Self-Improving Marketing

Self-Improving Marketing Skill

Log marketing-specific learnings, campaign issues, and feature requests to markdown files for continuous improvement. Captures messaging misses, channel underperformance, audience drift, brand inconsistency, attribution gaps, and content decay. Important learnings get promoted to brand guidelines, content calendars, channel playbooks, audience personas, or attribution models.

First-Use Initialisation

Before logging anything, ensure the .learnings/ directory and files exist in the project or workspace root. If any are missing, create them:

mkdir -p .learnings
[ -f .learnings/LEARNINGS.md ] || printf "# Marketing Learnings\n\nMessaging misses, audience drift, brand inconsistency, content decay, and channel insights captured during marketing operations.\n\nCategories: messaging_miss | channel_underperformance | audience_drift | brand_inconsistency | attribution_gap | content_decay\nAreas: content | campaigns | seo | social | email | paid_media | analytics\n\n---\n" > .learnings/LEARNINGS.md
[ -f .learnings/CAMPAIGN_ISSUES.md ] || printf "# Campaign Issues Log\n\nCampaign failures, channel problems, messaging errors, and performance issues.\n\n---\n" > .learnings/CAMPAIGN_ISSUES.md
[ -f .learnings/FEATURE_REQUESTS.md ] || printf "# Feature Requests\n\nMarketing tools, automation capabilities, and analytics improvements.\n\n---\n" > .learnings/FEATURE_REQUESTS.md

Never overwrite existing files. This is a no-op if .learnings/ is already initialised.

Do not log customer PII, API keys, ad account credentials, or internal revenue figures. Prefer aggregated metrics and redacted campaign identifiers over raw customer data.

If you want automatic reminders, use the opt-in hook workflow described in Hook Integration.

Quick Reference

| Situation | Action | |-----------|--------| | CTR drops >20% from baseline | Log to .learnings/CAMPAIGN_ISSUES.md with performance details | | Conversion rate declines >15% | Log to .learnings/CAMPAIGN_ISSUES.md with funnel analysis | | Brand guideline violation found | Log to .learnings/LEARNINGS.md with category brand_inconsistency | | Organic traffic falls >25% | Log to .learnings/LEARNINGS.md with category content_decay | | Email bounce rate spikes >5% | Log to .learnings/CAMPAIGN_ISSUES.md with deliverability details | | UTM attribution breaks | Log to .learnings/LEARNINGS.md with category attribution_gap | | Messaging misses target segment | Log to .learnings/LEARNINGS.md with category messaging_miss | | Channel underperforms benchmark | Log to .learnings/LEARNINGS.md with category channel_underperformance | | Audience persona no longer fits | Log to .learnings/LEARNINGS.md with category audience_drift | | Social engagement declines | Log to .learnings/CAMPAIGN_ISSUES.md with engagement metrics | | Recurring campaign pattern | Link with See Also, consider priority bump | | Broadly applicable insight | Promote to brand guidelines, playbook, or persona doc |

OpenClaw Setup (Recommended)

OpenClaw is the primary platform for this skill. It uses workspace-based prompt injection with automatic skill loading.

Installation

Via ClawdHub (recommended):

clawdhub install self-improving-marketing

Manual:

git clone https://github.com/jose-compu/self-improving-marketing.git ~/.openclaw/skills/self-improving-marketing

Workspace Structure

OpenClaw injects these files into every session:

~/.openclaw/workspace/
├── AGENTS.md          # Multi-agent workflows, delegation patterns
├── SOUL.md            # Behavioral guidelines, personality, principles
├── TOOLS.md           # Tool capabilities, integration gotchas
├── MEMORY.md          # Long-term memory (main session only)
├── memory/            # Daily memory files
│   └── YYYY-MM-DD.md
└── .learnings/        # This skill's log files
    ├── LEARNINGS.md
    ├── CAMPAIGN_ISSUES.md
    └── FEATURE_REQUESTS.md

Create Learning Files

mkdir -p ~/.openclaw/workspace/.learnings

Then create the log files (or copy from assets/):

