Marketing and Customer Analytics

Data Analytics and Business Intelligence October 25, 2025
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Introduction

This specialized course focuses on leveraging customer data to optimize marketing spend, personalize customer journeys, and maximize profitability. Participants will learn how to measure the effectiveness of various channels, model customer lifetime value, and build sophisticated segmentation strategies. We delve into digital analytics, attribution modeling, and the critical relationship between marketing activity and financial outcomes. This program equips marketing and customer experience professionals with the analytical tools necessary to prove campaign ROI and drive hyper-personalized engagement.

Objectives

Upon completion of this course, participants will be able to:

  • Calculate and interpret key marketing KPIs, including CPA, CAC, and ROI by channel.
  • Implement and evaluate various marketing attribution models (first-touch, multi-touch, time decay).
  • Develop predictive models for Customer Lifetime Value (CLV) and churn probability.
  • Utilize web and digital analytics tools (e.g., Google Analytics) for customer behavior mapping.
  • Design and analyze A/B tests to optimize conversion rates on digital assets.
  • Apply clustering techniques (e.g., K-means) for behavioral customer segmentation.
  • Analyze the full customer journey map to identify critical drop-off and engagement points.
  • Translate complex analytical findings into actionable budget allocation and campaign decisions.

Target Audience

  • Marketing Analysts and Specialists
  • Digital Marketing Managers
  • Customer Experience (CX) Professionals
  • Product Managers and Owners
  • E-commerce and Growth Managers
  • Brand and Communication Specialists

Methodology

The course employs a hands-on, case-based learning approach. **Mini-case studies** involve interpreting the results of a hypothetical A/B test run on an e-commerce landing page. **Group activities** center on developing a customer segmentation strategy for a new subscription service using provided behavioral data. **Individual exercises** require participants to calculate CLV and create a simple multi-touch attribution report using simulated marketing data. **Scenarios** involve a sudden decline in marketing ROI, requiring analysis and **syndicate discussions** on which channels to cut or increase investment in based on data analysis.

Personal Impact

  • Validate marketing spend and prove the financial ROI of all campaign activities.
  • Transition from relying on intuition to making precise, data-backed budget decisions.
  • Gain expertise in high-demand areas like CLV modeling and advanced attribution.
  • Improve ability to communicate marketing effectiveness to the finance and executive teams.
  • Personalize customer outreach more effectively, increasing engagement rates.

Organizational Impact

  • Optimize the marketing budget, ensuring maximum return and minimizing wasted spend on low-performing channels.
  • Improve customer retention and profitability through accurate CLV prediction and targeted loyalty programs.
  • Accelerate business growth by identifying and exploiting high-potential customer segments.
  • Enable faster, data-driven decisions on campaign changes and resource allocation.
  • Establish a single source of truth for marketing performance across the organization.

Course Outline

UNIT 1: Measuring Marketing Effectiveness

Core KPIs and Metrics
  • Defining the Marketing Funnel and Associated Metrics (Reach, Engagement, Conversion)
  • Calculating Cost Per Acquisition (CPA) and Customer Acquisition Cost (CAC)
  • Measuring Marketing Return on Investment (ROI) and Efficiency Ratio
  • Segmentation and Targeting Metrics (Reach, Frequency, Recency)
  • Integrating Data from Paid Media Platforms (Google Ads, Social)

UNIT 2: Customer Segmentation and Value Modeling

Predicting Future Profitability
  • Advanced RFM Analysis for Customer Grouping
  • Implementing Behavioral and Demographic Segmentation (Clustering)
  • Calculating and Forecasting Customer Lifetime Value (CLV)
  • Building Predictive Models for Customer Churn and Retention Probability
  • Identifying High-Value Customer Attributes for Lookalike Targeting

UNIT 3: Attribution and Channel Analysis

Credit Assignment and Budgeting
  • Understanding Single-Touch vs. Multi-Touch Attribution Models
  • Analyzing the Customer Journey and Touchpoint Sequencing
  • Data-Driven Attribution (DDA) Principles and Implementation
  • Budget Allocation Optimization based on Channel ROI
  • Measuring the Cross-Channel Impact (Online to Offline)

UNIT 4: Digital and Web Analytics

Behavior Mapping and Optimization
  • Key Metrics in Web Analytics (Bounce Rate, Exit Rate, Conversion Rate)
  • Funnel Analysis and Identifying Conversion Drop-Off Points
  • A/B Testing: Design, Sample Size Calculation, and Statistical Interpretation
  • Measuring User Experience (UX) using quantitative metrics
  • Introduction to Session Replay and Heatmap Analysis

UNIT 5: Data Visualization and Strategy

Translating Insights to Action
  • Designing Marketing Dashboards focused on Campaign Performance and Budget
  • Techniques for Visualizing Customer Journey and Flow
  • Developing a Data-Driven Marketing Strategy and Roadmap
  • Privacy and Data Protection in Customer Analytics (CCPA, Cookie Consent)
  • Communicating Analytical Findings to Creative and Sales Teams

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