1 min lesson
Combining the axes into a score
Use the example in "Combining the axes into a score" to explain the main idea in plain words.
Step 1 of 2
Combining the axes into a scoreso loud-but-rare can't beat quiet-but-catastrophic
Keep severity and impact as separate axes, then collapse them into a priority band. The point of the multiply is that volume alone never wins: a flood of S3 cosmetic complaints stays below a single S0 data-loss report with one reporter.
A priority score you can actually defend in a brief
priority = severity_weight × impact_score
severity_weight: S0=100 S1=30 S2=8 S3=2
impact_score = (pct_DAU_affected × 10)
+ (arr_affected_usd / 10_000)
+ frequency_factor // 1 rare … 5 every-keystroke
+ (is_regression ? 15 : 0)
# S0 with one reporter: 100 × (0.001 + 0 + 1 + 0) ≈ 100
# S3 with 400 reporters: 2 × (4 + 0 + 3 + 0) ≈ 14
# the catastrophe wins and the math shows whyThe score is a conversation starter, not an oracle
A formula's value is that it forces the inputs into the open. When you say P1, you can name the severity tier, the percent of DAU, the ARR exposed and whether it's a regression. The engineer can argue with an input, which is far better than arguing with your vibe.