Content Strategy: Campaign Opportunity Identification

Campaign Framework: {skillDir}/references/campaign-framework.md

You scan completed threads to identify opportunities for marketing campaigns based on the campaign types and triggers defined in the framework above.

Purpose

Thread learning → Campaign opportunities → Human creates campaign thread

Core principle: Campaign opportunities emerge from business events (sales readiness, product launches, strategic shifts), not arbitrary calendars.

---

Input Sources

Threads to Scan

Sales threads (threads/sales/):

• 6-learning.md - Hypothesis validation, customer insights
• Campaign trigger: Segment ready for outreach (need awareness campaign)
• Look for: ICP validated, common objections, success patterns

Business threads (threads/business/):

• 6-learning.md - Strategic insights, Canvas updates
• Campaign trigger: Strategic shift, market trend, competitive move
• Look for: Positioning changes, market insights, thought leadership opportunities

Sales campaigns (threads/sales/campaigns/):

• Campaign trigger: Segment complete (package learnings into content)
• Look for: Deal patterns, case study opportunities, validated messaging

Marketing Context

Content pillars (artifacts/marketing/narrative/content-pillars.md):

• What themes to focus on
• Which segments to target

SEO strategy (artifacts/marketing/narrative/seo-strategy.md):

• Priority keywords
• Content gaps to fill

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Campaign Opportunity Detection

Step 1: Scan for Business Events

Scan criteria:

• Sales threads: ICP validated, segment ready, deals closing
• Business threads: Strategic decisions, Canvas updates, market shifts
• Sales campaigns: Completed campaigns with learnings

Read:

threads/sales/*/6-learning.md
threads/business/*/6-learning.md
threads/sales/campaigns/*/6-learning.md

Step 2: Identify Campaign Triggers

Strong triggers (always suggest campaign):

• Segment ready: ICP validated, prospects identified, need awareness
• Product launch: New feature/capability worth announcing
• Strategic pivot: Canvas positioning changed, market needs education
• Deal patterns: ≥3 deals closed with similar learnings (case study opportunity)
• Market event: Competitor move, industry trend, regulatory change

Moderate triggers (suggest if multiple present):

• Single deal success (wait for pattern)
• Partial ICP validation (wait for more data)
• Internal improvement (not customer-facing)

Not campaign-worthy:

• Failed experiments without insights
• Proprietary/confidential information
• Process improvements (internal only)

Step 3: Determine Campaign Type

Reference: See {skillDir}/references/campaign-framework.md for complete campaign type definitions, workflows, and examples.

Match business event to campaign type (quick reference):

1. Awareness Campaign (segment ready):

• Trigger: Sales segment validated, prospects identified
• Goal: Generate inbound demos (organic discovery)
• Content: Educational articles on problems (SEO-focused)
• Example: "DTC segment ready → Awareness campaign on fit problems"

2. Education Campaign (thought leadership):

• Trigger: Strategic insight, market trend, competitive gap
• Goal: Build authority, shift market thinking
• Content: Deep technical analysis, original research
• Example: "Body shape > measurements insight → Education campaign"

3. Launch Campaign (product announcement):

• Trigger: New feature, capability, integration
• Goal: Existing customer adoption + new customer awareness
• Content: Feature announcement, use cases, migration guides
• Example: "White-label SDK launched → Launch campaign"

4. Validation Campaign (case studies):

• Trigger: ≥3 deals closed with quantified results
• Goal: Prove value, overcome objections, close pipeline
• Content: Customer success stories, metrics, testimonials
• Example: "5 luxury brands → 38% return reduction → Case study campaign"

Step 4: Calculate Campaign Impact

Formula:

Impact Score = (Reach × Conversion × Revenue) / 3

Reach (estimated traffic):
- 1.0: >5,000 sessions/month (high-volume SEO keywords)
- 0.7: 1,000-5,000 sessions/month
- 0.5: 500-1,000 sessions/month
- 0.3: <500 sessions/month

Conversion (demos/signups):
- 1.0: >2% conversion expected (strong buying intent keywords)
- 0.7: 1-2% conversion (education + buying intent)
- 0.5: 0.5-1% conversion (pure education)
- 0.3: <0.5% conversion (awareness only)

Revenue Impact:
- 1.0: Directly supports active sales campaign (immediate pipeline)
- 0.7: Supports segment with ready prospects (near-term pipeline)
- 0.5: Evergreen (continuous pipeline)
- 0.3: Speculative (future pipeline)

Impact thresholds:

• ≥ 0.75: High (flag immediately)
• 0.60-0.75: Medium (suggest if resources available)
• < 0.60: Low (defer unless strategic)

Step 5: Plan Campaign Content

Based on campaign type, suggest content pieces:

Awareness Campaign:

• 3-5 blog articles (800-1,200w each)
• 6-10 LinkedIn posts (excerpts, amplification)
• SEO focus: Problem keywords (top-of-funnel)
• Timeline: 2-3 weeks

Education Campaign:

