Funnel Planner — Target Setting

Solution Track — Step 3 of 4. Sets data-driven targets for each prioritized initiative.

Inputs Required

• Prioritized initiatives from .agents/mkt/prioritized-initiatives.md (preferred)
• OR: A business with metrics that need targets

Output

• .agents/mkt/targets.md

Quality Gate

Before delivering, verify:

• Every target has a numeric baseline (zero "TBD" values)
• Every target cites a justification: historical data, benchmark, or calculated improvement factor
• 70% test passes: hitting 70% of each target is still valuable
• LTV:CAC ≥ 3:1 (if acquisition targets involved) — or flagged as unhealthy

Chain Position

Previous: mkt-initiative-prioritize | Next: mkt-experiment

Before Starting

Step 0: Product Context

Check for .agents/mkt/product-context.md. If missing: INTERVIEW. Ask the user 8 product questions (what, who, problem, differentiator, proof points, pricing, objections, voice) and save to .agents/mkt/product-context.md. Or recommend running mkt-copywriting to bootstrap it.

Required Artifacts

Optional Artifacts

Read .agents/mkt/prioritized-initiatives.md if it exists — set a target for each "Proceed" initiative. If it doesn't exist, interview the user:

• What business type? (SaaS, e-commerce, B2B services, etc.)
• What stage? (Pre-launch, early traction, growth, mature)
• What metrics need targets?

Step 1: Choose Funnel Model

See references/funnel-models.md for detailed stage definitions.

Step 2: Collect Baselines

Baselines are non-negotiable. For each metric, get the current number.

If user lacks data, use WebSearch:

• "[industry] [metric] benchmark [year]" (e.g., "B2B SaaS trial conversion benchmark 2025")
• "[business type] average [metric] by stage" (e.g., "Series A SaaS average churn rate")
• "[metric] good vs bad [industry]" (e.g., "email open rate good vs bad SaaS")

See references/benchmarks.md for reference ranges. Note: benchmarks are starting points, not guarantees.

Step 3: Set Targets

For each initiative/metric:

LTV:CAC sanity check (for acquisition targets):

• LTV:CAC should be ≥ 3:1, ideally 5:1
• Payback < 12 months (SMB) or < 18 months (mid-market)
• If targets imply unhealthy economics, flag it explicitly

See references/unit-economics.md for formulas.

Step 4: Validate

Anti-Pattern Check

See references/anti-patterns.md for detailed detection.

Stress Tests

1. Revenue test: If we hit this but revenue doesn't move, was it worth it?
2. 70% test: If we hit 70%, is that still valuable?
3. Ownership test: Who owns this? What are they NOT doing to focus on it?
4. Measurement test: Can we check this weekly, or only at period end?

See references/stress-tests.md for stage-specific questions.

Artifact Template

On re-run: rename existing artifact to targets.v[N].md and create new with incremented version.

---
skill: mkt-funnel-planner
version: 1
date: {{today}}
status: draft
---

# Targets

**Funnel Model:** [AARRR / AIDA / TOFU-MOFU-BOFU]

## Target Table

| Initiative | Metric | Baseline | Benchmark | Target | Justification | Owner |
|-----------|--------|----------|-----------|--------|---------------|-------|
| [Name] | [Metric] | [Current] | [Industry ref] | [Goal] | [Why achievable] | [Person] |

## Validation

### Anti-Patterns: [None detected / List any found + fixes]
### 70% Test: [Pass/fail per target]
### LTV:CAC Check: [Ratio] — [Healthy / Flag]

## Next Step

Run `mkt-experiment` to design minimum viable tests before full rollout.

Worked Example

# Targets

**Date:** 2026-03-13
**Skill:** mkt-funnel-planner
**Funnel Model:** AARRR

## Target Table

| Initiative | Metric | Baseline | Benchmark | Target | Justification | Owner |
|-----------|--------|----------|-----------|--------|---------------|-------|
| Restore Paid Targeting | Paid visitor signup rate | 1.2% | 3-5% (B2B SaaS median) | 3.0% | Was 3.5% before targeting change; conservative recovery with lookalike | Sarah |
| Restore Social Proof | Homepage bounce rate | 52% | 30-40% (B2B SaaS) | 40% | Old homepage was 35%; restoring trust signals should recover most of gap | James |
| Overall | Weekly signups | 200 | — | 300 | Combined effect: paid fix (150 → 225 from paid) + bounce fix (all sources) | Sarah (owner) |

## Validation

### Anti-Patterns: None detected. All targets have baselines, single owners, revenue connection.
### 70% Test: Hitting 70% (2.5% paid rate, 43% bounce, 270 signups) still represents meaningful recovery from 200.
### LTV:CAC Check: Current CAC $120, LTV $1,800 → 15:1 ratio. Healthy even if CAC rises 50%.

## Next Step

Run `mkt-experiment` to design A/B test for paid targeting and before-after for social proof restoration.

References