Draft OKRs

MEMORANDUM

TO: Grace Mensah, Chief Product Officer FROM: Staff Product Manager DATE: October 3, 2024 SUBJECT: Q3 OKR Post-Mortem & Proposed Q4 Product OKRs

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1. The Disconnect: What Q3 OKRs Actually Tell Us

At our recent all-hands, a product OKR score of 0.81 was celebrated as a “great quarter.” In reality, Q3 was a business failure: * Net Revenue Retention (NRR) dropped from 103% to 102% (Target: 108%). * Quarterly Logo Churn rose from 3.1% to 3.4% (Target: 2.5%).

The 0.81 score is a vanity metric masking acute operational disconnects across four distinct areas:

``` [Output Orientation: 9/16 KRs] ──> Shipped features, missed business outcomes [Metric Manipulation] ──> Completion rate rose via relaxed definition (100% -> 80%) [Activation Breakdown] ──> 31 days to 1st learner (Goal: 14) fuels 45% of churn [Premature Expansion] ──> Pushed upgrade prompts while seat activity fell to 29% ```

#### A. Feature Delivery Was Confused with Value Creation Nine of our 16 Key Results were binary outputs (shipping features or running counts). Teams scored 1.0 on outputs—shipping offline mode, streaks, dashboards, upgrade prompts, and integrations—while customer-facing outcomes collapsed: * Learner App scored 1.0 on features, yet Weekly Active Learners per seat fell from 0.31 to 0.29 (Target: 0.40). * Growth scored 1.0 on experiments and upgrade prompts, yet Seats added in existing accounts fell from 9,200 to 8,100 (Target: 14,000). * Admin delivered 3 HRIS integrations (misclassified as an “outcome”), but the actual outcome—Days from contract to first learner—only improved from 34 to 31 days (Target: 14).

#### B. Vanity Gains and Metric Redefinition Content claimed a 1.0 score by driving Course Completion Rate from 41% to 58% (Target: 55%). However, this was largely engineered by lowering the completion threshold on August 1 from 100% of modules to 80%. Artificially inflating course completions obscured stagnant baseline learner demand.

#### C. Product Neglect Directly Fed Q3 Exit Drivers Our exit interviews across 38 churned accounts reveal direct causation with our product failures: 1. "Our people didn’t use it" (17 accounts / 45%): Directly caused by dropping learner engagement (0.29 weekly active learners per seat). Gamification (streaks/badges) failed to solve core utility. 2. "Moved to HR system’s LMS" (7 accounts / 18%): While we launched 3 HRIS integrations, we treated them as tick-box deliverables rather than driving customer activation. 3. Budget cuts (9 accounts / 24%): When CFOs review seat utilization below 30%, unused seats are cut at renewal. 4. Onboarding Lag: Accounts reaching first learner within 14 days churn at one-third the rate of slower accounts. By taking 31 days to activate, Admin is losing accounts before they even start.

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2. Strategic Narrative for Q4

We cannot expand accounts that are not using the product. To move NRR to 106% and logo churn down to 2.6%, the product strategy must execute in strict sequence across the account lifecycle:

``` [Admin: Days 1–14] [Learner App: Days 15–365] [Growth: Renewal & Expansion] Compress onboarding to <14d ──> Drive habitual weekly usage ──> Monetize 80%+ utilized accounts (Reduces churn by 66%) (Eliminates "nobody used it") (Reverses NRR contraction) ```

1. Admin (Time-to-Value): Compress onboarding to under 14 days using automated HRIS provisioning to unlock the 66% churn reduction.
2. Learner App (Habitual Engagement): Drive weekly active learners per seat from 0.29 to 0.40 to resolve the #1 churn driver and defend against budget cuts.
3. Growth (Expansion Based on Utilization): Stop spamming low-usage accounts with prompts; identify accounts reaching >80% active seat capacity and drive contract tier expansion.