Promotion Targets

When marketing learnings prove broadly applicable, promote them:

| Learning Type | Promote To | Example | |---------------|------------|---------| | Messaging patterns | Brand guidelines | "Enterprise segment requires ROI-first messaging, not feature lists" | | Channel insights | Channel playbooks | "LinkedIn carousel ads outperform single-image by 3x for B2B" | | Audience shifts | Audience personas | "ICP shifted from SMB founders to mid-market VPs of Engineering" | | Content patterns | Content calendar | "Publish comparison posts after competitor launches" | | Attribution fixes | Attribution model | "Always use server-side UTM capture for redirect chains" | | Email patterns | TOOLS.md | "Warm new sending domains for 14 days minimum" |

Optional: Enable Hook

For automatic reminders at session start:

cp -r hooks/openclaw ~/.openclaw/hooks/self-improving-marketing
openclaw hooks enable self-improving-marketing

See references/openclaw-integration.md for complete details.

Generic Setup (Other Agents)

For Claude Code, Codex, Copilot, or other agents, create .learnings/ in the project or workspace root:

mkdir -p .learnings

Create the files inline using the headers shown above.

Add reference to agent files

Add to AGENTS.md, CLAUDE.md, or .github/copilot-instructions.md:

#### Self-Improving Marketing Workflow

When marketing issues or insights are discovered: 1. Log to .learnings/CAMPAIGN_ISSUES.md, LEARNINGS.md, or FEATURE_REQUESTS.md 2. Review and promote broadly applicable learnings to: - Brand guidelines — messaging tone, positioning, and identity rules - Channel playbooks — per-channel strategy and benchmarks - Audience personas — updated ICP definitions and segment profiles - Content calendars — content type cadence and topic frameworks - Attribution models — tagging standards and measurement methodology

Logging Format

Learning Entry [LRN-YYYYMMDD-XXX]

Append to .learnings/LEARNINGS.md:

## [LRN-YYYYMMDD-XXX] category
Logged: ISO-8601 timestamp
Priority: low | medium | high | critical
Status: pending
Area: content | campaigns | seo | social | email | paid_media | analytics
Summary
One-line description of the marketing insight
Details
Full context: what happened in the campaign or channel, why the expected outcome
differed from actual, what the correct approach or messaging strategy should be.
Include relevant metrics (CTR, CVR, CPL, ROAS) with before/after comparison.
Evidence
Metrics before:
CTR: X.X%
Conversion rate: X.X%
CPL: $XX.XX
Metrics after (or expected):
CTR: X.X%
Conversion rate: X.X%
CPL: $XX.XX
Suggested Action
Specific campaign adjustment, messaging change, targeting update, or process improvement
Metadata
Source: analytics_dashboard | a_b_test | campaign_report | brand_audit | customer_feedback | attribution_tool
Channel: google_ads | meta_ads | linkedin | email | organic_search | social | direct
Segment: enterprise | mid_market | smb | consumer | all
Tags: tag1, tag2
See Also: LRN-20250110-001 (if related to existing entry)
Pattern-Key: messaging_miss.wrong_value_prop | audience_drift.icp_shift (optional)
Recurrence-Count: 1 (optional)
First-Seen: 2025-01-15 (optional)
Last-Seen: 2025-01-15 (optional)

Categories for learnings:

| Category | Use When | |----------|----------| | messaging_miss | Value proposition, copy, or positioning failed to resonate with target segment | | channel_underperformance | Channel metrics (CTR, CPL, ROAS) fall significantly below benchmarks | | audience_drift | Target audience behavior, demographics, or needs have shifted | | brand_inconsistency | Messaging, visual identity, or tone deviates from brand guidelines | | attribution_gap | Tracking breaks, UTM parameters lost, conversion path unclear | | content_decay | Previously high-performing content loses traffic or engagement over time |

Campaign Issue Entry [CMP-YYYYMMDD-XXX]

Append to .learnings/CAMPAIGN_ISSUES.md:

