---

id: marketing-templates title: Marketing Agent Prompt Templates project: Marketing tags: [soulfield/agents, marketing, templates, prompts] created: 2025-10-04 status: active

Marketing Agent Prompt Templates

Template Library Structure

Each template follows the Deliverable-First Framework with 4 parts:

1. Context & Purpose - Industry, audience, goals, constraints
2. Specific Components - Required deliverable parts
3. Data Structure - Format, fields, validation rules
4. Quality Checks - Acceptance criteria

---

Category 1: Campaign Planning

Template 1.1: Marketing Funnel Design

Purpose: Design complete marketing funnel from awareness to retention with channel strategy and conversion optimization.

When to Use: New product launch, market entry, funnel restructure, channel expansion.

Template:

You are designing a marketing funnel for [PRODUCT/SERVICE] targeting [AUDIENCE].

## Context & Purpose
- Industry: [e.g., B2B SaaS, e-commerce, local services]
- Target audience: [demographics, psychographics, behavior]
- Primary goal: [conversions, revenue, market share]
- Budget: [total monthly budget]
- Timeline: [launch date, ramp-up period]
- Constraints: [team size, technical limitations, compliance]

## Funnel Components

### Stage 1: Awareness (Top of Funnel)
- Channels: [paid search, SEO, social, display, PR]
- Content types: [blog posts, videos, infographics, podcasts]
- Metrics: [impressions, reach, brand lift]
- Budget allocation: [% of total]

### Stage 2: Consideration (Middle of Funnel)
- Tactics: [retargeting, email nurture, webinars, case studies]
- Content types: [whitepapers, demos, comparison guides]
- Metrics: [engagement rate, time on site, download rate]
- Budget allocation: [% of total]

### Stage 3: Conversion (Bottom of Funnel)
- Tactics: [free trials, consultations, limited offers]
- Content types: [product pages, testimonials, ROI calculators]
- Metrics: [conversion rate, CAC, close rate]
- Budget allocation: [% of total]

### Stage 4: Retention (Post-Purchase)
- Tactics: [onboarding, upsells, loyalty programs, referrals]
- Content types: [tutorials, success stories, exclusive content]
- Metrics: [churn rate, LTV, NPS, referral rate]
- Budget allocation: [% of total]

## Data Structure

Deliver as JSON with this schema:
{
  "funnel_stages": [
    {
      "stage": "awareness|consideration|conversion|retention",
      "channels": ["channel1", "channel2"],
      "tactics": ["tactic1", "tactic2"],
      "content_types": ["type1", "type2"],
      "budget_pct": 0-100,
      "metrics": {
        "primary": "metric_name",
        "target": "numeric_value",
        "tracking": "tool_name"
      }
    }
  ],
  "timeline": {
    "weeks_1_4": "focus_area",
    "weeks_5_8": "focus_area",
    "weeks_9_12": "focus_area"
  },
  "assumptions": ["assumption1", "assumption2"],
  "risks": ["risk1", "risk2"]
}

## Quality Checks
- [ ] Budget adds to 100% across stages
- [ ] Metrics aligned with business goals
- [ ] Channel mix matches audience behavior
- [ ] Timeline accounts for creative production
- [ ] Retention tactics defined (not just acquisition)
- [ ] Assumptions explicitly marked
- [ ] At least 3 channels per stage for resilience

Example Output Structure:

{
  "funnel_stages": [
    {
      "stage": "awareness",
      "channels": ["google_search", "linkedin_ads", "seo_content"],
      "tactics": ["competitor_keywords", "thought_leadership", "blog_seo"],
      "content_types": ["how-to_guides", "industry_reports", "explainer_videos"],
      "budget_pct": 40,
      "metrics": {
        "primary": "impressions",
        "target": "500000_monthly",
        "tracking": "google_analytics_4"
      }
    }
  ],
  "assumptions": [
    "[HYPOTHESIS] LinkedIn CPM remains under $15",
    "[ASSUMPTION] SEO traffic grows 20% monthly"
  ]
}

---

Template 1.2: Audience Targeting Strategy

Purpose: Define target audience segments with demographics, psychographics, messaging, and channel preferences.

When to Use: New market research, persona development, campaign segmentation, messaging refinement.

Template:

You are creating audience targeting strategy for [PRODUCT/SERVICE] in [MARKET].

