AI-Powered Google Ads Campaign Builder PRD and Technical Design

Source:https://chatgpt.com/share/6824d9bd-d228-8004-b1ab-bc6943e2b99b

Product Requirements Document (PRD)

1. Overview and Objectives

1.1. Product Overview

The product is an AI-powered SaaS platform designed to automate the creation and management of Google Ads marketing campaigns. By leveraging advanced large language models (LLMs) and autonomous agentic behavior, the system simplifies campaign creation: a user needs only to input a URL, and the platform automatically handles content analysis, market research, ad copy generation, and campaign automation via Google Ads API integration.

1.2. Objectives

  • Simplify Campaign Management: Reduce the complexity and manual labor associated with creating and managing Google Ads campaigns.
  • Enhance Marketing Efficiency: Leverage AI to generate optimized ad copy and campaign strategies based on deep content analysis and research.
  • Automate End-to-End Processes: Enable automatic campaign launch and ongoing management with minimal human intervention.
  • Drive Better ROI: Improve ad performance by employing data-driven insights and continuous optimization.
  • User-Friendly Experience: Ensure a straightforward, streamlined interface accessible to both technical and non-technical users.

2. User Experience and Workflow

2.1. User Journey

  • Login/Onboarding: Users register or log into the platform and undergo a brief onboarding process to understand key features.

  • URL Input: Users input the URL of the product or service they wish to advertise.

  • Content Analysis: The system scans the provided URL to extract and understand relevant information (e.g., product features, benefits, target audience).

  • Research Phase:

  • The platform conducts market and competitor research.

  • Identifies best-performing keywords and target demographics.

  • Ad Creation:

  • AI generates ad elements including headlines, descriptions, and call-to-actions.

  • Provides a preview for user review and optional editing.

  • Campaign Automation:

  • The system integrates with the Google Ads API.

  • Automatically sets up and launches the campaign with defined budgets, bidding strategies, and schedules.

  • Monitoring & Optimization:

  • Continuous performance tracking with automated suggestions for optimization.

  • Provides a dashboard for users to monitor campaign performance and analytics.

2.2. User Interface Considerations

  • Dashboard: Central hub for managing campaigns, monitoring performance metrics, and accessing historical data.
  • Step-by-Step Wizard: Guides users through the campaign setup process.
  • Real-Time Previews: Visual representation of ad elements and expected placements.
  • Customization Options: Allow users to modify auto-generated content if desired.

3. System Architecture

3.1. High-Level Components

  • Frontend:

  • Web-based UI built with a modern JavaScript framework (e.g., React or Vue.js).

  • Mobile-responsive design.

  • Backend:

  • RESTful API services built with a scalable framework (e.g., Node.js, Python/Django, or Ruby on Rails).

  • Microservices architecture to separate content analysis, research, ad generation, and campaign management tasks.

  • LLM Engine:

  • Integration with cloud-based LLM services for natural language processing and content generation.

  • Database:

  • SQL/NoSQL databases for storing user data, campaign data, analytics, and logs.

  • External Integrations:

  • Google Ads API for campaign management.

  • Third-party marketing research APIs.

  • Automation & Scheduler:

  • Job scheduling system (e.g., Celery, AWS Lambda) for background tasks such as continuous campaign optimization.

3.2. Data Flow Diagram

  • User Input (URL) → Frontend
  • Frontend → Backend API → Content Analysis Module (LLM)
  • Content Analysis Module → Research Module
  • Research Module → Ad Generation Module (LLM)
  • Ad Generation Module → Campaign Preparation Module
  • Campaign Preparation Module → Google Ads API Integration
  • Campaign Data & Analytics → Dashboard (Frontend)

4. Key Features and Functional Requirements

4.1. Content Analysis

  • URL Parsing: Automatically crawl the provided URL to extract relevant product/service information.
  • Content Extraction: Use NLP techniques to understand key attributes, value propositions, and target audience signals.
  • Metadata Identification: Extract meta tags, structured data, and other SEO signals.

4.2. Research

  • Market Analysis: Gather data on industry trends, competitor campaigns, and consumer behavior.
  • Keyword Research: Identify high-performing keywords and negative keywords.
  • Audience Segmentation: Define and segment target audiences based on research data.

