behave differently? If yes, it's a KR. If no, it's a task. ### One OKR Per Function (For Q2 2026, as example) ``` PRODUCT: Objective: Increase time-to-value and activate more power users KR1: 30-day retention from 58% to 68% KR2: Time to first success from 35 min to 18 min KR3: Session frequency from 3.2/week to 4.8/week ENGINEERING: Objective: Stabilize infrastructure and reduce technical debt KR1: Error rate from 1.2% to under 0.6% KR2: P99 latency from 1,200ms to under 800ms KR3: Tech debt reduction (20% of roadmap capacity shipped) GTM: Objective: Expand market presence in mid-market segment KR1: Close 12 mid-market new customers KR2: Improve sales cycle from 60 days to 45 days KR3: Increase qualified pipeline by 40% CUSTOMER SUCCESS: Objective: Drive adoption and expansion for existing customers KR1: Net revenue retention to 115% (from 108%) KR2: Accounts with power-user behavior from 35% to 55% KR3: Customer satisfaction from 7.2 to 8.1/10 ``` ### Remember - OKRs are not a straightjacket. They're a North Star that guides decisions. - Mid-quarter adjustment is not failure. It's learning. - Good OKRs create clarity. If people are confused about priorities, your OKRs are too vague. - The process of writing OKRs is more valuable than the OKRs themselves. You'll learn what your team actually thinks matters.