## Context & Purpose
- Market: [geographic, industry, vertical]
- Product/service: [description, value proposition]
- Current audience data: [CRM stats, analytics, surveys]
- Business goal: [customer acquisition, market penetration, brand awareness]

## Audience Segments (Define 3-5)

For each segment:

### Segment [N]: [Name]

**Demographics:**
- Age range: [e.g., 25-34]
- Gender: [if relevant]
- Income: [range or bracket]
- Location: [geographic specifics]
- Job title/role: [for B2B]
- Company size: [for B2B]

**Psychographics:**
- Values: [what they care about]
- Pain points: [problems they face]
- Goals: [what they want to achieve]
- Decision drivers: [price, quality, speed, support]
- Media consumption: [where they spend time]

**Behavioral:**
- Purchase triggers: [what prompts action]
- Research behavior: [how they evaluate options]
- Channel preferences: [email, social, search, direct]
- Buying cycle: [impulsive, considered, lengthy]

**Messaging:**
- Primary value prop: [tailored to segment]
- Tone: [professional, casual, technical]
- Key differentiators: [vs competitors]
- CTAs: [what action to request]

**Channel Strategy:**
- Primary channels: [ranked by effectiveness]
- Content formats: [video, text, audio]
- Ad platforms: [Google, Meta, LinkedIn, etc.]
- Estimated reach: [audience size]

## Data Structure

Deliver as JSON:
{
  "segments": [
    {
      "name": "segment_name",
      "size_estimate": "numeric_value",
      "revenue_potential": "$X_per_customer",
      "demographics": {...},
      "psychographics": {...},
      "behavioral": {...},
      "messaging": {
        "value_prop": "text",
        "tone": "descriptor",
        "cta": "action_phrase"
      },
      "channels": [
        {
          "name": "channel_name",
          "priority": 1-3,
          "cpm_estimate": "$X",
          "conversion_estimate": "X%"
        }
      ]
    }
  ],
  "cross_segment_insights": ["insight1", "insight2"],
  "data_gaps": ["gap1", "gap2"]
}

## Quality Checks
- [ ] 3-5 distinct segments (not overlapping)
- [ ] Each segment has unique messaging
- [ ] Channel mix differs by segment
- [ ] Revenue potential calculated
- [ ] Data sources cited for estimates
- [ ] Unknowns marked [UNKNOWN]
- [ ] Segment size >10k for paid advertising viability

---

Category 2: Growth Strategy

Template 2.1: Acquisition Channel Analysis

Purpose: Evaluate and prioritize customer acquisition channels based on CAC, volume, quality, and scalability.

When to Use: Budget allocation, channel testing, growth planning, performance optimization.

Template:

You are analyzing acquisition channels for [BUSINESS] to optimize [GOAL].

## Context & Purpose
- Business model: [B2B, B2C, marketplace, SaaS]
- Current channels: [list existing channels with spend]
- Target CAC: [$X per customer]
- Monthly budget: [$X total]
- Growth goal: [X new customers per month]
- Timeline: [immediate, 3 months, 6 months]

## Channel Evaluation Framework

For each channel, analyze:

### Channel: [Name]

**Volume Potential:**
- Addressable audience: [size estimate]
- Current monthly reach: [if active]
- Saturation point: [max monthly conversions]
- Growth trajectory: [flat, growing X%, declining]

**Cost Structure:**
- CPM/CPC: [$X current or estimated]
- Conversion rate: [X% or [UNKNOWN]]
- CAC: [$X calculated or projected]
- CAC trend: [increasing, stable, decreasing]

**Quality Metrics:**
- LTV: [$X per customer from this channel]
- Payback period: [X months]
- Churn rate: [X% vs overall avg]
- NPS/satisfaction: [score or qualitative]

**Scalability:**
- Investment required: [$X to scale 10x]
- Operational complexity: [low, medium, high]
- Team expertise: [have, need, can hire]
- Tech requirements: [tools, platforms, integrations]

**Strategic Fit:**
- Brand alignment: [strong, moderate, weak]
- Competitive intensity: [low, medium, high]
- Defensibility: [easy to copy, moderately unique, highly proprietary]
- Future outlook: [growing channel, stable, declining]

## Channel Prioritization Matrix

|Channel|CAC|Volume|Quality (LTV/CAC)|Scalability|Priority Score|
|-------|---|------|----------------|-----------|-------------|
|[Name] |$X |X/mo  |X.X             |1-5        |X/20         |

Priority formula: (Volume/1000) + (LTV/CAC * 3) + (Scalability * 2) - (CAC/100)

## Budget Allocation Recommendation

DATA: Current performance metrics
INTERPRETATION: Channel efficiency analysis
SPECULATION: [HYPOTHESIS] Projected impact of reallocation

Recommended split:
- Channel A: X% ($X/month) - BECAUSE [causal reasoning]
- Channel B: X% ($X/month) - BECAUSE [causal reasoning]
- Testing budget: X% ($X/month) - FOR [new channels to test]

## Data Structure

{
  "channels": [
    {
      "name": "channel_name",
      "status": "active|testing|proposed",
      "metrics": {
        "cac": "$X",
        "ltv": "$X",
        "ltv_cac_ratio": "X.X",
        "monthly_volume": "X",
        "conversion_rate": "X%"
      },
      "budget_recommendation": {
        "monthly_spend": "$X",
        "pct_of_total": "X%",
        "rationale": "because_statement"
      },
      "risks": ["risk1", "risk2"],
      "testing_plan": "if_status_testing"
    }
  ],
  "overall_strategy": {
    "primary_channel": "name",
    "growth_channels": ["name1", "name2"],
    "experimental_channels": ["name1", "name2"]
  }
}

## Quality Checks
- [ ] All channels have CAC calculated or estimated
- [ ] LTV/CAC ratio >3 for primary channels
- [ ] Budget totals 100%
- [ ] At least 10% allocated to testing
- [ ] Causal reasoning for each allocation
- [ ] Data sources cited
- [ ] Unknowns marked [UNKNOWN]
- [ ] Scalability constraints identified

---

Template 2.2: Conversion Optimization Playbook

Purpose: Design systematic conversion rate optimization strategy with testing roadmap and implementation plan.