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3. Proposed Q4 OKRs (Learner App, Admin, Growth)

``` ┌──────────────────────────────────────────────────────────────────────────────────┐ │ COMPANY OBJECTIVE (CEO) │ │ Grow by keeping and expanding the customers we have │ │ NRR: 102% -> 106% │ Logo Churn: 3.4% -> 2.6% │ └───────────────────────┬──────────────────────────┬───────────────────────────────┘ │ │ ┌───────────────┴──────────┐ ┌───────────┴──────────────┐ ▼ ▼ ▼ ▼ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ ADMIN │ │ LEARNER APP │ │ GROWTH │ │ Time-to-1st- │ │ Weekly Active│ │ High-Utility │ │ Learner <14d │ │ Learners >38%│ │ Seat Growth │ └──────────────┘ └──────────────┘ └──────────────┘ ```

#### A. Admin Team * Objective: Accelerate customer time-to-value to protect new cohort retention. * Context: Closing the activation gap from 31 to 14 days addresses our highest-leverage retention vector. * Key Results: * KR 1 (Outcome): Reduce median Days from contract to first learner from 31 days to 14 days. * KR 2 (Outcome): Increase % of newly signed accounts with active HRIS employee sync within 7 days from 22% to 65% (Baseline: internal onboarding sample). * KR 3 (Outcome): Increase the percentage of invited admins who configure reporting within 10 days of signature from 28% to 60% (Baseline: Q3 dashboard launch data).

#### B. Learner App Team * Objective: Establish enduring learning habits to eliminate non-usage churn. * Context: Attacking the #1 churn reason ("people didn't use it") by lifting routine learner engagement rather than vanity completions. * Key Results: * KR 1 (Outcome): Increase Weekly active learners per seat from 0.29 to 0.38. * KR 2 (Outcome): Increase 4-week active learner retention (learners active in Week 1 who return in Week 4) from 34% to 48% (Baseline: Q3 cohort average). * KR 3 (Outcome): Reduce the percentage of licensed seats with zero activity in the trailing 30 days from 52% to 35%.

#### C. Growth Team * Objective: Capitalize on active accounts to drive sustainable seat expansion. * Context: Expansion must be an outcome of account saturation, not blind in-app prompts. * Key Results: * KR 1 (Outcome): Increase Seats added in existing accounts from 8,100 to 12,500. * KR 2 (Outcome): Increase account-level seat utilization (accounts with >80% active seats in past 30 days) from 19% to 32% (Baseline: Q3 active accounts). * KR 3 (Outcome): Convert 35% of accounts reaching >90% seat capacity into expanded contracts within 30 days (Baseline: 18%).

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4. OKR Governance Rules for Q4

To ensure we never celebrate a 0.81 score while missing core business commitments again, I propose three immediate operating changes:

1. Zero Pure Output KRs: No KR may simply measure shipping code, closing tickets, or running experiments. Every KR must measure a verifiable shift in customer or business behavior.
2. Target Continuity & Definition Lock: Any changes to metric definitions (such as completion percentages) must maintain restated historical baselines and require Product Operations sign-off.
3. Cross-Functional Dependencies on Onboarding: Admin and Learner App will review weekly leading indicators (TTFL and weekly seat activation) jointly with Customer Success to intercept at-risk renewals early in the contract cycle.

To: Grace Mensah, CPO From: Staff PM Subject: Q3 diagnosis and proposed Q4 OKRs

Recommendation

Q3 was a strong delivery quarter, not a strong customer-growth quarter. We should carry the missed company targets into Q4, reset baselines to Q3 actuals, and judge teams on customer behavior and retained revenue—not shipped features.

Proposed Q4 company objective: Grow by keeping and expanding existing customers. - Net revenue retention (NRR): 102% → 108%. - Quarterly logo churn: 3.4% → 2.5%.

These are recovery targets, not a forecast justified by last quarter’s results.

What Q3 actually tells us

The 0.81 score obscures the company’s deterioration. NRR fell from 103% to 102%; logo churn rose from 3.1% to 3.4%. After correctly classifying “launch three HRIS integrations” as an output, 10 of the 16 team KRs measured delivery. All ten scored 1.00. The six remaining measures averaged approximately 0.48—and one of those, completion, is not comparable over time. Equal-weight averaging rewarded delivery while masking misses on customer value.

Learner adoption is the clearest warning. Weekly active learners per paid seat fell from 31% to 29%, despite offline mode, streaks and badges shipping. App-store ratings improved from 4.2 to 4.5, but that does not establish broader adoption among employees whose employers pay for seats. “Our people didn’t use it” appeared in 17 of 38 exit interviews, the largest category. This supports prioritizing adoption, although exit interviews neither represent every customer nor establish causality.

Admin delivery barely changed time to value. Three integrations and a reporting dashboard shipped, but time from contract to first learner improved only from 34 to 31 days against a 14-day target. Accounts activating within 14 days churned at one-third the rate of other accounts. That is a promising intervention hypothesis, not proof: easier-to-activate customers may also be healthier for other reasons.

Growth generated activity, not expansion. Thirteen experiments and upgrade prompts accompanied a decline in seats added from 9,200 to 8,100. We cannot infer that those interventions caused the decline, but we have no basis for declaring them successful.

Some improvements are real; others require repair. Uptime reached 99.93%, a meaningful reliability improvement, though its retention effect is unmeasured. Completion rose from 41% to 58%, but the definition changed from finishing every module to finishing 80%. We must restate both periods under one definition before claiming improvement. Content volume and translations show capacity delivered, not that industry-fit problems were solved.

Proposed Q4 team OKRs

All baselines below are Q3 actuals. Delivery milestones belong in team plans, not as substitutes for these KRs.

The churn KR is one shared result, not two independent contributions. Likewise, Growth’s NRR accountability does not make retention solely Growth’s responsibility. The CPO arbitrates cross-team trade-offs.

The behavioral targets recover ambitions already set in Q3; shipping the previous roadmap did not validate their feasibility. Teams should present intervention evidence and capacity assumptions before commitment. Learner and Admin should prioritize existing accounts with low adoption, stalled rollouts and upcoming renewals—not rely only on newly signed accounts whose retention impact may arrive later.

How these connect to the company goal

The causal hypothesis is:

Faster activation → sustained learning → stronger renewal value → fewer departures and more paid expansion.

The financial test is separate:

NRR = (opening recurring revenue + expansion − contraction − churned revenue) / opening recurring revenue.

Using the company’s existing measurement window, 108% means ending with $108 from each $100 of opening recurring revenue, versus Q3’s $102. Therefore, expansion less contraction and churn must improve by six percentage points of opening revenue.

We cannot honestly allocate that six-point improvement from the supplied data. Logo churn counts customers, not dollars; seats added omit pricing, discounts and offsetting seat reductions. A 0.9-point logo-churn improvement plus 5,900 additional seats does not automatically equal six NRR points.

In week one, Growth and Finance must build the account-level revenue bridge: opening revenue, renewal exposure, churn, contraction and expansion. That bridge must quantify a credible route to 108%; otherwise, revise the intervention plan rather than claim the seat target is sufficient.

Operating discipline

Review adoption and activation weekly; review renewal risk and the revenue bridge monthly. Freeze metric definitions, restate completion, and segment results by account size, tenure and renewal timing. Check that activation improvements are not just token first starts and that adoption gains are not driven by shrinking paid-seat denominators.

At quarter-end, report the two company outcomes first. No blended delivery score should again turn deteriorating retention into “a great quarter.”