Mohit Aggarwal Growth Experiment Backlog Skill
Growth is a rate of learning, not a list of ideas. This skill turns a pile of "we should try…" into a prioritised backlog of falsifiable experiments — each tied to a metric, scored for impact and effort, and shaped as the smallest test that could prove it — so the team ships learning every week, not opinions.
Required Inputs
Ask for these only if they aren't already provided:
- The metric to move — the one growth metric this cycle (activation, conversion, retention, referral).
- The funnel stage / leak — where the opportunity is (pair with
marketing-funnel-plan). - Raw ideas — any experiment ideas already on the table.
- Constraints — eng/design bandwidth and traffic volume (which caps how many tests can reach significance).
Output Format
Growth Backlog: [metric this cycle]
1. Focus — the one metric and the funnel stage, with the current baseline. A backlog without a focus metric is just a wish list.
2. Backlog table — every idea as a hypothesis, scored and sortable:
| # | Hypothesis ("If we ___, then [metric] will ___ because ___") | Stage | Impact | Confidence | Ease | ICE | Status |
|---|
(Use ICE (1–10 each) or PXL for less gameable scoring. Sort by score; the top few are this cycle's tests.)
3. Test designs (top 3) — for each top experiment: the exact change, the primary metric + guardrail metrics, the variant(s), the sample size/duration to detect the expected effect, and the definition of done (ship / iterate / kill).
4. Cadence — the weekly rhythm: pick → build → run → read → decide → document the learning back into the backlog (winners and losers both teach).
Quality Checks
- Every item is a falsifiable hypothesis with the metric it moves and a "because" — not a vague idea
- Scoring (ICE/PXL) is applied consistently so the backlog is sortable, not cherry-picked
- Top experiments specify sample size/duration to actually detect the expected effect
- Each test has guardrail metrics so a "win" can't quietly harm something else
- There's a cadence that captures the learning from losers, not just winners
Anti-Patterns
- Do not run experiments without a hypothesis and a target metric — that's just shipping changes and hoping
- Do not call a test before it reaches the planned sample size — peeking and stopping early manufactures fake wins
- Do not chase many tiny tests when traffic is low — you'll never reach significance; pick fewer, bigger bets
- Do not ignore guardrail metrics — a conversion win that tanks refunds or retention is a loss
- Do not discard losing experiments silently — the learning is the asset; record why it failed
Based On
Growth-process practice — ICE/PXL prioritisation, hypothesis-driven experiments, and the build–measure–learn cadence.