When to Use: Landing page optimization, checkout flow improvement, lead gen enhancement, activation rate increase.

Template:

You are optimizing conversion for [PAGE/FLOW] to improve [METRIC] from [CURRENT] to [TARGET].

## Context & Purpose
- Page/flow: [landing page, checkout, signup, onboarding]
- Current conversion rate: [X%]
- Target conversion rate: [X%]
- Traffic volume: [X visits/month]
- Revenue impact: [$X per 1% improvement]
- Timeline: [X weeks for testing]

## Conversion Audit

### Current State Analysis
- Funnel drop-off points: [step 1: X%, step 2: X%]
- Average time on page: [X seconds]
- Bounce rate: [X%]
- Device breakdown: [desktop X%, mobile X%]
- Traffic sources: [organic X%, paid X%, direct X%]

### Friction Points Identified
1. [Issue]: [Description] - IMPACT: [high/medium/low]
2. [Issue]: [Description] - IMPACT: [high/medium/low]
3. [Issue]: [Description] - IMPACT: [high/medium/low]

### Opportunity Areas
1. [Element]: [Current state] → [Proposed change] - LIFT: [estimated X% improvement]
2. [Element]: [Current state] → [Proposed change] - LIFT: [estimated X% improvement]

## A/B Testing Roadmap

### Test 1: [Hypothesis Name]
**Hypothesis:** IF [change] THEN [expected outcome] BECAUSE [reasoning]

**Variations:**
- Control: [current version description]
- Variant A: [change description]
- Variant B: [optional second variation]

**Success Metrics:**
- Primary: [conversion rate increase >X%]
- Secondary: [engagement metric, revenue, time on page]
- Guardrail: [metric that shouldn't decrease]

**Sample Size:** [X visitors per variation]
**Duration:** [X days to significance]
**Statistical Power:** [95% confidence, 80% power]

**Implementation:**
- Design changes: [list specific changes]
- Copy changes: [before/after text]
- Technical requirements: [tracking, tools]

### Test 2-5: [Repeat structure]

## Prioritization Framework

|Test|Est. Lift|Effort|Confidence|Priority Score|
|----|---------|------|----------|--------------|
|[#1]|X%       |S/M/L |High/Med  |X/10          |

Priority = (Est. Lift * Confidence) / Effort

## Implementation Plan

**Week 1-2:** [Tests to launch]
**Week 3-4:** [Tests to launch]
**Week 5-6:** [Tests to launch]
**Week 7-8:** [Analysis and rollout]

## Data Structure

{
  "tests": [
    {
      "id": "test_identifier",
      "hypothesis": "if_then_because_statement",
      "variations": [
        {
          "name": "control|variant_a|variant_b",
          "description": "text",
          "traffic_split": "X%"
        }
      ],
      "metrics": {
        "primary": "conversion_rate",
        "target_lift": "X%",
        "statistical_significance": "95%"
      },
      "timeline": {
        "start_date": "YYYY-MM-DD",
        "min_duration": "X_days",
        "sample_size": "X_visitors"
      },
      "implementation": {
        "effort": "S|M|L",
        "dependencies": ["dep1", "dep2"],
        "tools": ["tool1", "tool2"]
      }
    }
  ],
  "expected_impact": {
    "current_cr": "X%",
    "projected_cr": "X%",
    "annual_revenue_lift": "$X",
    "confidence": "high|medium|low"
  }
}

## Quality Checks
- [ ] Each test has clear hypothesis
- [ ] Sample size calculated for statistical power
- [ ] Guardrail metrics defined
- [ ] Tests prioritized by impact/effort
- [ ] Implementation dependencies identified
- [ ] Rollout plan for winners
- [ ] Unknowns marked [HYPOTHESIS]
- [ ] Causal reasoning for expected lifts

---

Category 3: Content Calendars

Template 3.1: Multi-Channel Content Calendar

Purpose: Create coordinated content publishing schedule across blog, social, email, and video with SEO integration.

When to Use: Content strategy planning, editorial calendar creation, campaign coordination, team alignment.

Template:

You are building a content calendar for [BRAND] across [CHANNELS] for [TIMEFRAME].

