AI-Powered Marketing Personalisation and Campaign Analytics Training Course

Programme Summary

AI-powered marketing personalisation and campaign analytics is the subject of this five-day training course for marketing and customer analytics leaders at advanced level, culminating in an AI marketing plan for one customer segment or brand. Customers expect relevant offers, while marketing budgets face harder questions about what truly drives revenue. The approach moves from customer data and predictive segmentation, through personalisation and generative content, to measurement, privacy and governance. Mars Training Centre UK delivers this programme in AI-powered marketing personalisation.

The Business Case

Most marketing teams now run several AI tools, yet few can show which ones change customer behaviour or how much revenue each campaign truly adds. Leaders who connect predictive models, personalised journeys and rigorous measurement can move spend towards the customers and channels that respond, while keeping brand voice and customer consent under control.

• Customer Lifetime Value: Rises as offers and journeys are matched to predicted needs and value.
• Marketing Efficiency: Improves as incrementality tests reveal which spend changes outcomes.
• Brand and Privacy Risk: Falls as generative content and data use follow agreed controls.

Principles and Evidence Base

• Customer Lifetime Value Modelling: Value-based segmentation frames targeting choices in Session 2.
• Marketing Mix Modelling: Econometric models of channel contribution structure budget decisions in Session 4.
• Incrementality Testing: Controlled experiments separate causal lift from correlation in Session 4.
• ISO/IEC 42001: Its AI management system requirements shape the governance of marketing AI in Session 5.

Intended Participants

• Leaders accountable for marketing strategy, budget and revenue contribution.
• Heads of digital who manage paid media, content and marketing technology.
• Senior customer analytics leaders who build segmentation and measurement models.
• Executives responsible for customer data, consent and brand standards.

The programme serves marketing, digital, CRM and analytics functions in retail, banking, telecommunications, hospitality and consumer goods.

Learning Outcomes

1. Participants will be able to evaluate customer data readiness for predictive marketing models.
2. Participants will be able to prioritise segments and journeys using predicted customer lifetime value.
3. Participants will be able to design personalisation and next-best-action rules with clear business goals.
4. Participants will be able to set brand and accuracy controls for generative content at scale.
5. Participants will be able to justify budget shifts using marketing mix and incrementality evidence.
6. Participants will be able to construct governance for marketing AI that protects consent and privacy.

Programme Structure

Day 1: Customer Data and AI Marketing Foundations

Is the organisation's customer data ready to support AI-driven marketing decisions?

• First-Party Data Sources and Customer Data Platforms
• Data Readiness and Identity Resolution Assessment
• AI Use Cases Across the Customer Journey
• Marketing Technology Stack Audit
• AI Marketing Maturity Self-Assessment

Day 2: Predictive Segmentation and Customer Value

Which customers matter most, and how are their next needs predicted?

• Customer Lifetime Value Prediction Models
• Propensity and Churn Scoring
• Behavioural Clustering and Micro-Segments
• Segment Prioritisation by Value and Reach
• Bias and Fairness Checks in Targeting Models

Day 3: AI-Powered Marketing Personalisation and Content

How is each customer shown the right message without losing brand control?

• Next-Best-Action and Recommendation Engines
• Journey Orchestration and Real-Time Triggers
• Generative AI for Content Variants at Scale
• Brand Voice Guidelines and Content Approval Workflows
• A/B and Multivariate Testing of Personalised Content

Day 4: Campaign Analytics and Measurement

Which campaigns and channels truly create additional revenue?

• AI Bidding and Audience Optimisation in Paid Media
• Multi-Touch Attribution and Its Limits
• Marketing Mix Modelling for Budget Allocation
• Incrementality Testing With Holdout Groups
• Executive Marketing Performance Dashboards

Day 5: Governing and Applying Marketing AI

Is the AI marketing plan responsible, measurable and ready for approval?

• ISO/IEC 42001 Controls for Marketing AI
• Consent, Privacy and Customer Data Handling Rules
• Applying Day 2 Segment Priorities to the Chosen Brand
• Setting Day 4 Measurement Plans for Each Initiative
• Completing the AI Marketing Plan

Methodology

The programme combines data workshops with case analysis. Participants score sample customer data, design personalised journeys, review generative content against brand rules and interpret attribution and incrementality results. Cases come from retail banking, telecommunications and hospitality brands.

• Suggested activity: Ranking customer segments by predicted lifetime value and reach.
• Suggested activity: Reviewing AI-generated campaign variants against brand guidelines.
• Suggested activity: Comparing attribution results with a holdout test for one campaign.

Capstone Output

Each participant completes an AI marketing plan for one segment or brand. It contains a data readiness assessment, priority segments, personalisation rules, generative content controls, a measurement plan and governance controls. It is presented to the marketing leadership team as the basis for the next campaign cycle.

Frequently Asked Questions

Is AI-powered marketing personalisation training suitable for senior marketing leaders?

Yes. It is set at advanced level for leaders who own marketing budgets or customer analytics and assumes marketing management experience, but no programming background.

Which models does the AI-powered marketing personalisation programme rest on?

It rests on customer lifetime value modelling, marketing mix modelling, incrementality testing and ISO/IEC 42001.

How is the plan from AI-powered marketing personalisation training used at work?

You can use it to set priorities for the next campaign cycle, agree measurement methods and apply content and data controls.

How does AI-powered marketing personalisation differ from a general digital marketing programme?

It focuses on predictive models, personalisation, causal measurement and governance of AI, while general programmes cover channels and campaign planning.