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The memo is logically structured, rooted in the evidence, and could be sent with minimal edits after pulling the TBD baselines and confirming the proposed company targets.
Output
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.
| Team | Objective | Key result | Q3 baseline | Q4 target |
|---|---|---|---|---|
| Learner app | Learners come back every week | Weekly active learners per seat | 0.29 | 0.35 |
| Share of learners active in week 2 after their first course | TBD (pull week 1) | Set week 2; expect +10 pts | ||
| Admin | Accounts get a first learner fast | Median days from contract to first learner | 31 | 21 |
| Share of new contracts reaching first learner within 14 days | TBD (pull week 1) | Set week 2 once baseline is known | ||
| Growth | Expansion follows real usage | Seats added in existing accounts | 8,100 | 10,500 |
| Renewal-risk accounts (low usage, renewal in 90 days) with a named save plan | TBD (pull week 1) | 100% covered; save rate tracked |
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
- Agree the company targets (NRR 106%, churn 2.8%), or tell me to hold 108% and accept the risk.
- 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.
- 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.
- 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.
Grades and run details
Decision model 91 · LLM judge 100
Decision model checks
- passUses the supplied evidence correctly38%
- passAddresses the actual decision41%
- passRespects explicit constraints21%
- passIdentifies material uncertainty80%
- partialAvoids unsupported claims51%
- partialProduces the required deliverable31%
- passReads the scorecard for what it is91%
- passCatches the redefined key result100%
- passKey results are outcomes, not output73%
- passFocuses on the big rock79%
- passShows how the goals add up27%
Run
- Run
- #1
- API response time
- 32 s
- Submitted
- 8 Oct 2026