## Context & Purpose
- Brand: [name, industry, voice]
- Channels: [blog, LinkedIn, Twitter, YouTube, email, etc.]
- Timeframe: [Q1 2025, next 90 days, etc.]
- Content team: [size, roles, capacity]
- Business goals: [traffic, leads, brand awareness]
- Key themes: [product launches, seasonal, thought leadership]

## Content Strategy

### Theme 1: [Name]
- Business objective: [why this theme]
- Target audience: [who this serves]
- Content pillars: [3-5 sub-topics]
- SEO keywords: [primary keyword, secondary keywords]
- Success metrics: [traffic, engagement, conversions]

### Theme 2-3: [Repeat structure]

## Calendar Structure (Per Week)

### Week [N]: [Theme Name]

**Monday:**
- Blog: [Topic] - [SEO keyword] - [CTA: newsletter signup]
  - Word count: [X words]
  - Internal links: [link to page A, B]
  - Publish time: [9 AM EST]

- LinkedIn: [Post type: carousel/video/text]
  - Content: [Summary or hook]
  - Link to: [blog post]
  - Hashtags: [#tag1, #tag2]

**Tuesday:**
- Twitter: [Thread or single tweet]
  - Content: [Key insight from blog]
  - Visual: [yes/no]

- Email: [Segment: all subscribers]
  - Subject: [text]
  - Preview text: [text]
  - Content blocks: [1. intro, 2. blog link, 3. CTA]

**Wednesday:**
- YouTube: [Video type: tutorial/interview/explainer]
  - Title: [SEO-optimized title]
  - Description: [text with timestamps]
  - Thumbnail: [description]
  - Call-out in blog post

**Thursday:**
- LinkedIn: [Repurpose blog section as standalone post]
  - Focus: [specific angle]

- Twitter: [Poll or question]
  - Topic: [related to theme]

**Friday:**
- Blog: [Roundup post or case study]
  - Round up week's content
  - Internal linking: [all week's posts]

- Email: [Newsletter to engaged segment]
  - Weekly digest format

## SEO Integration

**Keyword Mapping:**
|Week|Primary Keyword|Search Volume|Difficulty|Target URL|
|----|---------------|-------------|----------|----------|
|1   |[keyword]      |X/mo         |X/100     |/blog/slug|

**Internal Linking Strategy:**
- Hub page: [/resource-center] links to all pillar posts
- Pillar posts: [/topic-guide] links to supporting blog posts
- Supporting posts: Link to pillar and related posts

## Production Workflow

**Week N-2:** Ideation and outlining
**Week N-1:** Drafting and review
**Week N:** Publishing and promotion
**Week N+1:** Performance analysis

## Data Structure

{
  "calendar": [
    {
      "week": "N",
      "theme": "theme_name",
      "content_items": [
        {
          "day": "monday",
          "channel": "blog",
          "title": "text",
          "type": "how-to|listicle|case-study",
          "seo_keyword": "keyword",
          "word_count": X,
          "cta": "newsletter|demo|download",
          "publish_time": "HH:MM TZ",
          "promotion": [
            {"channel": "linkedin", "format": "carousel"},
            {"channel": "twitter", "format": "thread"}
          ]
        }
      ],
      "weekly_metrics": {
        "target_traffic": "X visits",
        "target_leads": "X leads",
        "target_engagement": "X likes/shares"
      }
    }
  ],
  "team_capacity": {
    "writers": X,
    "designers": X,
    "editors": X,
    "posts_per_week": X
  },
  "dependencies": ["SEO research complete", "templates ready"]
}

## Quality Checks
- [ ] Each piece of content has clear CTA
- [ ] SEO keywords mapped to URLs
- [ ] Internal linking plan documented
- [ ] Team capacity not exceeded
- [ ] Content repurposed across 3+ channels
- [ ] Promotion plan for each blog post
- [ ] Metrics defined per week
- [ ] Production buffer (N-2 planning)

---

Category 4: Performance Analysis

Template 4.1: Marketing Metrics Dashboard

Purpose: Design comprehensive marketing performance dashboard with KPIs, attribution, and forecasting.

When to Use: Monthly reporting, executive dashboards, campaign analysis, budget justification.

Template:

You are creating a marketing dashboard for [BUSINESS] tracking [METRICS] for [STAKEHOLDERS].

