AI and First-Party Data Activation

Overview

This course focuses on using AI and first-party data to optimise advertising and content performance. Marketers today must integrate AI-first, profit-centred strategies across major ad platforms and their own sites. Each leading platform now offers first-party data capabilities designed to maximise revenue:

• Google Ads with GA4, Enhanced Conversions, Consent Mode and Google Tag Gateway to refine AI-driven ad models
• Meta Advantage+ Shopping, which blends prospecting and retention audiences using Conversions API signals for higher-value purchases
• LinkedIn Insight Tag and Conversions API, which now supports first-party cookies and 'optimise for value' bidding
• Microsoft Advertising with UET and Enhanced Conversions, allowing for hashed customer data in tROAS and Max-Value strategies
• On-site personalisation engines that use data segments to dynamically adjust headlines, CTAs and offers for higher conversion rates
• TikTok Smart Performance Campaigns, which automate creative, bidding and value-based optimisation with minimal inputs

This course shows how to connect these systems into a unified, profit-first first-party data strategy that supports sustainable growth.

Who should attend?

This course is designed for mid to senior marketers, digital strategists, performance media leads and marketing analysts who want to develop skills in AI-driven, data-led, profit-centric advertising.

Learning outcomes

• Align profit targets with cross-platform KPIs and governance
• Assess data and privacy readiness across major ad platforms and on-site personalisation tools
• Set up a consented first-party data stack that streams events across Google, Meta, TikTok, LinkedIn and Microsoft
• Choose and apply profit-optimised bid strategies on each platform, including target ROAS, value-based and predictive lifetime value models
• Customise landing pages with AI-driven dynamic blocks matched to audience and channel intent
• Measure incremental profit per channel and refine channel mix using MMM, causal impact tests and platform lift studies
• Balance margin and scale using AI planning tools, including automated forecasting, predictive analytics and scenario modelling