4.3. Ad Creation

  • Ad Copy Generation: Use LLMs to create persuasive headlines, descriptions, and call-to-action elements.
  • A/B Testing Suggestions: Generate multiple versions of ad copy for A/B testing.
  • Customization: Allow manual editing of generated content.

4.4. Automation

  • Campaign Setup: Automatically configure campaign settings (budget, bid strategy, schedule).
  • Google Ads Integration: Seamless API integration for campaign launch and management.
  • Performance Monitoring: Real-time tracking of key metrics (CTR, CPC, conversion rates) and automated optimization recommendations.

4.5. Agentic Behavior

  • Autonomous Operation: Utilize LLMs to operate with minimal human intervention, from content analysis to campaign launch.
  • Self-Optimization: Continuously refine campaigns based on performance data.
  • Adaptive Learning: Incorporate user feedback and campaign outcomes to improve future recommendations.

5. Integration and API Details

5.1. Google Ads API Integration

  • Authentication: Secure OAuth 2.0 for user and service authentication.
  • Campaign Management: APIs to create, update, and delete campaigns.
  • Data Reporting: Fetch performance metrics and analytics.
  • Error Handling: Robust error handling and retry mechanisms for API calls.

5.2. Third-Party APIs

  • Market Research APIs: Integrate with external platforms (e.g., SEMrush, Ahrefs) for keyword and competitor data.
  • LLM Providers: Connect with cloud-based LLM services (e.g., OpenAI, Anthropic) via API for content generation.
  • Scheduler and Automation Services: Use serverless functions or task queues for asynchronous processing.

6. LLM Usage and Agentic Functionality

6.1. LLM Usage

  • Content Understanding: Employ LLMs to parse and understand website content, extracting valuable insights about the product/service.
  • Ad Copy Generation: Utilize LLMs to craft compelling ad headlines, descriptions, and CTAs.
  • Data Interpretation: Process market research data to inform keyword and audience segmentation decisions.

6.2. Agentic Functionality

  • Autonomous Decision-Making: Enable the system to autonomously choose keywords, budget allocations, and campaign strategies based on AI insights.
  • Self-Optimizing Algorithms: Implement feedback loops where campaign performance data feeds back into the LLM to improve future ad generation.
  • Adaptive Workflows: Adjust workflow steps based on user behavior and real-time campaign data without human intervention.

7. Performance and Scalability Considerations

7.1. Performance Requirements

  • Real-Time Processing: Ensure that URL content analysis and ad generation occur within a few seconds to maintain a smooth user experience.
  • API Response Times: Maintain sub-second response times for internal and external API calls.
  • Scalable Infrastructure: Use cloud-native services to dynamically scale resources during peak loads (e.g., auto-scaling groups, container orchestration).

7.2. Scalability Strategies

  • Microservices Architecture: Isolate modules to allow independent scaling based on demand.
  • Caching Mechanisms: Use caching (e.g., Redis, Memcached) to reduce redundant processing for frequently analyzed content.
  • Load Balancing: Implement load balancing across servers to distribute traffic evenly.
  • Asynchronous Processing: Offload heavy tasks to background workers to prevent UI blocking.

8. Security and Compliance

8.1. Security Considerations

  • Data Encryption: Encrypt data at rest and in transit using industry-standard protocols (e.g., TLS, AES-256).
  • Authentication & Authorization: Implement robust user authentication (e.g., OAuth, SSO) and role-based access controls.
  • API Security: Secure API endpoints with rate limiting, input validation, and monitoring to prevent abuse.
  • Vulnerability Management: Regular security audits, penetration testing, and adherence to secure coding practices.

8.2. Compliance

  • GDPR and CCPA: Ensure compliance with data protection regulations, including user data consent and data erasure requests.
  • Industry Standards: Follow relevant standards such as ISO/IEC 27001 for information security management.
  • Audit Trails: Maintain logs for all significant actions (e.g., campaign launches, API calls) for auditability and forensic analysis.

Conclusion

This PRD outlines a comprehensive framework for developing an AI-powered SaaS platform that automates Google Ads campaign management. By blending advanced LLM capabilities with robust automation and seamless integration with Google Ads, the product aims to provide both technical and non-technical stakeholders with a tool that is efficient, secure, and capable of delivering high ROI on advertising spend. This document serves as a roadmap for both development and operational teams, ensuring alignment on vision, features, and performance benchmarks.