## Context & Purpose
- Business type: [B2B SaaS, e-commerce, local services]
- Stakeholders: [CEO, marketing team, board]
- Reporting frequency: [daily, weekly, monthly]
- Decision use: [budget allocation, hiring, strategy pivots]
- Current tools: [Google Analytics, CRM, ad platforms]

## Dashboard Sections

### Section 1: North Star Metrics (Top-Level KPIs)

**Metric 1: [Name]**
- Definition: [exactly how it's calculated]
- Current value: [X]
- Target: [X]
- Trend: [↑ X% MoM, ↓ X% YoY]
- Status: [🟢 on track | 🟡 at risk | 🔴 behind]

**Why This Metric:**
DATA: [Historical performance]
INTERPRETATION: [What the trend means]
CAUSALITY: IF [leading indicator changes] THEN [this metric responds] BECAUSE [mechanism]

### Section 2: Acquisition Metrics

|Channel|Impressions|Clicks|CTR|Conversions|CVR|CAC|LTV|LTV/CAC|
|-------|-----------|------|---|-----------|---|---|---|-------|
|Google |X          |X     |X% |X          |X% |$X |$X |X.X    |
|Meta   |X          |X     |X% |X          |X% |$X |$X |X.X    |
|[...]  |           |      |   |           |   |   |   |       |
|TOTAL  |X          |X     |X% |X          |X% |$X |$X |X.X    |

**Insights:**
- [Channel] has lowest CAC but [UNKNOWN] if quality is high (need LTV data)
- [Channel] CVR dropped X% → INVESTIGATE: Ad fatigue or audience saturation?

### Section 3: Funnel Metrics

Awareness: [████████████████████] 100,000 visitors Consideration: [████████████] 60,000 engaged (60% drop-off) Conversion: [████] 3,000 trials (95% drop-off) Retention: [██] 300 paid (90% drop-off)

**Conversion Rates:**
- Visitor → Engaged: X% (industry avg: Y%)
- Engaged → Trial: X% (industry avg: Y%)
- Trial → Paid: X% (industry avg: Y%)

**Drop-Off Analysis:**
- Biggest leak: [stage] at X% drop-off
- HYPOTHESIS: [Reason for drop-off]
- TEST PLAN: [A/B test to validate]

### Section 4: Content Performance

|Post|Traffic|Engagement|Leads|Lead %|Status|
|----|-------|----------|-----|------|------|
|[T] |X      |X min     |X    |X%    |🟢    |

**Top Performers:**
1. [Post title] - X leads - BECAUSE [topic resonates with audience segment]
2. [Post title] - X leads - BECAUSE [SEO ranking for high-intent keyword]

### Section 5: Attribution Model

**Model Type:** [First-touch | Last-touch | Multi-touch linear | Time-decay]

|Touchpoint|First-Touch %|Last-Touch %|Multi-Touch %|
|----------|-------------|------------|-------------|
|Organic   |X%           |X%          |X%           |
|Paid      |X%           |X%          |X%           |
|Direct    |X%           |X%          |X%           |

**Attribution Insights:**
- Organic search drives X% of first touches (awareness)
- Paid retargeting drives X% of last touches (conversion)
- INTERPRETATION: Organic builds pipeline, paid closes deals
- BUDGET IMPLICATION: Maintain organic investment for top-of-funnel

### Section 6: Forecasting

**Q[N] Projection (Based on Current Trends):**

IF current CAC trend continues (-X% MoM)
AND traffic growth sustains (+X% MoM)
THEN expect:
- X new customers (confidence: [HYPOTHESIS] 70%)
- $X revenue (confidence: [HYPOTHESIS] 65%)
- $X profit (confidence: [HYPOTHESIS] 60%)

DEPENDS ON:
- No major algorithm changes (Google, Meta)
- Seasonal patterns hold (Q4 spike expected)
- Competitor activity stable

FAILURE MODES:
- CAC spike if competitor increases spend
- Traffic drop if core pages lose rankings
- CVR decline if product-market fit weakens

## Data Structure

{
  "north_star": [
    {
      "metric": "metric_name",
      "value": "X",
      "target": "X",
      "trend": "up|down|flat",
      "mom_change": "X%",
      "status": "green|yellow|red"
    }
  ],
  "channels": [
    {
      "name": "channel_name",
      "metrics": {
        "impressions": X,
        "clicks": X,
        "conversions": X,
        "cac": "$X",
        "ltv": "$X",
        "ltv_cac": X.X
      },
      "trend": "improving|declining|stable",
      "actions": ["action1", "action2"]
    }
  ],
  "funnel": {
    "stages": [
      {
        "name": "awareness",
        "volume": X,
        "conversion_to_next": "X%",
        "benchmark": "X%",
        "gap": "X pp"
      }
    ]
  },
  "forecast": {
    "period": "Q1_2025",
    "customers": {"low": X, "mid": X, "high": X},
    "revenue": {"low": "$X", "mid": "$X", "high": "$X"},
    "confidence": "X%",
    "assumptions": ["assumption1", "assumption2"]
  }
}

## Quality Checks
- [ ] All metrics have targets and benchmarks
- [ ] Trends explained with causal reasoning
- [ ] Forecasts show confidence levels
- [ ] Assumptions explicitly listed
- [ ] Data sources cited
- [ ] Refresh frequency documented
- [ ] Actionable insights per section
- [ ] Unknowns marked [UNKNOWN] or [HYPOTHESIS]

---

Category 5: Brand Positioning

Template 5.1: Messaging Framework

Purpose: Develop complete brand messaging system with value proposition, tagline, elevator pitch, and supporting messages.

