Draft OKRs

To: Grace Mensah, CPO From: Staff PM Re: What Q3 OKRs told us, and Q4 team OKRs that add up to the company goal

Bottom line

Q3 looked like a good quarter (0.81 average) but failed on the goal it was meant to serve. We shipped almost everything we planned and moved the customer numbers the wrong way. Q4 needs to be built around the three things customers told us drive churn: people not using the product, slow time to first learner, and seat growth that depends on usage. Output shipping stays, but it no longer counts as success on its own.

What Q3 actually tells us

The 0.81 is a shipping score. Of 16 key results, nine were outputs (things we built, published, or closed). All nine scored 1.00. Outcomes scored 0.56 on average, and the outcome KRs that map to the company goal mostly failed:

• Net revenue retention: 102% against a 108% target, and down from the 103% baseline.
• Quarterly logo churn: 3.4% against a 2.5% target, and worse than the 3.1% baseline.
• Seats added in existing accounts: 8,100, down from 9,200. Growth shipped 13 experiments and a prompt, and expansion still fell.
• Weekly active learners per seat: 0.29, down from 0.31. Usage declined while we shipped streaks and offline mode.
• Days from contract to first learner: 31, down from 34 against a target of 14. We barely moved the number that most affects churn.

The bright spots are real but don't map to the goal: app rating rose from 4.2 to 4.5, and uptime is ahead of target.

Why the goal didn't move: the team KRs never owned it. No team had NRR or churn as its KR. Each team optimized its own outputs, and the company numbers fell in the gaps between them.

The exit interviews say where the gaps are. Of 38 accounts that left in Q3:

• 17 (45%): "Our people didn't use it." This is adoption, which sits with the Learner app and with the onboarding that Admin owns.
• 9 (24%): budget cut. Hard to move directly, but more likely when usage is low.
• 7 (18%): moved to the LMS that came with their HR system. Our three HRIS integrations did not stop this. We need to know which HR systems these accounts used.
• 5 (13%): content didn't fit their industry. This is Content's problem, and it isn't in this memo's scope.

The most useful finding is the activation data: accounts that reached a first learner within 14 days churned at one-third the rate of the rest. Our average is 31 days. Activation speed is probably our highest-leverage lever, and Q3 barely touched it.

The completion rate score can't be trusted. On 1 August we changed the definition of "complete" from every module finished to 80% of modules finished. The 41→58 movement (scored 1.00) mixes two definitions, and the new definition is looser. Some or all of that gain is a measurement change, not learning. We should restate the Q2 baseline on the new definition before anyone cites this number. Until then it should not count as a win.

A smaller measurement issue: "Launch 3 HRIS integrations" was labeled an outcome but is really a count of shipped work. It should be an output, paired with an adoption outcome.

What this means for Q4

We should commit to the company numbers and make the team KRs add up to them. My proposal:

• Net revenue retention: 102% → 106% at Q4 end (108% stays the year-end target, but I don't think it's reachable in one quarter from 102%).
• Quarterly logo churn: 3.4% → 2.8%.

Churn and expansion both depend on usage, so I've set team targets on usage and activation rather than on shipping. Finance should confirm how much each lever contributes to NRR. I've assumed both churn and seat expansion have to move for the 106% target to hold.

Q4 team OKRs

Each team has two outcome KRs. Output KRs can sit underneath as supporting work but are not scored toward the goal. "TBD" baselines need to be pulled in week one, before targets are locked.

How the teams connect to the goal

• Learner app reduces the 17 "didn't use it" churns. Higher weekly usage also feeds expansion, since seat growth needs people actually learning.
• Admin targets the 14-day activation link. Getting the median from 31 to 21 days should move the share of accounts inside 14 days, which is where the churn gap is. The HRIS KR should be reviewed in week 2 by checking which HR systems the seven accounts that left for their HR LMS were using.
• Growth stops measuring success as experiments run and starts measuring seats added in accounts that use the product. Expansion in low-usage accounts is a risk, not a win, so the renewal-risk KR makes Growth own the save motion for the budget-cut and low-usage accounts.

Decisions I need from you

1. Agree the company targets (NRR 106%, churn 2.8%), or tell me to hold 108% and accept the risk.
2. Restate completion rate. Finance or Data should back-calculate the Q2 baseline under the new definition. Until then Content's completion KR cannot be scored.
3. Assign the churn-reason KR to a single owner. Right now no one owns the 38 exit interviews. I suggest Customer Success, reporting to you monthly.
4. Content's fit problem. Five churns cited content fit. Content's Q4 OKRs should include an industry-fit KR. I'd like to discuss this with the Content lead before it's locked.

I can have the baselines pulled and the final targets locked by the end of week two.

MEMORANDUM

TO: Grace Mensah, Chief Product Officer FROM: Staff PM, Product DATE: October 2, 2023 SUBJECT: Q3 OKR Post-Mortem & Q4 Product OKRs

---

Part 1: What Q3 Actually Tells Us (Beyond the 0.81 Score)

On paper, Q3 looked like a triumph. The company-wide average key result score across product teams was 0.81, prompting celebratory remarks at the all-hands. However, a closer look at the data reveals a stark disconnect between our product output and the company’s core objective: growth by keeping and expanding the customers we have.

