Video Ad Deconstructor

AI-powered deconstruction of video ad creatives into actionable marketing insights.

What This Skill Does

• Generate Summaries: Product, features, audience, CTA extraction
• Deconstruct Marketing Dimensions: Hooks, social proof, urgency, emotion, etc.
• Support Multiple Content Types: Consumer products and gaming ads
• Progress Tracking: Callback support for long analyses
• JSON Output: Structured data for downstream processing

Setup

1. Environment Variables

# Required for Gemini
GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account.json

2. Dependencies

pip install vertexai

Usage

Basic Ad Deconstruction

from scripts.deconstructor import AdDeconstructor
from scripts.models import ExtractedVideoContent
import vertexai
from vertexai.generative_models import GenerativeModel

# Initialize Vertex AI
vertexai.init(project="your-project-id", location="us-central1")
gemini_model = GenerativeModel("gemini-1.5-flash")

# Create deconstructor
deconstructor = AdDeconstructor(gemini_model=gemini_model)

# Create extracted content (from video-ad-analyzer or manually)
content = ExtractedVideoContent(
    video_path="ad.mp4",
    duration=30.0,
    transcript="Tired of messy cables? Meet CableFlow...",
    text_timeline=[{"at": 0.0, "text": ["50% OFF TODAY"]}],
    scene_timeline=[{"timestamp": 0.0, "description": "Person frustrated with tangled cables"}]
)

# Generate summary
summary = deconstructor.generate_summary(
    transcript=content.transcript,
    scenes="0.0s: Person frustrated with tangled cables",
    text_overlays="50% OFF TODAY"
)
print(summary)

Full Deconstruction

# Deconstruct all marketing dimensions
def on_progress(fraction, dimension):
    print(f"Progress: {fraction*100:.0f}% - Analyzed {dimension}")

analysis = deconstructor.deconstruct(
    extracted_content=content,
    summary=summary,
    is_gaming=False,  # Set True for gaming ads
    on_progress=on_progress
)

# Access dimensions
for dimension, data in analysis.dimensions.items():
    print(f"\n{dimension}:")
    print(data)

Output Structure

Summary Output

Product/App: CableFlow Cable Organizer

Key Features:
Magnetic design: Keeps cables organized automatically
Universal fit: Works with all cable types
Premium materials: Durable silicone construction

Target Audience: Tech users frustrated with cable management

Call to Action: Order now and get 50% off

Deconstruction Output

{
    "spoken_hooks": {
        "elements": [
            {
                "hook_text": "Tired of messy cables?",
                "timestamp": "0:00",
                "hook_type": "Problem Question",
                "effectiveness": "High - directly addresses pain point"
            }
        ]
    },
    "social_proof": {
        "elements": [
            {
                "proof_type": "User Count",
                "claim": "Over 1 million happy customers",
                "credibility_score": 7
            }
        ]
    },
    # ... more dimensions
}

Marketing Dimensions Deconstructed

Dimension	What It Extracts
spoken_hooks	Opening hooks from transcript
visual_hooks	Attention-grabbing visuals
text_hooks	On-screen text hooks
social_proof	Testimonials, user counts, reviews
urgency_scarcity	Limited time offers, stock warnings
emotional_triggers	Fear, desire, belonging, etc.
problem_solution	Pain points and solutions
cta_analysis	Call-to-action effectiveness
target_audience	Who the ad targets
unique_mechanism	What makes product special

Customizing Prompts

Edit prompts in prompts/marketing_analysis.md to customize:

• What dimensions to analyze
• Output format
• Scoring criteria
• Gaming vs consumer product focus

Common Questions This Answers

• "What hooks does this ad use?"
• "What's the emotional appeal?"
• "How does this ad create urgency?"
• "Who is this ad targeting?"
• "What social proof is shown?"
• "Deconstruct this competitor's ad"