When to Use: Rebranding, market repositioning, new product launch, sales enablement, website copy refresh.

Template:

You are developing messaging framework for [BRAND] in [MARKET] targeting [AUDIENCE].

## Context & Purpose
- Brand: [name, industry, stage]
- Market position: [leader, challenger, niche]
- Target audience: [primary and secondary segments]
- Competitive context: [3-5 main competitors]
- Unique capabilities: [what only you can do]
- Business goals: [revenue, market share, category creation]

## Core Positioning Statement

**For** [target customer]
**Who** [customer need or problem]
**Our** [product/service category]
**Provides** [key benefit]
**Unlike** [competitive alternatives]
**We** [unique differentiator]

Example:
"For fast-growing B2B SaaS companies who struggle with fragmented customer data, our customer data platform provides a single source of truth that improves marketing ROI by 40%. Unlike legacy CRMs that require months of integration, we go live in under 2 weeks with zero engineering resources."

## Value Proposition Hierarchy

### Level 1: Core Value Prop (1 sentence)
[The single most important benefit + proof point]

Example: "Reduce customer acquisition cost by 30% with AI-powered attribution modeling."

### Level 2: Supporting Benefits (3-5 bullets)
- [Benefit 1]: [Specific outcome] - BECAUSE [mechanism]
- [Benefit 2]: [Specific outcome] - BECAUSE [mechanism]
- [Benefit 3]: [Specific outcome] - BECAUSE [mechanism]

### Level 3: Feature-Benefit Mapping
|Feature|Benefit|Customer Impact|
|-------|-------|---------------|
|[Tech] |[What it enables]|[Business outcome]|

## Messaging Pillars (3-5 Core Themes)

### Pillar 1: [Theme Name]
**Headline:** [Attention-grabbing claim]
**Subhead:** [Supporting detail with proof]
**Body:**
- DATA: [Statistic or research backing]
- INTERPRETATION: [What this means for customer]
- PROOF: [Case study, testimonial, or data]

**Target Audience:** [Which segment this resonates with]
**Use Cases:** [Where to deploy this message]

### Pillar 2-5: [Repeat structure]

## Tagline Options

1. **[Option 1]** - [Explanation of positioning angle]
   - Pros: [Strength 1, Strength 2]
   - Cons: [Weakness 1, Weakness 2]

2. **[Option 2]** - [Explanation of positioning angle]
   - Pros: [Strength 1, Strength 2]
   - Cons: [Weakness 1, Weakness 2]

RECOMMENDATION: [Chosen tagline] BECAUSE [Strategic rationale with causal reasoning]

## Elevator Pitch (30-second, 60-second, 2-minute versions)

### 30-Second Version (75 words)
[Hook: Problem or surprising stat]
[Solution: What you do]
[Proof: Key metric or customer]
[CTA: Next step]

### 60-Second Version (150 words)
[Expand on problem with context]
[Explain solution with unique approach]
[Add 2-3 proof points]
[Explain why now matters]
[Clear CTA with friction removal]

### 2-Minute Version (300 words)
[Full problem elaboration]
[Solution with demonstration]
[Multiple proof points]
[Competitive differentiation]
[Vision and roadmap tease]
[Strong CTA with urgency]

## Voice & Tone Guidelines

**Brand Voice Attributes:**
- [Attribute 1]: [Definition] - Example: "Confident but not arrogant"
- [Attribute 2]: [Definition] - Example: "Technical but accessible"
- [Attribute 3]: [Definition] - Example: "Ambitious but realistic"

**Tone Variations by Context:**
- Website homepage: [Inspiring, aspirational]
- Product pages: [Detailed, proof-driven]
- Pricing page: [Transparent, reassuring]
- Support docs: [Clear, patient]
- Social media: [Conversational, human]

**Language Dos and Don'ts:**

DO:
- Use active voice: "We reduce CAC" not "CAC is reduced"
- Lead with outcomes: "Grow revenue 40%" before "Advanced analytics"
- Quantify claims: "3x faster" not "significantly faster"

DON'T:
- Use jargon without definition
- Make unsubstantiated claims
- Copy competitor language
- Use weak qualifiers ("try to", "help with")

## Competitive Differentiation

|Competitor|Their Message|Our Counter-Message|Proof Point|
|----------|-------------|-------------------|-----------|
|[Name]    |[Their claim]|[Our differentiator]|[Evidence]|

**Key Battlegrounds:**
1. [Competitive dimension]: They say [X], we say [Y] BECAUSE [causal reasoning]
2. [Competitive dimension]: They say [X], we say [Y] BECAUSE [causal reasoning]

## Supporting Evidence Library

**Case Studies:**
1. [Company name]: [Outcome achieved] - [Quote from customer]
2. [Company name]: [Outcome achieved] - [Quote from customer]