## [CMP-YYYYMMDD-XXX] issue_description
Logged: ISO-8601 timestamp
Priority: high
Status: pending
Area: content | campaigns | seo | social | email | paid_media | analytics
Summary
Brief description of the campaign problem
Performance Data
Campaign: campaign name or ID (redacted if needed)
Channel: google_ads | meta_ads | linkedin | email | organic_search
Date Range: YYYY-MM-DD to YYYY-MM-DD
Budget Spent: $X,XXX
Key Metrics:
  - Impressions: XX,XXX
  - Clicks: X,XXX
  - CTR: X.X% (benchmark: X.X%)
  - Conversions: XXX
  - CVR: X.X% (benchmark: X.X%)
  - CPL: $XX.XX (benchmark: $XX.XX)
  - ROAS: X.Xx (benchmark: X.Xx)
Root Cause
What in the campaign setup, targeting, creative, or messaging caused the issue.
Fix Applied
What changes were made to address the problem.
Prevention
How to avoid this issue in future campaigns (checklist item, review step, automation)
Context
Trigger: performance_alert | manual_review | a_b_test | customer_complaint | deliverability_report
Campaign Type: lead_gen | brand_awareness | retargeting | nurture | product_launch | event_promotion
Audience: target segment description
Metadata
Reproducible: yes | no | unknown
Related Campaigns: campaign-name-or-id
See Also: CMP-20250110-001 (if recurring)

Feature Request Entry [FEAT-YYYYMMDD-XXX]

Append to .learnings/FEATURE_REQUESTS.md:

## [FEAT-YYYYMMDD-XXX] capability_name
Logged: ISO-8601 timestamp
Priority: medium
Status: pending
Area: content | campaigns | seo | social | email | paid_media | analytics
Requested Capability
What marketing tool, automation, or analytics capability is needed
User Context
Why it's needed, what workflow it improves, what manual process it replaces
Complexity Estimate
simple | medium | complex
Suggested Implementation
How this could be built: dashboard widget, automation rule, integration, report template, alert system
Metadata
Frequency: first_time | recurring
Related Features: existing_tool_or_feature

ID Generation

Format: TYPE-YYYYMMDD-XXX

Examples: LRN-20250415-001, CMP-20250415-A3F, FEAT-20250415-002

Resolving Entries

When an issue is fixed, update the entry:

1. Change Status: pending → Status: resolved 2. Add resolution block after Metadata:

### Resolution
Resolved: 2025-01-16T09:00:00Z
Campaign/Initiative: Q1 Product Launch v2
Notes: Updated messaging to lead with ROI data instead of feature list

Other status values:

Detection Triggers

Automatically log when you encounter:

CTR / Click Performance (→ campaign issue or learning):

Conversion Performance (→ campaign issue with performance data):

Email Deliverability (→ campaign issue with deliverability trigger):

Organic / SEO (→ learning with content_decay or campaign issue):

Social / Engagement (→ learning with channel_underperformance):

Brand / Messaging (→ learning with brand_inconsistency or messaging_miss):

Attribution / Tracking (→ learning with attribution_gap):

Priority Guidelines

| Priority | When to Use | Marketing Examples | |----------|-------------|-------------------| | critical | Brand crisis, major campaign failure with significant budget waste, data breach affecting customers | PR crisis from messaging mistake, $50K+ budget spent on broken tracking, sending PII in email blast | | high | Significant CTR/conversion drops, deliverability issues affecting large audience, attribution fully broken | CPL 3x above benchmark for >1 week, bounce rate >10%, all UTMs stripped by new redirect | | medium | Channel underperformance, content decay, gradual audience drift, minor brand inconsistency | Blog post lost 40% traffic after update, LinkedIn ads below median CTR, persona needs refresh | | low | Minor copy improvements, process optimization, small tracking gaps, cosmetic brand issues | Subject line A/B test insight, minor UTM naming inconsistency, social post timing optimization |

Area Tags

Use to filter learnings by marketing domain:

| Area | Scope | |------|-------| | content | Blog posts, landing pages, whitepapers, case studies, video content, webinars | | campaigns | Campaign strategy, multi-channel orchestration, launch planning, promotions | | seo | Organic search, keyword strategy, technical SEO, content optimization, link building | | social | Social media strategy, community management, organic social, influencer partnerships | | email | Email campaigns, nurture sequences, deliverability, list hygiene, segmentation | | paid_media | PPC, display ads, social ads, programmatic, retargeting, budget allocation | | analytics | Attribution, reporting, dashboards, A/B testing, conversion tracking, data quality |

Promoting to Permanent Marketing Standards

When a learning is broadly applicable (not a one-off campaign fix), promote it to permanent marketing standards.