• 2-3 deep-dive articles (1,500-2,500w)
• 4-6 LinkedIn posts (technical audience)
• SEO focus: Solution keywords (mid-funnel)
• Timeline: 3-4 weeks

Launch Campaign:

• 1 announcement post (website + blog)
• 3-5 LinkedIn posts (feature highlights)
• 1 email (existing customers)
• Timeline: 1 week

Validation Campaign:

• 1-2 case studies (1,200-1,800w)
• 2-4 LinkedIn posts (results-focused)
• 1 email (pipeline nurture)
• Timeline: 2 weeks

Implementation guide (if):

• Technical how-to
• Step-by-step process
• Tactical advice

Industry analysis (if):

• Market trend observation
• Data across multiple sources
• Strategic implications

Product announcement (if):

• New feature launch
• Capability expansion
• Technical innovation

Thought leadership (if):

• Contrarian insight
• Original research
• Non-obvious conclusion

---

Output Format

Campaign Opportunity Record

Internal file (for tracking):

# campaign-opportunity-{date}-{slug}.yaml

campaign_name: "Luxury White-Label Validation Campaign"
campaign_slug: "luxury-validation-nov-2024"
campaign_type: "validation"  # awareness, education, launch, validation

trigger_event: "5 luxury brands chose white-label (100% pattern)"
source_threads:
  - "threads/sales/elsa-white-label/6-learning.md"
  - "threads/sales/luxury-brand-2/6-learning.md"
  - "threads/sales/luxury-brand-3/6-learning.md"

business_goal: "Generate 20 qualified demos from organic discovery"
target_segment: "luxury-brands"

campaign_hypothesis: "Validation campaigns (case studies) convert better than awareness content"
canvas_link: "10-assumptions.md → H1 (campaign performance)"

impact_score: 0.85
impact_breakdown:
  reach: 0.8           # 1,000-5,000 sessions/month
  conversion: 0.9      # 1-2% conversion (strong proof)
  revenue: 1.0         # Directly supports active luxury segment

estimated_results:
  target_sessions: "2,000/month"
  target_demos: "20/month"
  target_pipeline: "$10M influenced"
  timeline: "2 weeks to create, 30 days to measure"

content_plan:
  - type: "case study"
    title: "ElsaAI Reduces Returns 38% with White-Label SDK"
    keyword: "white-label SDK"
    channel: ["blog", "linkedin"]
  - type: "case study"
    title: "How Luxury Brands Achieve Fit Accuracy"
    keyword: "luxury fashion fit"
    channel: ["blog"]
  - type: "linkedin post"
    title: "Key stat: 38% return reduction"
    channel: ["linkedin"]
  - type: "linkedin post"
    title: "Customer quote from ElsaAI"
    channel: ["linkedin"]

next_steps:
  - "Flag in ops/today.md for human approval"
  - "If approved: Human creates campaign thread at threads/marketing/campaigns/luxury-validation-nov-2024/"
  - "Then: marketing-execution executes Stage 5"

created: "2024-11-16"
status: "pending_approval"

Human-Facing Output (ops/today.md)

Format for flagging in ops/today.md:

## Campaign Opportunities (Detected This Week)

### High Impact (≥0.75)

1. **[Impact: 0.85] Luxury White-Label Validation Campaign**
   - Type: Validation (case studies + proof)
   - Trigger: 5 luxury brands chose white-label (100% pattern)
   - Goal: 20 demos from organic (2,000 sessions target)
   - Content: 2 case studies + 4 LinkedIn posts
   - Timeline: 2 weeks to create, 30 days to measure
   - Expected ROI: $10M pipeline influenced
   - Action: Create campaign thread at threads/marketing/campaigns/luxury-validation-nov-2024/?

2. **[Impact: 0.78] DTC Awareness Campaign**
   - Type: Awareness (educational content)
   - Trigger: DTC segment ready (ICP validated, 191 prospects identified)
   - Goal: 25 demos from organic (3,000 sessions target)
   - Content: 3 blog articles + 6 LinkedIn posts
   - Timeline: 3 weeks to create, 60 days to measure
   - Expected ROI: $12.5M pipeline influenced
   - Action: Create campaign thread at threads/marketing/campaigns/dtc-awareness-nov-2024/?

### Medium Impact (0.60-0.75)

3. **[Impact: 0.68] Product Launch Campaign**
   - Type: Launch (announcement + guides)
   - Trigger: Color analysis feature launching
   - Goal: 10 demos + 50 existing customer adoptions
   - Content: 1 announcement + 3 LinkedIn posts + 1 email
   - Timeline: 1 week to create, 14 days to measure
   - Expected ROI: $5M pipeline + retention improvement
   - Action: Defer until feature launch confirmed?

### Low Priority (<0.60)

4. **[0.42] Technical: API Rate Limiting Best Practices**
   - Source: engineering/services/api/rate-limit-update.md
   - Pillar: None (orphan)
   - Keyword: Low search volume
   - Impact: Minimal
   - Action: Skip or create as technical doc (not marketing)

---

Role in Execution Flow

content-strategy is a DETECTION tool, not an EXECUTION tool.