**Data Points:**
- [Stat 1]: [Source]
- [Stat 2]: [Source]

**Awards/Recognition:**
- [Recognition 1]: [Granting organization, date]

## Data Structure

{
  "positioning": {
    "target_customer": "segment_description",
    "core_problem": "problem_statement",
    "solution_category": "category_name",
    "key_benefit": "primary_benefit",
    "differentiator": "unique_advantage"
  },
  "value_prop": {
    "core": "one_sentence",
    "supporting_benefits": ["benefit1", "benefit2", "benefit3"]
  },
  "messaging_pillars": [
    {
      "theme": "pillar_name",
      "headline": "text",
      "proof": "data_or_case_study",
      "target_audience": "segment",
      "use_cases": ["website", "sales_deck", "ads"]
    }
  ],
  "tagline": {
    "selected": "tagline_text",
    "rationale": "because_statement"
  },
  "elevator_pitch": {
    "30_sec": "text",
    "60_sec": "text",
    "2_min": "text"
  },
  "voice": {
    "attributes": ["attribute1", "attribute2"],
    "tone_by_context": {...}
  },
  "competitive_moat": [
    {
      "dimension": "feature|price|quality|speed",
      "our_claim": "text",
      "competitor_claim": "text",
      "proof": "evidence"
    }
  ]
}

## Quality Checks
- [ ] Positioning statement passes "so what?" test
- [ ] Value prop leads with outcome (not feature)
- [ ] All claims have proof points
- [ ] Messaging differentiates from top 3 competitors
- [ ] Voice attributes are specific and actionable
- [ ] Elevator pitches tested with target audience
- [ ] Tagline options evaluated against criteria
- [ ] Evidence library includes 5+ case studies
- [ ] No marketing jargon without definition
- [ ] Causal reasoning for all "why" claims

---

Template 5.2: Competitive Positioning Map

Purpose: Map competitive landscape, identify white space, and determine optimal market position.

When to Use: Market entry, repositioning strategy, competitor analysis, blue ocean exploration.

Template:

You are mapping the competitive landscape for [CATEGORY] to position [BRAND].

## Context & Purpose
- Market category: [SaaS, e-commerce, services, etc.]
- Market size: [$X TAM, growing at X% CAGR]
- Your brand: [current position or new entrant]
- Strategic goal: [market share, premium positioning, niche domination]
- Key competitors: [list 5-10 competitors]

## Competitive Axes (Choose 2)

**Axis 1 (X-axis):** [e.g., Price: Low to High]
**Axis 2 (Y-axis):** [e.g., Features: Simple to Complex]

Common axis pairs:
- Price vs Quality
- Features vs Ease of Use
- Customization vs Speed to Value
- Technical vs Non-Technical Users
- Local vs Global Focus
- Full-Service vs DIY

## Competitor Mapping

For each competitor, plot position:

**[Competitor 1]:**
- X-axis position: [Low/Mid/High on scale 1-10]
- Y-axis position: [Low/Mid/High on scale 1-10]
- Market share: [X%]
- Positioning claim: "[Their tagline or value prop]"
- Target customer: [Segment they serve]
- Strengths: [2-3 key advantages]
- Weaknesses: [2-3 vulnerabilities]

[Repeat for all competitors]

## White Space Analysis

**Underserved Positions:**
1. [Position description, e.g., "High-quality, low-price"]
   - Market size: [ESTIMATE] $X or [UNKNOWN]
   - Customer need: [What they want but can't get]
   - Why underserved: BECAUSE [no competitor can profitably serve this | technology limitations | regulatory barriers]
   - Opportunity: [Potential if you could own this space]

2. [Repeat for 2-3 white space opportunities]

**Overcrowded Positions:**
1. [Position description] - [X competitors clustered here]
   - Why crowded: [Easiest to execute, most profitable, legacy positioning]
   - Risk: Commoditization, price pressure, high CAC

## Recommended Positioning

**Chosen Position:**
- X-axis: [Value on axis 1]
- Y-axis: [Value on axis 2]
- Quadrant: [e.g., "Premium-Complex" or "Budget-Simple"]

**Strategic Rationale:**

IF we position here
THEN we differentiate from [competitors X, Y, Z]
BECAUSE [causal reasoning: market gap, capabilities, customer need]

DEPENDS ON:
- Our ability to deliver [capability]
- Market willingness to pay [price point]
- [Other critical assumption]

RISKS:
- [Competitor A] could move into this space
- Customer education required for new category
- Operational complexity of servicing this segment

**Proof of Concept:**
- [Evidence this position is viable]: Customer interviews, beta tests, competitor weakness

## Perceptual Map Visualization

   High Quality
        │
        │  [Premium Brand 1]
        │         [Premium Brand 2]
        │

────────────┼──────────────────────── Low Price │ High Price │ [YOU] │ [Competitor A] │ [Budget Brand 1] │ Low Quality

**Interpretation:**
- We position [where] to capture [segment]
- Differentiation from nearest competitor: [X units on axis Y]
- HYPOTHESIS: This position is defensible for [X years] because [moat description]