When to Promote

Promotion Targets

| Target | What Belongs There | |--------|-------------------| | Brand guidelines | Messaging tone, positioning statements, visual identity rules, do/don't examples | | Channel playbooks | Per-channel best practices, benchmarks, creative specs, targeting strategies | | Audience personas | ICP definitions, segment profiles, buying triggers, objection handling | | Content calendar | Content type cadence, topic frameworks, seasonal planning, repurposing rules | | Attribution model | UTM taxonomy, tagging standards, measurement methodology, reporting templates | | TOOLS.md | Marketing tool configurations, integration gotchas, API limits | | AGENTS.md | Automated marketing workflows, review checkpoints |

How to Promote

1. Distill the learning into a concise guideline, checklist item, or rule 2. Add to appropriate target (brand doc, playbook, persona profile) 3. Update original entry: - Change Status: pending → Status: promoted - Add Promoted: brand guidelines (or channel playbook, persona doc, content calendar, attribution model)

Promotion Examples

Learning (verbose): > Product launch email sent to enterprise segment with SMB-focused messaging. > Used "easy setup" and "affordable pricing" — enterprise buyers want ROI data, > security compliance, and integration capabilities. Open rate 8% vs. 22% benchmark.

As brand guideline (concise):

## Enterprise Messaging Rules
Lead with ROI metrics and business impact, not ease-of-use
Reference security certifications (SOC2, ISO 27001) in first fold
Include integration ecosystem and API capabilities
Avoid "affordable" or "cheap" — use "cost-effective" with TCO comparison

Learning (verbose): > LinkedIn carousel ads outperform single-image for B2B. 12 campaigns, 3 months: > 2.8x higher CTR, 1.9x lower CPL. Best with 5-7 slides, data-driven, strong hook.

As channel playbook (actionable):

## LinkedIn Ad Creative Guidelines
Default to carousel format for B2B educational content (5-7 slides)
First slide: strong hook (question or bold stat)
Benchmark CTR: 0.8-1.2% (carousel) vs. 0.3-0.5% (single image)

Recurring Pattern Detection

If logging something similar to an existing entry:

1. Search first: grep -r "keyword" .learnings/ 2. Link entries: Add See Also: CMP-20250110-001 in Metadata 3. Bump priority if issue keeps recurring 4. Consider systemic fix: Recurring marketing issues often indicate: - Missing brand guideline (→ add to brand standards) - Outdated persona (→ refresh audience research) - Broken process (→ add to campaign checklist) - Attribution gap (→ fix tracking infrastructure)

Periodic Review

Review .learnings/ at natural breakpoints:

When to Review

Quick Status Check

# Count pending marketing issues
grep -h "Status\*\*: pending" .learnings/*.md | wc -l
List pending high-priority campaign issues
grep -B5 "Priority\*\*: high" .learnings/CAMPAIGN_ISSUES.md | grep "^## \["
Find all messaging misses
grep -B2 "messaging_miss" .learnings/LEARNINGS.md | grep "^## \["

Review Actions

Hook Integration

Enable automatic reminders through agent hooks. This is opt-in.

Quick Setup (Claude Code / Codex)

Create .claude/settings.json in your project:

{
  "hooks": {
    "UserPromptSubmit": [{
      "matcher": "",
      "hooks": [{
        "type": "command",
        "command": "./skills/self-improving-marketing/scripts/activator.sh"
      }]
    }]
  }
}

This injects a marketing-focused learning evaluation reminder after each prompt (~50-100 tokens overhead).

