Framework

RICE prioritization on a real SaaS team's Q3 backlog

12 candidate features, scored honestly, ranked by RICE. The surprising losers and one feature the team almost killed that scored at the top.

King MarkLast reviewed 4 min read

A B2B SaaS team running their Q3 planning. 12 candidate features on the backlog, capacity for roughly 5. The team had been arguing about three favorites for two weeks. They ran RICE on a Tuesday afternoon — by 5pm the ranking was settled and the planning meeting on Thursday took 20 minutes instead of two hours.

If you need the formula and scoring rules before reading this example, see RICE score calculator: the formula with 3 worked examples.

Setup

  • Product: workflow automation for mid-market customer success teams
  • Active accounts: 1,800
  • Team capacity: ~12 engineer-months for Q3 features (after carrying ops and bugs)
  • Scoring scale: Reach = customers affected/quarter; Impact = 0.25/0.5/1/2/3; Confidence = %; Effort = engineer-months

The candidates and scores

FeatureReachImpactConfEffortRICE
Bulk-edit account fields1,400180%1.5747
Slack notification routing900270%2630
Custom dashboard widgets600260%3240
New onboarding wizard for trial users1,2000.590%2270
Salesforce 2-way sync v2400360%4180
Mobile push notifications1,8000.570%1.5420
Account merge tool300190%1270
Inline AI summaries on accounts1,800140%2.5288
Multi-workspace support200380%680
Auto-renew reminder emails1,6000.590%0.51,440
Custom report builder500250%4125
Real-time activity feed1,800160%3360

Total effort if everything shipped: 31 engineer-months. Available: 12.

The ranked top 5

By RICE:

  1. Auto-renew reminder emails — 1,440 (the surprise winner)
  2. Bulk-edit account fields — 747
  3. Slack notification routing — 630
  4. Mobile push notifications — 420
  5. Real-time activity feed — 360

Total effort for top 5: 8.5 engineer-months. Fits with room to spare.

What was surprising

Auto-renew reminders almost didn't make the candidate list. A product manager had floated it casually; nobody pushed hard for it. When the team computed RICE honestly — affects almost every customer, modest per-customer impact, very high confidence (we know exactly what this does), and tiny effort (0.5 eng-month) — it landed at 1,440. The Effort being so low and Reach so high produced the disproportionate score the formula was designed to surface.

Multi-workspace support, the founder's favorite, ranked dead last. Reach was small (only enterprise customers needed it), Effort was huge (6 eng-months), and the resulting RICE score of 80 was honest. The team would have shipped it on intuition; the scoring forced an explicit conversation about whether enterprise-tier expansion justified the trade. They concluded: not this quarter. Revisit when 3+ enterprise prospects ask for it.

Inline AI summaries had high Reach × Impact (1,800 × 1) but low Confidence (40%) because the team had no evidence summaries would actually be read. The Confidence multiplier dropped it from a potential 1,800 to 288. Penalty for speculation working as intended.

The "we cheated" check

After the scoring, the lead asked the question that prevents most RICE abuse: "are any of these scores driven by what we wanted the answer to be?"

One score got revised: Salesforce sync v2 (Confidence raised from 60% → 80%) was an attempt by an engineer to push the score up. The team caught it because Salesforce sync v2 had failed user testing twice before — 60% confidence was the right number. After the revision back, Salesforce sync stayed out of the top 5.

What this teaches

  • The formula's denominator (Effort) is what produces non-obvious winners. A small Effort with even modest other inputs jumps to the top. Most teams under-weight this.
  • Confidence is the term that penalizes wishful thinking. Without honest confidence numbers, the score becomes "what we hope is true × what we wish it cost".
  • Surprise winners and surprise losers are signals. When RICE produces a ranking that conflicts with team intuition, both deserve a serious look. Sometimes the intuition was right and the scoring is off; more often the scoring is honest and the intuition was anchored on irrelevant features.

Run your own

The full method is in the Academy guide →. Open the framework page for the catalog entry, or start a canvas to score your own backlog.

More examples

All examples →