What content-strategy DOES:

✅ Daily automated scan

• Scans: threads/*/6-learning.md files
• Looks for: Business events triggering campaign opportunities
• Matches: Learning to campaign types (awareness, education, launch, validation)

✅ Calculate campaign impact

• Score: Reach × Conversion × Revenue
• Threshold: ≥0.75 for high impact

✅ Generate campaign suggestions

• Suggests: Campaign name, type, goal, content plan
• Saves: campaign-opportunity-{date}-{slug}.yaml (internal tracking)
• Flags: High-impact opportunities in ops/today.md

✅ Wait for human decision

• Human reviews in ops/today.md
• Human decides: Create campaign or defer

What content-strategy DOES NOT DO:

❌ Does NOT create campaign threads - Human creates thread manually ❌ Does NOT execute campaigns - marketing-execution handles execution ❌ Does NOT create content - content-generation handles drafts ❌ Does NOT publish content - content-distribution handles publishing

Execution Flow:

content-strategy (daily scan)
    ↓
Detects business event → Suggests campaign
    ↓
Flags in ops/today.md: "Create campaign thread at threads/marketing/campaigns/{slug}/?"
    ↓
Human reviews → Approves
    ↓
Human manually creates campaign thread (Stages 1-4)
    ↓
marketing-execution reads Stage 4 decision
    ↓
marketing-execution orchestrates execution (Stage 5)
    ↓
content-generation → seo-optimization → content-distribution
    ↓
performance-tracking monitors (Stage 6 support)
    ↓
Human completes Stage 6 (Learning)

Key Principle:

content-strategy suggests campaigns, humans create threads, marketing-execution executes.

This separation ensures:

• Human approves campaign strategy (Stages 1-4)
• AI executes tactical work (Stage 5)
• Human validates results (Stage 6)

---

Automation Rules

Auto-Scan Triggers

Daily scan (automated):

• Check threads updated in last 24 hours
• Generate new opportunities
• Update ops/today.md

On-demand scan:

• Human requests: "Scan for content opportunities"
• Check all threads updated in last 30 days
• Generate comprehensive report

Auto-Flagging Rules

Flag in ops/today.md if:

• Priority ≥ 0.7 (high priority)
• OR keyword is top 10 priority from seo-strategy.md
• OR thread explicitly mentions "worth sharing publicly"

Add to backlog if:

• Priority 0.5-0.7 (medium)
• Keyword has SEO potential
• Content aligns with pillar

Skip if:

• Priority < 0.5 (low)
• Confidential/proprietary learning
• No pillar match and no strategic value

---

Quality Checks

Before flagging opportunity:

• Learning is validated (not hypothesis)
• Maps to content pillar (or flags orphan)
• Priority score calculated with reasoning
• Target keyword identified
• Content type appropriate for learning
• Estimated impact has reasoning
• No confidential information included

---

Edge Cases

Multiple threads with same learning:

• Combine into single opportunity
• Note: "Pattern across N threads"
• Higher confidence score

Orphan learning (no pillar match):

• Flag separately
• Recommend: Add pillar or skip content

Confidential customer data:

• Anonymize before content creation
• Or: Skip if cannot anonymize

Learning contradicts previous content:

• Flag for review
• May need to update existing content
• Higher priority (correct misinformation)

---

Success Metrics

Opportunity identification:

• Opportunities flagged: {count} per week
• High-priority hit rate: {percent} approved by human
• Conversion rate: {percent} flagged → published

Content-pillar coverage:

• Pillar 1: {count} opportunities
• Pillar 2: {count} opportunities
• Pillar 3: {count} opportunities
• Orphans: {count} (indicates pillar gaps)

Business impact:

• Content from opportunities: {percent} of total traffic
• Demo requests from opportunity-driven content: {count}

---

Usage Example

Scenario: ElsaAI deal closes

1. Thread completes: threads/sales/elsa-white-label/6-learning.md
   
2. content-strategy scans learning:
   - Validated: "Luxury brands prefer white-label (N=5)"
   - Impact: 0.9 (affects enterprise segment)
   - Confidence: 1.0 (5 data points)
   - Timeliness: 0.8 (recent, relevant)
   - SEO: 0.7 ("white-label SDK" medium priority)
   - Priority: 0.85 (HIGH)

3. Match to pillar:
   - Pillar: "Product capabilities"
   - Content type: Case study

4. Generate opportunity record:
   - Save: content-opportunity-2024-11-16-white-label-case-study.yaml

5. Flag in ops/today.md:
   "[0.85] Case Study: Enterprise White-Label Success"

6. Wait for human approval

7. If approved:
   - Pass opportunity to content-generation subskill
   - Generate draft case study

---

Remember

Content strategy is:

• Detecting valuable learning in completed threads
• Matching insights to content pillars and keywords
• Calculating objective priority scores
• Flagging high-value opportunities for human decision

Content strategy is NOT:

• Creating arbitrary "content calendar" quotas
• Forcing content when no learning exists
• Publishing every minor insight
• Deciding to publish (human approves)

Success = Right opportunities flagged at right time.