Advanced Setup (With Error Detection)

{
  "hooks": {
    "UserPromptSubmit": [{
      "matcher": "",
      "hooks": [{
        "type": "command",
        "command": "./skills/self-improving-marketing/scripts/activator.sh"
      }]
    }],
    "PostToolUse": [{
      "matcher": "Bash",
      "hooks": [{
        "type": "command",
        "command": "./skills/self-improving-marketing/scripts/error-detector.sh"
      }]
    }]
  }
}

Enable PostToolUse only if you want the hook to inspect command output for campaign performance issues, deliverability problems, and tracking errors.

Available Hook Scripts

| Script | Hook Type | Purpose | |--------|-----------|---------| | scripts/activator.sh | UserPromptSubmit | Reminds to evaluate marketing learnings after tasks | | scripts/error-detector.sh | PostToolUse (Bash) | Triggers on campaign issues, deliverability errors, tracking problems |

See references/hooks-setup.md for detailed configuration and troubleshooting.

Automatic Skill Extraction

When a marketing learning is valuable enough to become a reusable skill, extract it.

Skill Extraction Criteria

| Criterion | Description | |-----------|-------------| | Recurring | Same campaign pattern in 2+ channels or quarters | | Verified | Status is resolved with proven fix and measured improvement | | Non-obvious | Required actual analysis, testing, or investigation | | Broadly applicable | Not campaign-specific; useful across segments or channels | | User-flagged | User says "save this as a skill" or similar |

Extraction Workflow

1. Identify candidate: Learning meets extraction criteria 2. Run helper (or create manually):

   ./skills/self-improving-marketing/scripts/extract-skill.sh skill-name --dry-run
   ./skills/self-improving-marketing/scripts/extract-skill.sh skill-name
   

Extraction Detection Triggers

Use conversation signals ("This campaign pattern keeps working", "Save this as a playbook") to identify extraction candidates.

Multi-Agent Support

| Agent | Activation | Detection | |-------|-----------|-----------| | Claude Code | Hooks (UserPromptSubmit, PostToolUse) | Automatic via error-detector.sh | | Codex CLI | Hooks (same pattern) | Automatic via hook scripts | | GitHub Copilot | Manual (.github/copilot-instructions.md) | Manual review | | OpenClaw | Workspace injection + inter-agent messaging | Via session tools |

Best Practices

1. Log immediately — campaign context and metrics fade fast after the moment passes 2. Include before/after metrics — quantify the impact with CTR, CVR, CPL, ROAS comparisons 3. Specify the channel — patterns differ between Google Ads, LinkedIn, email, organic 4. A/B test before scaling — never scale a campaign change based on gut feel alone 5. Document creative rationale — why a headline, image, or CTA was chosen for the segment 6. Track attribution end-to-end — verify UTMs survive redirects, link shorteners, and cross-domain hops 7. Review personas quarterly — audience needs and behaviors shift; validate with data 8. Audit brand consistency monthly — check all active assets against current brand guidelines 9. Promote aggressively — if a messaging pattern works across 3+ campaigns, codify it

Gitignore Options

Keep learnings local (per-team):

.learnings/

Track learnings in repo (org-wide): Don't add to .gitignore — learnings become shared marketing knowledge.

Hybrid (track templates, ignore entries): add .learnings/*.md and !.learnings/.gitkeep to .gitignore.

Stackability Contract (Standalone + Multi-Skill)

This skill is standalone-compatible and stackable with other self-improving skills.

Namespaced Logging (recommended for 2+ skills)

Required Metadata

Skill: marketing

Hook Arbitration (when 2+ skills are enabled)

Narrow Matcher Scope (marketing)

Cross-Skill Precedence

Ownership Rules

1. Log immediately — campaign context and metrics fade fast after the moment passes 2. Include before/after metrics — quantify the impact with CTR, CVR, CPL, ROAS comparisons 3. Specify the channel — patterns differ between Google Ads, LinkedIn, email, organic 4. A/B test before scaling — never scale a campaign change based on gut feel alone 5. Document creative rationale — why a headline, image, or CTA was chosen for the segment 6. Track attribution end-to-end — verify UTMs survive redirects, link shorteners, and cross-domain hops 7. Review personas quarterly — audience needs and behaviors shift; validate with data 8. Audit brand consistency monthly — check all active assets against current brand guidelines 9. Promote aggressively — if a messaging pattern works across 3+ campaigns, codify it