What you will learn / 學習目標
多通道廣告自動優化
Leverage predictive algorithms to allocate advertising budgets dynamically across Meta, Google, and TikTok.
預測性競價策略
Deploy machine learning models for real-time bid adjustments and maximum ROI.
受眾自動分群與擴展
Create hyper-targeted lookalike audiences using advanced predictive analytics.
ROI 歸因與歸因建模
Master multi-touch attribution (MTA) frameworks to track qualitative touchpoints.
Target Audience / 課程受眾
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Media Buyers / 廣告投手
期望利用 AI 演算法突破人工競價極限、降低獲客成本的投手。
🔬
Data Analysts / 分析師
需建立多歸因數據模型、精準評估廣告跨平台成效的分析人員。
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Marketing Directors / 總監
面臨預算分配決策壓力，需制定科學化 ROI 提升策略的主管。
Professional Skills / 核心技能
Predictive Bidding
Multi-touch Attribution
Media Mix Modeling
Audience Expansion
Programmatic Buying
Curriculum / 課程大綱
01
AI 廣告生態與實時競價機制演進 / Programmatic Ecosystem
Deconstructing programmatic auctions and the shift towards algorithmic bidding.
02
跨渠道預算智能調配與動態歸因 / Budget & Attribution
Using machine learning models to dynamically balance multi-channel spending.
03
基於 AI 的第一方數據激活與受眾擴展 / Audience Activation
Strategies to safely activate CRM data and generate predictive lookalikes.
04
ROI 終極優化：指標度量與MMM建模 / MMM Modeling
Applying Media Mix Modeling to forecast long-term impact and business growth.
Recommended Path / 延伸學習路徑
Next Step
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MKT-AI-22
Marketing Resource Allocation via AI
深入學習跨部門、全行銷渠道預算動態分配與財務決策。
Deep Dive
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MKT-AI-09
Hyper-Personalized Marketing Journeys
技術進階：結合精準畫像，為不同流失風險客群制定專屬營銷旅程。
Quality Boost
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MKT-AI-01
Content Creation at Scale: AI Strategies
提升素材產出效率，利用 AI 大規模衍生多樣化素材進行 A/B 測試。