5 Topics
5 Days
15 Sessions
Programme Overview
Training Description
Who Should Attend
This course is ideal for:
· Data Scientists and Analysts in Marketing
· CRM and Loyalty Program Managers
· Marketing Automation Specialists
· Product Marketing and E-commerce Managers
· Digital Strategists and Innovation Leads
· Customer Experience (CX) Officers.
Session Objectives
- Build and deploy predictive modeling techniques.
- Master real-time data activation for campaigns.
- Utilize AI to drive hyper-personalized content.
- Forecast customer Lifetime Value (LTV) and churn risk.
- Integrate analytics into the marketing technology stack.
About the Course
This advanced course is designed to transition marketers from basic segmentation to creating individualized customer journeys using data science. Participants will master how to leverage predictive analytics, machine learning (ML), and real-time data streams to forecast customer behavior, anticipate needs, and deliver content, product recommendations, or offers at the precise moment of impact. The training covers everything from building and testing sophisticated predictive models, such as churn and lifetime value (LTV) scores, to deploying dynamic content across all channels—email, web, app, and advertising. Ultimately, you'll learn to drive significant revenue growth by minimizing wasted spend and maximizing relevance at a one-to-one scale.
Curriculum & Topics
5 Topics | 15 Sessions
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Workshop 1.1:
Understanding the difference between basic personalization and true hyper-personalization.
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Workshop 1.2:
Setting up the data infrastructure required for real-time customer profiles and activation.
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Workshop 1.3:
Reviewing ethical considerations and privacy compliance (e.g., anonymization) in personalization.
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Workshop 2.1:
Methods for calculating and utilizing Customer Lifetime Value (CLV) models for targeting.
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Workshop 2.2:
Techniques for building and testing churn prediction models to identify at-risk customers.
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Workshop 2.3:
Introduction to regression and classification algorithms relevant to marketing forecasting.
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Workshop 3.1:
Integrating Customer Data Platforms (CDPs) and other tools for real-time event streaming.
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Workshop 3.2:
Designing dynamic decision-making rules to trigger personalized actions across channels instantly.
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Workshop 3.3:
Orchestrating complex cross-channel journeys based on immediate behavioral signals.
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Workshop 4.1:
Utilizing AI and machine learning to generate dynamic content blocks and product recommendations
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Workshop 4.2:
Strategies for personalizing user interface (UI) and website experiences based on profile data.
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Workshop 4.3:
Advanced A/B/n testing and multivariate testing of personalized elements
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Workshop 5.1:
Developing metrics and attribution models necessary to measure the ROI of hyper-personalization.
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Workshop 5.2:
Techniques for scaling predictive campaigns across large customer bases efficiently.
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Workshop 5.3:
Best practices for maintaining model accuracy and addressing model drift over time.
