Generate Multimodal Marketing Campaign with Vertex AI

Create a comprehensive marketing campaign with all assets generated via Google Vertex AI multimodal capabilities.

What This Does

1. Campaign Brief Analysis: Understand product, target audience, goals
2. Asset Generation: Create all required media assets
3. Multi-Channel Content: Generate content for all marketing channels
4. Implementation Guide: Provide deployment instructions

Campaign Assets Generated

Visual Assets (Imagen 4)

• Hero image (1920x1080)
• Social media graphics (Instagram, Facebook, LinkedIn)
• Display ad creatives (multiple sizes)
• Product lifestyle images
• A/B test variations

Video Assets (Gemini 2.5 Pro)

• Video scripts (30s, 60s, 2min versions)
• Storyboard descriptions
• Video editing instructions
• Thumbnail designs

Audio Assets (Lyria)

• Background music compositions
• Voiceover scripts
• Audio ad scripts
• Podcast episode outlines

Written Content (Gemini 2.5 Pro)

• Email marketing sequences
• Blog post (SEO optimized)
• Social media captions
• PPC ad copy
• Landing page copy

Usage

/vertex-campaign

Then provide campaign details:

• Product/service name
• Target audience
• Campaign objectives
• Brand guidelines
• Budget considerations

Example Workflow

Input:

Product: Premium noise-canceling headphones
Audience: Remote workers, 25-45, tech-savvy
Goal: Product launch, 10K units in Q1
Budget: $50K

Output:

1. 15 product images (lifestyle, studio, use-cases)
2. 30s product launch video script
3. Background music track (energetic, professional)
4. Email sequence (5 emails)
5. Social media content (30 posts across platforms)
6. Blog post "Best Headphones for Remote Work 2025"
7. PPC campaigns (Google, Facebook, LinkedIn)

Technical Implementation

Step 1: Initialize Vertex AI

from google.cloud import aiplatform
from vertexai.preview.generative_models import GenerativeModel
from vertexai.preview.vision_models import ImageGenerationModel

aiplatform.init(project=PROJECT_ID, location="us-central1")

Step 2: Generate Visual Assets

imagen = ImageGenerationModel.from_pretrained("imagen-4")
hero_image = imagen.generate_images(
    prompt=f"Professional product photography of {product}, studio lighting, clean background",
    number_of_images=1,
    aspect_ratio="16:9"
)

Step 3: Create Video Script

gemini = GenerativeModel("gemini-2.5-pro")
video_script = gemini.generate_content([
    f"Create a 30-second video script for {product} targeting {audience}. Include scene descriptions, voiceover, music cues."
])

Step 4: Generate Audio

from vertexai.preview.audio_models import AudioGenerationModel
lyria = AudioGenerationModel.from_pretrained("lyria")
background_music = lyria.generate_audio(
    prompt=f"Background music for {product} video ad, {mood}, 30 seconds",
    duration=30
)

Step 5: Create Multi-Channel Copy

content = gemini.generate_content([
    f"""Generate marketing content for {product}:
    - 5-email drip campaign
    - 10 Instagram captions
    - 5 LinkedIn posts
    - SEO blog post (1500 words)
    - Google Ads copy (5 variations)"""
])

Cost Estimation

Per Campaign:

• Visual Assets: $2-3 (50 images @ $0.04 each)
• Video Scripts: $0.50 (Gemini tokens)
• Audio: $1-2 (Lyria generation)
• Written Content: $1 (Gemini tokens)

Total: ~$5-7 per complete campaign

Best Practices

1. Brand Consistency: Provide brand guidelines in prompt
2. Batch Generation: Generate multiple variations simultaneously
3. Quality Control: Review and iterate on generated assets
4. Version Control: Save prompts and outputs for reproducibility
5. A/B Testing: Generate 3-5 variations of each asset

Integration with Marketing Stack

Export to:

• Google Ads (PMax campaigns)
• Meta Business Suite (Facebook/Instagram)
• LinkedIn Campaign Manager
• Email marketing platforms (HubSpot, Mailchimp)
• CMS platforms (WordPress, Contentful)

Performance Tracking

Monitor:

• Asset generation time
• Cost per asset
• Approval rates
• Campaign performance metrics
• ROI vs traditional production

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This command turns Jeremy into a one-person marketing agency powered by Vertex AI multimodal capabilities.