Define Prioritization Framework: Brainshelf Q3 Features
Scenario
Brainshelf is a B2C AI book-curation subscription. The PM has 8 candidate features for Q3 and engagement plus engineering estimates, so RICE and ICE both run, with MoSCoW as a scope-bounding view. The lesson is two divergent items (Mobile app, Family plan) that expose RICE’s reach bias. All reach/impact/effort numbers are illustrative PM inputs for this scenario [fictional].
Source Notes:
- Sean McBride, “RICE: Simple prioritization for product managers” (Intercom, intercom.com) - the RICE formula and the 0.25-3 impact scale used here originate from Intercom’s framework.
- Sean Ellis (GrowthHackers) - the ICE framework (Impact, Confidence, Ease) originated for prioritizing growth experiments; used here as the lower-input complement to RICE.
- Dai Clegg, DSDM (MoSCoW) - the Must/Should/Could/Won’t commitment buckets used for the Q3 scope bound.
Prompt
/prioritization-framework
prioritize our Q3 candidate features for brainshelf (AI book-curationsubscription). decision context: Q3 roadmap, ~14 eng-weeks of capacity.
features (i have engagement data + eng estimates):1. rec algorithm v2 2. social follow-friends 3. mobile app4. public reading profiles 5. reading streaks 6. audiobook recs7. export to goodreads 8. family plan
run whatever frameworks apply and show me where they disagree.Output
Prioritization: Brainshelf Q3 Roadmap Candidates
Applicability Filter Summary
Reach, impact, effort, and confidence are available, so RICE and ICE both run. The decision bounds Q3 scope (~14 eng-weeks), so MoSCoW runs as a commitment view. Weighted Scoring is excluded (no competing stakeholder criteria provided). Kano is excluded: no customer-research data. A Kano survey on these eight features would unlock it.
Inputs Summary
Eight Q3 candidates with PM-supplied estimates (illustrative [fictional]). Reach is users affected per quarter; effort in eng-weeks.
Per-Framework Scoring
RICE
| Item | Reach/qtr | Impact (0.25-3) | Conf (%) | Effort (wk) | RICE | Notes |
|---|---|---|---|---|---|---|
| Reading streaks | 35,000 | 1 | 60% | 2 | 10,500 | Cheap retention lever |
| Rec algorithm v2 | 40,000 | 2 | 70% | 6 | 9,333 | Core value prop |
| Mobile app | 30,000 | 2 | 80% | 12 | 4,000 | Big reach, big build |
| Export to Goodreads | 8,000 | 0.5 | 90% | 1 | 3,600 | Trivial, high confidence |
| Social follow-friends | 25,000 | 1 | 50% | 5 | 2,500 | Speculative |
| Audiobook recs | 20,000 | 1 | 50% | 4 | 2,500 | Speculative |
| Family plan | 6,000 | 2 | 70% | 4 | 2,100 | Narrow reach, high per-user value |
| Public reading profiles | 15,000 | 0.5 | 60% | 3 | 1,500 | Low impact |
ICE
| Item | Impact (1-10) | Conf (1-10) | Ease (1-10) | ICE | Notes |
|---|---|---|---|---|---|
| Export to Goodreads | 4 | 9 | 9 | 324 | Trivial, well-understood |
| Family plan | 7 | 7 | 6 | 294 | High per-user value |
| Reading streaks | 6 | 6 | 8 | 288 | Cheap and solid |
| Rec algorithm v2 | 9 | 7 | 4 | 252 | High value, harder |
| Audiobook recs | 6 | 5 | 6 | 180 | Middling |
| Mobile app | 8 | 8 | 2 | 128 | Valuable but a 12-week slog |
| Public reading profiles | 3 | 6 | 7 | 126 | Low value |
| Social follow-friends | 5 | 5 | 5 | 125 | Speculative |
MoSCoW (Q3 scope, ~14 eng-weeks)
| Item | Bucket | Rationale | Risk if dropped |
|---|---|---|---|
| Rec algorithm v2 | Must | The product’s core promise | Erodes the differentiator |
| Reading streaks | Should | Cheap retention win | Slower habit formation |
| Export to Goodreads | Should | Cheap, reduces lock-in anxiety | Minor friction |
| Family plan | Should | New revenue line | Forgone revenue |
| Mobile app | Could | 12 eng-weeks blows most of Q3 | Mobile users wait |
| Audiobook recs | Could | Nice expansion | None critical |
| Social follow-friends | Won’t (this time) | Low confidence | Defer until validated |
| Public reading profiles | Won’t (this time) | Low impact | None |
Per-Framework Ranking Output
Each scoring table above is sorted high to low, so the per-framework ranking is the row order shown (top item first, lowest last). The side-by-side rank positions, and the items where the frameworks disagree, are consolidated in the Cross-Framework Comparison below.