## Positioning Migration Path (If repositioning)

**Current Position:** [Where you are now]
**Target Position:** [Where you want to be]
**Timeline:** [X quarters to complete]

**Phase 1 (Q1):** [Interim positioning]
- Actions: [Product changes, messaging shifts, customer targeting]
- Metrics: [How to measure progress]

**Phase 2 (Q2):** [Interim positioning]
- Actions: [Further evolution]
- Metrics: [How to measure progress]

**Phase 3 (Q3):** [Final positioning achieved]

**Risk:** IF we move too quickly THEN [customer confusion, brand dilution]

## Data Structure

{
  "axes": {
    "x": {"name": "axis_name", "low": "label", "high": "label"},
    "y": {"name": "axis_name", "low": "label", "high": "label"}
  },
  "competitors": [
    {
      "name": "competitor_name",
      "position": {"x": 1-10, "y": 1-10},
      "market_share": "X%",
      "positioning": "their_claim",
      "strengths": ["strength1", "strength2"],
      "weaknesses": ["weakness1", "weakness2"]
    }
  ],
  "white_space": [
    {
      "position": {"x": 1-10, "y": 1-10},
      "description": "text",
      "market_size": "$X or [UNKNOWN]",
      "why_open": "because_statement",
      "opportunity": "text"
    }
  ],
  "recommended_position": {
    "coordinates": {"x": 1-10, "y": 1-10},
    "rationale": "if_then_because_statement",
    "dependencies": ["dep1", "dep2"],
    "risks": ["risk1", "risk2"],
    "proof": "evidence_description"
  }
}

## Quality Checks
- [ ] Axes are meaningful to customers (not just internal)
- [ ] All major competitors plotted
- [ ] White space validated with customer research
- [ ] Recommended position aligns with capabilities
- [ ] Migration path has measurable milestones
- [ ] Causal reasoning for why position is defensible
- [ ] Market size estimates cited or marked [UNKNOWN]
- [ ] Competitive responses anticipated

---

Usage Instructions

How to Invoke Templates

Via HTTP API:

curl -X POST http://localhost:8790/chat \
  -d '{"prompt":"@marketing: use template 1.1 (marketing funnel) for emergency glazier London, B2C local services, £5k monthly budget"}'

Via Council.js:

const result = await council.process({
  text: "@marketing: audience targeting strategy (template 1.2) for B2B SaaS selling to CMOs at 100-500 person companies"
});

Template Selection Logic (in @marketing agent)

Agent will auto-select template based on keywords:

• "funnel", "campaign", "channels" → Template 1.1
• "audience", "persona", "segment" → Template 1.2
• "CAC", "channels", "acquisition" → Template 2.1
• "conversion", "CRO", "A/B test", "landing page" → Template 2.2
• "content calendar", "editorial", "publishing" → Template 3.1
• "dashboard", "metrics", "KPI", "reporting" → Template 4.1
• "messaging", "value prop", "positioning statement" → Template 5.1
• "competitive", "market map", "white space" → Template 5.2

Multi-Template Workflows

Some requests require chaining multiple templates:

Example: Full campaign creation

1. Template 1.2 (Audience Targeting) → Define segments
2. Template 1.1 (Funnel Design) → Map channels to segments
3. Template 3.1 (Content Calendar) → Plan content for each stage
4. Template 4.1 (Dashboard) → Set up tracking

Example: Optimization cycle

1. Template 4.1 (Dashboard) → Identify underperforming area
2. Template 2.2 (CRO Playbook) → Design tests to improve
3. Template 2.1 (Channel Analysis) → Reallocate budget based on results

Validation Rules

Every template output must pass:

1. Structure Validation - All required sections present
2. Truth Lens - Data/interpretation/speculation separated
3. Causality Lens - IF/THEN/BECAUSE reasoning for recommendations
4. Data Quality - Sources cited or unknowns marked
5. Actionability - Clear next steps with owners and timelines
6. Audience Fit - Language matches target stakeholder

Performance Benchmarks

Token Efficiency:

• Average prompt: 1,500 tokens (template + user context)
• Average output: 2,500 tokens (deliverable)
• Total: 4,000 tokens per request ($0.024 per deliverable)

Quality Metrics:

• Deliverable completeness: >95% (all required sections)
• Stakeholder acceptance: >80% (approved without major revisions)
• Unknown marking: >90% (all uncertainties flagged)
• Causal reasoning: >90% (all recommendations have because statements)

Template Maintenance

Monthly Review:

• Check token usage trends (optimize if >5k avg)
• Review stakeholder feedback (update based on complaints)
• Add new templates for recurring requests
• Archive unused templates (if <5 uses per month)

Quality Checks:

• All examples use real-world scenarios (not generic)
• Validation rules enforced in system prompt
• Lens integration tested (outputs pass all 6 lenses)
• Data structure schemas valid JSON