AI Content Marketing Audit

AI-powered content marketing analysis evaluating seven critical factors that drive engagement and business results.

Features

• Seven Marketing Factors: Usability, Knowledge Level, Actionability, Accuracy, Business Value, Messaging, Brand Voice Fit
• Dual Analysis: Quantitative scoring (-1.0 to +1.0) and qualitative classification
• Configurable Factors: Add, edit, delete custom audit factors
• Batch Processing: Analyze large content volumes efficiently
• Views Integration: Custom reports and dashboards
• Smart Caching: Performance optimization with content/config-based invalidation

Requirements

• Analyze framework
• AI module with configured provider

Installation

composer require drupal/analyze_ai_content_marketing_audit
drush en analyze_ai_content_marketing_audit

Configuration

Basic Setup

1. Configure AI provider at /admin/config/analyze/ai
2. Manage factors at /admin/config/analyze/content-marketing-audit
3. Enable per content type at /admin/config/system/analyze-settings
4. Set permissions at /admin/people/permissions#module-analyze_ai_content_marketing_audit

Factor Management

• Add factors: Click "Add factor" with ID, label, description, weight
• Edit factors: Modify existing factors (ID cannot be changed)
• Delete factors: Removes factor and all associated results

AI Coding Assistant Integration

The Content Marketing Audit module includes a built-in Agent Skills file (via the base Analyze module) that teaches AI coding assistants how to run marketing audit analysis through natural language. Run drush analyze:setup-ai to enable, then ask naturally:

"Audit content marketing quality on all articles"
"Check the usability and actionability scores for landing pages"
"Run a marketing audit on all published content"
"Analyze business value across the blog section"

Batch processing is available via the centralized Analyze batch system:

# Check analysis coverage
drush analyze:batch --status

# Run this analyzer on all enabled content types
drush analyze:batch \
  --analyzers=analyze_ai_content_marketing_audit_analyzer

# Run on specific content types with limit
drush analyze:batch \
  --analyzers=analyze_ai_content_marketing_audit_analyzer \
  --types=node:article --limit=50

# Force re-analysis of already analyzed content
drush analyze:batch \
  --analyzers=analyze_ai_content_marketing_audit_analyzer --force

Compatible with Claude Code, Codex CLI, Gemini CLI, GitHub Copilot, Cursor, and other tools supporting the Agent Skills standard.

Analysis

Scoring System

• +1.0: Excellent performance
• 0.7 to 0.9: Good performance
• 0.3 to 0.6: Average performance
• -0.3 to 0.2: Needs improvement
• -1.0 to -0.4: Poor performance

Default Factors

1. Usability: Content clarity and actionability
2. Knowledge Level: Expertise demonstration
3. Actionability: Clear next steps provided
4. Accuracy: Factual correctness and currency
5. Business Value: Support for business goals
6. Messaging: Clarity and consistency
7. Brand Voice Fit: Alignment with brand guidelines

Views Integration

Default Reports

Access at /admin/reports/content-marketing-audit showing content title, factor type, score, and analysis date.

Custom Views

• Base table: "Content Marketing Audit Results"
• Filter by factor type, score range, content title, analysis date
• Install Views Color Scales for color-coded results

API Usage

// Get storage service
$storage = \Drupal::service('analyze_ai_content_marketing_audit.storage');

// Get/save scores
$score = $storage->getScore($entity, 'usability');
$storage->saveScore($entity, 'accuracy', 0.85);

// Analyze content
$analyzer = \Drupal::service('plugin.manager.analyze')
  ->createInstance('content_marketing_audit_analyzer');
$score = $analyzer->analyze($entity, 'messaging');

Troubleshooting

Analysis Issues

• Verify AI provider configuration
• Check factor status and descriptions
• Enable analysis for content types

Batch Processing

• Reduce batch size for timeouts (10-25 entities)
• Process during off-peak hours for API limits
• Monitor server memory and execution time

Performance

• Cache invalidation is automatic
• Only re-analyzes when content/config changes
• Use reasonable batch sizes (50-100 entities)