Cross-Framework Comparison
| Item | RICE rank | ICE rank | MoSCoW | Agreement |
|---|---|---|---|---|
| Reading streaks | 1 | 3 | Should | Strong |
| Rec algorithm v2 | 2 | 4 | Must | Strong |
| Mobile app | 3 | 6 | Could | Divergent |
| Export to Goodreads | 4 | 1 | Should | Close |
| Social follow-friends | 5 | 8 | Won’t | Mostly agree (RICE 5th, borderline) |
| Audiobook recs | 6 | 5 | Could | Close |
| Family plan | 7 | 2 | Should | Divergent |
| Public profiles | 8 | 7 | Won’t | Agree (defer) |
Divergent - Mobile app (RICE 3rd, ICE 6th): RICE rewards the 30,000 reach; ICE punishes Ease (2) because it is a 12-week build. Driver: reach vs. effort. With only 14 eng-weeks, the effort signal should win for Q3 - hence MoSCoW Could.
Divergent - Family plan (RICE 7th, ICE 2nd): RICE’s reach term punishes the narrow 6,000 reach; ICE rewards the high per-user impact and decent ease. Driver: RICE under-weights revenue-concentrated features. Because Family plan opens a new revenue line, weight the ICE/MoSCoW view and keep it as a Should.
Executive Summary with Recommendation
Fund Rec algorithm v2 and Reading streaks: both score high across frameworks and fit the budget. Add the two cheap, high-confidence items - Export to Goodreads (1 week) and Family plan (4 weeks) - the latter despite a low RICE rank, because RICE undervalues its concentrated revenue impact. That fills ~13 of 14 eng-weeks. Defer Mobile app to a quarter with capacity for a 12-week build, and drop Public profiles (all three frameworks agree) and Social follow-friends (ICE and MoSCoW both defer it; RICE ranks it a borderline 5th) this cycle. The one judgment call is Family plan: if new revenue is not a Q3 goal, swap it for Audiobook recs.
Sensitivity / What Changes the Ranking
- If Mobile app effort were 6 weeks (not 12), its ICE Ease rises and it becomes a credible Should.
- If Rec algorithm v2 confidence dropped below 50%, Reading streaks becomes the clear single priority.
- Family plan’s standing depends entirely on whether Q3 has a revenue goal; with one, it is a Must.
Recommendations (Sequencing)
- Fund now: Rec algorithm v2, Reading streaks, Export to Goodreads, Family plan
- Defer: Mobile app (needs a low-effort quarter), Audiobook recs
- Drop this cycle: Social follow-friends, Public reading profiles
- Data that would change this: A Kano survey to confirm which features are delighters vs. table stakes; a firmer mobile-app effort estimate
Limitations and Biases
- RICE under-ranked Family plan purely on reach; do not let it auto-cut revenue-concentrated features.
- No sequencing dependencies are modeled (e.g., Family plan may need billing changes first).
- Every score is a PM estimate; a Kano survey would replace the guessed impact values with customer signal.