feature-prioritization

Feature Prioritization

Stop arguing about what to build. Use a scoring matrix to make prioritization decisions fast, defensible, and transparent. Track everything in SQL so the decision logic is auditable.

The Matrix

Score each feature on three dimensions (1-5 scale):

Dimension	1 (Low)	3 (Medium)	5 (High)
Impact	Nice-to-have, <5% of users	Useful for core segment	Critical path, >30% of users or major revenue
Confidence	Hunch / no data	1-2 data points	Validated by user research / A/B test
Effort	5 = lowest (easy)	3 = medium	1 = highest (hardest)

Score = Impact × Confidence × Effort (higher = higher priority)

Note: Effort scoring is inverted — easy things score higher because ROI is better.

Setup

CREATE TABLE features (
    id TEXT PRIMARY KEY,
    title TEXT NOT NULL,
    description TEXT,
    impact INTEGER,          -- 1-5
    confidence INTEGER,      -- 1-5
    effort INTEGER,          -- 1-5 (5=easy, 1=very hard)
    score REAL,              -- impact * confidence * effort
    status TEXT DEFAULT 'backlog',  -- backlog | in_sprint | shipped | rejected
    notes TEXT,
    created_at DATETIME DEFAULT CURRENT_TIMESTAMP
);

Workflow

Step 1: List and Describe Features

> I have the following feature requests for [product area]:
> [paste feature list or backlog items]
>
> For each feature, write a 1-sentence description of the user benefit.
> Then help me think through the scoring dimensions.

Step 2: Score Each Feature

> Let's score each feature against our prioritization matrix.
>
> For [Feature X]:
> - Impact: What % of users does this affect? Is it on the critical path to revenue?
> - Confidence: Do we have user research, data, or is this a hypothesis?
> - Effort: Engineering complexity estimate (S/M/L → 5/3/1)
>
> Challenge my assumptions if something seems over- or under-valued.

Alternative Frameworks