Purpose: Your revenue is the product of nine levers, so the weakest one caps the whole chain — score all nine against benchmark, and the lowest score is where the next multiplication is waiting. Fix that one first; polishing a lever that is already strong buys almost nothing.
Foundation readiness (pre-check)
The Foundation is not a tenth lever — it is the quality of every lever’s improvement. Confirm it is in place before scoring, because a weak Foundation halves every gain below it.
| Foundation layer | In place? | Notes |
|---|---|---|
| Company Context — what you sell, how, who you are | Y / N | ________________ |
| Market Awareness — the landscape you compete in | Y / N | ________________ |
| Customer Avatar — the real person you sell to | Y / N | ________________ |
If any answer is N, stop and complete worksheet.md first. Scoring on a hollow Foundation produces confident, wrong numbers.
The scorecard
Run your own current figure down the Your number column and read each against the benchmark band beside it — the same band its step chapter settled on. Then score the lever 1–5: how far inside (or below) its band do you sit?
Score key: 5 well above band · 4 comfortably inside · 3 at the lower bound · 2 below band · 1 far below band (or unknown / not running).
| Lever | Its metric | Benchmark band (as settled in-chapter) | Your number | Score 1–5 | Notes |
|---|---|---|---|---|---|
| HOOK | Click-through rate | Paid social avg 1.71% (traffic) / 2.59% (leads); Google Search avg 6.42–6.66%; Google Display ~0.46%; email open 30–45% reported (Apple MPP inflates by 10–15pp; true engagement ~20–25%) | ______ | __ | ______ |
| GIFT | Landing-page opt-in rate | Median ~6–7% across all pages; email-driven traffic ~19.3%; warm traffic top performers ~20%+; below ~6% underperforming | ______ | __ | ______ |
| IDENTIFY | Opt-in / form-completion rate | Warm traffic 10–30% (top performers 20%+); cold paid 5–15%; each additional field reduces conversion ~4.1%; avg US checkout has 11.3 fields vs ideal 8 | ______ | __ | ______ |
| ENGAGE | Cart abandonment / assisted conversion | Abandonment ~68–72% (≈28–32% completion) is the baseline; below it is progress | ______ | __ | ______ |
| SELL | Conversion rate (also check checkout completion: desktop avg ~52.5%, mobile avg ~42.4%, top-quartile desktop ~73.2%) | Average 1.4%; global ~1.7–1.89%; above average 2–3%; top 10%: 4.7%+ | ______ | __ | ______ |
| NURTURE | Welcome / sequence engagement | Routine open 30–45% reported (Apple MPP caveat; true ~20–25%); routine click 1.5–4% (ecommerce avg 1.74%); welcome opens ecommerce ~34–36%; cross-industry up to 80%+ | ______ | __ | ______ |
| UPSELL | Post-purchase take-rate | Post-purchase upsell average 5–15%; top performers 25–30%; in-cart order bump typically 10–35%; average 30–40% for well-matched offers | ______ | __ | ______ |
| EDUCATE | 365-day repeat-purchase rate | Overall average ~18.8%; consumables 25–40%; fashion 12–17% | ______ | __ | ______ |
| SHARE | Referred-customer LTV uplift | ≥16% higher LTV; 18% lower churn; ~4× more likely to convert | ______ | __ | ______ |
(Every band above is orientation drawn from the source named in its own step chapter, not a precision target. Benchmarks drift: read “roughly 20–40%”, never “27.4%”. Verify each figure against the current published source before citing it publicly, and refresh quarterly.)
How to read it
- Lowest score = weakest lever = where to act first. Among the levers scored
2or1, the one with the largest gap and the one earliest in the chain is your constraint — they usually point at the same row. - A figure far above its band is not always a trophy. A GIFT opt-in well above range can mean a brilliant gift — or freebie-seekers who never buy. Chase a suspiciously high number forward into lead quality before banking it as a win.
- Revisit the chapter and worksheet for that lever, re-read its diagnostic section, form one hypothesis, and run one test:
Instructions
- Run the Foundation pre-check. If any layer is missing, complete
worksheet.mdbefore going further. - Pull current figures for each lever from your analytics platform — real numbers from this period, not estimates and not last quarter’s, quietly rationalised upward.
- Fill the Your number column row by row, top to bottom.
- Score each lever 1–5 against its band using the key above. Where you have no number, score
1— an unmeasured lever is a blind one. - Find the lowest score. Ties break toward the earliest lever in the chain: fixing an upstream leak frees everything downstream of it.
- Confirm the cause before fixing it. A weak score can be the honest verdict on its own lever — or the downstream shadow of a weak lever before it. Use the
5-refine.mdmetrics cascade to tell symptom from cause. - Revisit that lever’s chapter and worksheet (table above), form one hypothesis, run one test, and re-score next cycle.
Feeds → REFINE. The Scorecard is the entry point to the REFINE loop — it locates the constraint; REFINE’s Full-Funnel Dashboard, cascade, and one-test discipline act on it. Re-score every cycle: the weakest lever moves as you fix it.
The Multiplier Principle: ../0-introduction/0.1-multiplier.md · The REFINE loop and full benchmark sources: ../5-refine/5-refine.md · The Foundation worksheet: worksheet.md