The company's Q3 goals failed. Net Revenue Retention (NRR) dropped from our baseline of 103% to an actual of 102% (target: 108%), and quarterly logo churn rose from 3.1% to 3.4% (target: 2.5%).

Why did we miss our financial and retention targets while hitting an 0.81 KR score? The data points to a systemic structural flaw in how we set and measure OKRs: we rewarded teams for shipping outputs, while outcomes that actually drive retention and expansion flatlined or missed.

1. The Output-Outcome Illusion: Look at the team scorecards. Every single output KR scored 1.00. Content published 41 courses (target 40), translated 60 items, shipped mobile offline mode, shipped streaks and badges, launched three HRIS integrations, shipped a new reporting dashboard, ran 13 expansion experiments, launched in-app upgrade prompts, migrated a video CDN, and closed 162 bugs. We were exceptionally busy. Conversely, our critical outcomes largely failed: * Learner App: Weekly active learners per seat plummeted from 0.31 to an actual of 0.29 (target: 0.40, score: 0.00). * Admin: Days from contract to first learner barely budged, moving from 34 to 31 days (target: 14 days, score: 0.15). * Growth: Seats added in existing accounts fell from a baseline of 9,200 to 8,100 (target: 14,000, score: 0.00).

2. The Root Causes of Churn: Our Q3 exit interviews across 38 churned accounts provide the smoking gun. The leading reason for churn—cited by nearly half (17 out of 38)—was simple: "Our people didn't use it." Furthermore, the data reveals a powerful operational lever we have ignored: accounts that reached their first learner within 14 days of contract signature churned at one-third of the rate of the rest. Yet, our Admin team’s initiative to reduce this time stalled at 31 days.

3. Metric Distortions: Even our apparent wins require scrutiny. Content’s course completion rate hit 1.00 (rising from 41% to 58%), but the metric dictionary reveals a rule change on August 1st: the definition of "complete" was quietly lowered from 100% of modules finished to 80%. The "success" is largely an artifact of moving the goalposts.

The Takeaway: In Q3, product engineering functioned as an effective feature factory, but those features did not translate into user engagement, rapid time-to-value, or account expansion. To fix NRR and logo churn in Q4, we must completely realign product incentives away from output delivery and lock them onto core engagement and adoption metrics.

---

Part 2: Q4 OKRs for Learner App, Admin, and Growth Teams

To directly support the CEO's ongoing company objective—grow by keeping and expanding the customers we have—we are restructuring Q4 OKRs. Every team’s objectives are anchored to lagging retention drivers (engagement, time-to-value, and expansion).

#### 1. Learner App Team * Context & Rationale: With 17 out of 38 churned accounts citing lack of employee usage, the Learner App team must pivot away from vanity features (streaks and badges shipped in Q3 did nothing to stop the drop) and focus entirely on driving habitual weekly usage. * Objective: Drive habitual engagement to secure employee adoption and prevent account-level churn. * Key Results: * KR 1 (Outcome): Increase weekly active learners per seat from 0.29 to 0.36. (Baseline: 0.29, Target: 0.36) * KR 2 (Outcome): Increase 30-day course retention rate for newly enrolled learners from 22% to 35%. (Baseline: 22%, Target: 35%) * KR 3 (Output): Ship personalized "Next Step" AI recommendations on the home screen to streamline the path from login to learning. (Baseline: 0, Target: 1)

#### 2. Admin Team * Context & Rationale: Exit data proves that getting the first learner active within 14 days slashes churn by two-thirds. In Q3, we focused on HRIS integrations (output), but customers still languished for 31 days before their employees started learning. Admin must own the friction reduction between contract signature and organizational activation. * Objective: Accelerate time-to-value so customer organizations realize immediate ROI. * Key Results: * KR 1 (Outcome): Reduce days from contract signature to first learner from 31 days to 14 days. (Baseline: 31, Target: 14) * KR 2 (Outcome): Increase self-serve HRIS integration completion rate (admins successfully connecting without support intervention) from 40% to 75%. (Baseline: 40%, Target: 75%) * KR 3 (Output): Launch automated "Nudge Admin" workflow triggers when zero learners are active 7 days post-contract. (Baseline: 0, Target: 1)

#### 3. Growth Team * Context & Rationale: Q3 saw expansion seats drop to 8,100 against a 14,000 target. Running 12 experiments and launching upgrade prompts generated output, but no revenue expansion. Growth must transition from running experiments to driving net-new seat expansion inside existing healthy accounts. * Objective: Expand footprint and seat count within existing healthy accounts. * Key Results: * KR 1 (Outcome): Increase net new seats added in existing accounts from 8,100 (Q3 actual) to 12,500. (Baseline: 8,100, Target: 12,500) * KR 2 (Outcome): Increase conversion rate of in-app upgrade prompts from trial/capped tiers to paid seat expansions from 1.2% to 3.5%. (Baseline: 1.2%, Target: 3.5%) * KR 3 (Output): Launch automated team-invitation workflows for departmental managers in accounts nearing seat capacity limits. (Baseline: 0, Target: 1)