From ai-business-skills
Calculates marketing budget via reverse KPI method (revenue → spend or spend → revenue) with region-specific benchmarks and 3-scenario sensitivity analysis.
How this skill is triggered — by the user, by Claude, or both
Slash command
/ai-business-skills:10-reverse-kpi-globalThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Calculate marketing budget by working backward from revenue goal — or forward from available spend to expected revenue. Universal math; currency and benchmark numbers vary per region (US/EU/SEA/LATAM).
Calculate marketing budget by working backward from revenue goal — or forward from available spend to expected revenue. Universal math; currency and benchmark numbers vary per region (US/EU/SEA/LATAM).
If you've never run a reverse KPI calc:
Before calculation:
.agents/product-marketing-context-global.md — get product, AOV, region, currency, target market.variants/01-us.md — USD, US benchmarksvariants/02-eu.md — EUR/GBP, EU benchmarksvariants/03-sea.md — USD/local, SEA benchmarksvariants/04-latam.md — USD/BRL/MXN, LATAM benchmarksAsk user up to 4 questions:
Use when: "I want to hit $200K/month — how much ad spend do I need?"
Revenue target
/ AOV (average order value)
= ORDERS NEEDED
/ Booking → Customer rate
= BOOKINGS NEEDED
/ Lead → Booking rate
= LEADS NEEDED
/ Click → Lead rate
= CLICKS NEEDED
/ CTR
= IMPRESSIONS NEEDED
× CPM / 1000
= TOTAL AD BUDGET
For e-commerce (no booking step):
Revenue target
/ AOV
= ORDERS NEEDED
/ Conversion rate
= SESSIONS NEEDED (clicks)
/ CTR
= IMPRESSIONS NEEDED
× CPM / 1000
= TOTAL AD BUDGET
For B2B (longer funnel):
Revenue target
/ ACV (annual contract value)
= CUSTOMERS NEEDED
/ Win rate
= OPPORTUNITIES NEEDED
/ SQL → Opportunity rate
= SQL NEEDED
/ MQL → SQL rate
= MQL NEEDED
/ Lead → MQL rate
= LEADS NEEDED
→ continue with CPL × LEADS NEEDED = SPEND
Use when: "I have $50K — how much revenue can I expect?"
Budget
/ CPM × 1000
= IMPRESSIONS
× CTR
= CLICKS
× Click → Lead rate
= LEADS
× Lead → Booking rate
= BOOKINGS
× Booking → Customer rate
= ORDERS
× AOV
= REVENUE
Always run three scenarios:
| Variable | Pessimistic | Realistic (Base) | Optimistic |
|---|---|---|---|
| CPM | Industry avg + 30% | Industry avg | Industry avg − 20% |
| Click → Lead | Industry avg − 15% | Industry avg | Industry avg + 15% |
| Lead → Booking | Industry avg − 10% | Industry avg | Industry avg + 10% |
| Booking → Customer | Industry avg − 10% | Industry avg | Industry avg + 10% |
Use Base for budget. Use Pessimistic as buffer. Use Optimistic as stretch goal.
| Variable | Base value | Change +10% | Budget change | Sensitivity |
|---|---|---|---|---|
| CPM | [#] | +10% | +10% | Direct 1:1 |
| CTR | [#]% | +10% | -9% | High |
| Click→Lead | [#]% | +10% | -9% | High |
| Lead→Booking | [#]% | +10% | -9% | High |
| Booking→Customer | [#]% | +10% | -9% | High |
| AOV | [#] | +10% | -9% (fewer orders needed) | Indirect |
The two highest-leverage levers are usually:
Break-even orders = Fixed costs / (AOV − Variable cost per order)
Break-even days = Break-even orders / (Avg orders per day)
| Item | Value |
|---|---|
| Fixed costs/month (rent, salary, tools, software) | [#] |
| Ad spend (variable, but allocated upfront) | [#] |
| Total fixed | [#] |
| AOV | [#] |
| Variable cost per order (COGS, shipping, fees) | [#] |
| Profit per order | AOV − VarCost = [#] |
| Break-even orders | Total fixed / Profit per order |
| Break-even days | BE orders / 30 |
| Result | Meaning | Action |
|---|---|---|
| BE < 50% of expected orders | Safe — good margin buffer | Can scale spend |
| BE = 50–80% of expected | Tight — limited margin | Optimize cost first |
| BE > 80% of expected | Risky — easy to lose | Cut costs or raise AOV |
| Phase | % of budget | Duration | Goal | Primary KPI |
|---|---|---|---|---|
| Teaser / Awareness | 15% | Week 1 | Curiosity, brand build | Reach, video views, saves |
| Soft launch | 20% | Week 2 | Test creative, first leads | CPL, lead, A/B test data |
| Full launch | 40% | Weeks 3–4 | Scale winners, drive sales | ROAS, orders, revenue |
| Maintenance + retarget | 25% | Week 5+ | Retarget, nurture, repeat | CPA, LTV, retention |
| Phase | % | Amount | Days | Daily |
|---|---|---|---|---|
| Teaser | 15% | $12K | 7 | $1,714/day |
| Soft launch | 20% | $16K | 7 | $2,286/day |
| Full launch | 40% | $32K | 14 | $2,286/day |
| Maintenance | 25% | $20K | balance | depends on remaining days |
| Phase | Duration | Expectation | Track |
|---|---|---|---|
| Testing | Weeks 1–2 | No orders yet, testing creative + audience | CPM, CTR, CPL |
| First results | Weeks 3–4 | First orders, ROAS still low | First orders, leads |
| Optimization | Month 2 | ROAS improving, stabilizing | ROAS, CPA |
| Scale | Month 3+ | Stable ROAS, controlled budget increases | ROAS held, revenue up |
| Mature | Month 6+ | Self-running, enough data to forecast | LTV, retention, organic % |
| Rule | Explanation |
|---|---|
| First 2 weeks lose money | Learning cost — don't panic, don't pause |
| Base ROAS achieved by month 2 | Month 1 is testing, don't judge ROAS yet |
| Scale budget max 20%/week | Faster scaling = performance drops, CPM rises |
| ROAS drops 30% when scaling | Normal — wider audience = lower conv rate |
| Retarget ROAS 2-3x prospecting | Always allocate budget for retargeting |
| Need | Skill |
|---|---|
| Full marketing plan first | 00-marketing-plan-global |
| Current performance to inform calc | 03-performance-eval-global |
| Competitive spend benchmarks | 08-competitor-research-global |
| Customer insight to refine conv rates | 09-customer-insight-global |
| Post-campaign data analysis | 13-data-analysis-global |
Before delivering reverse KPI report:
npx claudepluginhub minhnv0807/ai-business-skills --plugin ai-business-skillsTính KPI ngược từ doanh thu về ngân sách hoặc xuôi từ ngân sách ra doanh thu với 3 kịch bản, phân tích độ nhạy, break-even và phân bổ ngân sách theo giai đoạn/kenh dựa trên benchmark Vietnam 2025–2026.
Helps calculate minimum and target ROAS (Return on Ad Spend) for paid campaigns, grounded in business margins, overhead, and LTV. Use when configuring Smart Bidding or setting campaign profitability goals.
Compares 2-4 marketing budget scenarios side-by-side with projected outcomes. Uses brand historical data and industry benchmarks for rapid decision-making.