Paid Media Automation Pipeline for AI Products
AI 产品付费投放自动化方案
A comprehensive 4-layer pipeline for automating paid media across Google, X, and Reddit — purpose-built for AI products with subscription and API token models.
不只是自动化投放和报表，而是建立从广告效果到内容策略的完整反馈闭环，让数据驱动每一条素材的生成。
3 Ad Platforms
4 Layers
GKE Native
MCP Architecture
Contents
Current State / 现有能力
| Capability | Status | Notes |
|---|---|---|
| Social media automation (X, Reddit) | Production | Multi-persona, GKE deployed |
| AI content generation (Gemini) | Production | Trend-aware, multi-persona |
| TikTok / Meta / Google ad reporting | Live | MCP servers + dashboards |
| UGC management (Notion) | Running | Form-based creator delegation |
| Landing page A/B testing | Live | 8+ design variants |
| K8s / GKE deployment | Mature | Multi-mode containers, PVCs, Secrets |
Layer 1 — Ad Platform Unified API
广告平台 API 统一层：把三大平台封装成统一接口
Layer 1 · Foundation
One MCP Server per Platform
Reuse the architecture from your existing TikTok Ads MCP. Each server exposes unified AdCampaign, AdCreative, and AdPerformance schemas.
| Platform | API Focus | Key Capability |
|---|---|---|
| Google Ads | Campaign CRUD, YouTube video ads, Performance Max, bidding | YouTube In-stream / In-feed — highest ROI for AI product video ads |
| X / Twitter Ads | Campaign management, audience targeting, promoted tweets | Conversation ads — great for AI product demos and thought leadership |
| Reddit Ads | Campaign management, subreddit targeting, promoted posts | Community targeting is surgical — r/ChatGPT, r/LocalLLaMA, etc. |
Layer 2 — Performance Feedback Loop
投放效果反馈闭环：从数据到内容策略的自动化链路
Layer 2 · Intelligence
Data In, Better Content Out
The core loop: ad performance data feeds back into content strategy, which generates better creatives, which perform better.
Ad Performance Data
→
Performance Analyzer
→
Content Angle Extractor
→
Brief Generator
→
New Creatives
→
Re-deploy
2.1 Performance Analyzer / 效果分析器
- Cross-platform metric normalization: CPA, ROAS, CTR, VTR (Video Through Rate), Hook Rate (3-second view rate)
- AI subscription-specific metrics: Trial-to-Paid conversion, API token first-purchase rate
- Aggregate by content angle, not just creative ID — the angle is the insight
2.2 Content Angle Extractor / 内容角度提取器
- Use LLM (Gemini / Claude) to analyze top-performing creatives for common patterns
- Output: which pain points, use cases, and hooks drive the best results
Example insight: "Use Claude to code and save 3 hours" (productivity angle) has 40% lower CPA than "Look what AI can do" (capability showcase angle) — for the same product, same audience.
2.3 Brief Generator / 创意 Brief 自动生成
- Auto-generate creative briefs based on performance insights
- Output format compatible with Notion UGC workflow
- Includes: recommended angle, reference materials, target audience, platform-specific tips
Layer 3 — UGC Pipeline Enhancement
UGC 工作流增强：把现有 Notion 流程和广告系统打通
Layer 3 · Content Supply Chain
From Insight to Ad, Fully Automated
Connect your existing Notion-based UGC workflow to the ad platforms, creating a closed loop from performance insight to live campaign.
Performance Insights
→
Auto Brief
→
Notion Task
→
Creator Delivers
→
Auto Ingest
→
Multi-format Adapt
→
Auto Campaign
→
A/B Test
Key New Capabilities
- Notion API bidirectional integration — not just dispatching tasks, but auto-pulling deliverables when creators submit
- Auto format adaptation — YouTube vertical/horizontal, X video/carousel, Reddit native post formats
- Auto campaign creation — new creative lands → auto-create campaign with preset audience & budget on the right platform
Layer 4 — Smart Campaign Orchestrator
智能投放决策层：跨平台预算分配和竞品监控
Layer 4 · Orchestration
Cross-Platform Intelligence
The brain layer that decides where money goes, who to target, and what competitors are doing.
4.1 Budget Allocator / 预算分配器
- Auto-allocate budget across Google / X / Reddit based on real-time ROAS
- AI product channel characteristics:
- Reddit — highest intent (users actively searching for AI tools)
- YouTube — largest reach, best for video demos
- X — thought leadership, developer community
4.2 Audience Sync / 受众同步
- Cross-platform lookalike audience management
- Your
gtm_target_accounts+ organic engagement data → build seed audiences → upload to each ad platform
4.3 Competitor Ad Monitor / 竞品广告监测
- Track ad creatives and landing pages from ChatGPT, Kimi, Minimax, Lovable, etc.
- Reuse the
openclaw-radarcollector pattern - Data sources: Meta Ad Library, Google Ads Transparency Center, Reddit ad visibility
GKE & Kubernetes Architecture
复用现有 clawforce-marketing GKE 集群，天然适合扩展
Deployment + Service
MCP Servers
Google / X / Reddit Ads MCP, always-on for Claude Code & pipelines
CronJob
Performance Analyzer
Hourly / daily ad data pull & analysis
Job
Brief Generator
On-demand, triggered after analysis completes
CronJob
Budget Allocator
Daily pre-dawn run, adjusts next-day budgets
CronJob
Competitor Monitor
Daily crawl of competitor ad libraries
Deployment + Ingress
Dashboard (FastAPI)
Reuse existing dashboard architecture
Why GKE fits this project perfectly: multi-service orchestration (5+ services), elastic scaling during launch campaigns, unified secret management (K8s Secrets + GCP Secret Manager), Cloud Monitoring integration, and cost efficiency with CronJobs (pay only when running).
Priority & Roadmap
| Priority | What to Build | Expected Value |
|---|---|---|
| P0 | Google Ads MCP + X Ads MCP + Reddit Ads MCP | Unified 3-platform ad operations |
| P0 | Performance Analyzer + cross-platform normalization | Full visibility into campaign performance |
| P1 | Content Angle Extractor + Brief Generator | Close the feedback loop — data drives content |
| P1 | Notion API bidirectional integration | Fully automated UGC workflow |
| P2 | Budget Allocator | Smart cross-platform budget allocation |
| P2 | Competitor Ad Monitor | Competitive intelligence |
| P3 | Auto campaign creation + A/B framework | Full end-to-end automation |
Tech Stack
| Component | Choice | Rationale |
|---|---|---|
| Language | Python 3.12 | Consistent with gtm-engine |
| AI / LLM | Gemini + Claude | Gemini for content gen, Claude for analysis & reasoning |
| Database | PostgreSQL (existing) | New tables: ad_campaigns, ad_creatives, ad_performance, content_angles |
| API Layer | MCP Servers | One per ad platform, consistent with TikTok Ads MCP |
| Deployment | GKE (existing cluster) | Add Deployments / CronJobs alongside current workloads |
| Dashboard | FastAPI + Uvicorn | Extend existing dashboard |
| UGC Integration | Notion API | Bidirectional — dispatch briefs, pull deliverables |
Starting point: Begin with the Google Ads MCP Server (P0) — YouTube is the primary video channel, and you already have Google Ads reporting experience to build on. 建议从 Google Ads MCP 开始，YouTube 是核心视频广告渠道，且已有 Google 广告报表经验可复用。