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

• Define a strategic problem and structure a corresponding analytical hypothesis for testing.
• Perform advanced customer or business segmentation to identify high-value target groups.
• Conduct A/B testing and interpret results to support clear, data-driven decisions on product features or campaigns.
• Apply modeling techniques (e.g., regression, time series) to forecast business outcomes and resource needs.
• Calculate and communicate the financial Return on Investment (ROI) of analytical insights and proposed changes.
• Develop executive-level presentations that frame data in a strategic, persuasive narrative.
• Identify critical data gaps necessary for answering key strategic questions.
• Utilize scenario analysis and sensitivity testing to evaluate decision robustness under uncertainty.

• Business Analysts and Senior Analysts
• Strategy and Planning Professionals
• Department Managers and Team Leaders
• Product Managers and Marketing Managers
• Operations and Process Improvement Specialists
• Consultants and Advisors

The methodology is heavily focused on real-world business strategy application. **Mini-case studies** involve analyzing the impact of a product pricing change or a new marketing channel. **Scenarios** require participants to define the analytical approach to a major market entry decision. **Group activities** center on building a presentation and data-backed recommendation for a CEO to invest in a specific business unit. **Individual exercises** focus on calculating customer lifetime value (CLV) and building a simple predictive model for sales forecasting. **Syndicate discussions** debate the ethical responsibility of analysts when data supports unpopular strategic recommendations.

UNIT 1: Framing the Strategic Question

• Distinguishing Strategic Analysis from Operational Reporting
• Techniques for Scoping and Structuring a Business Problem
• Defining Key Performance Indicators (KPIs) and their strategic alignment
• Developing and Testing Data-Driven Hypotheses
• Introduction to Decision Trees and Expected Value Analysis

UNIT 2: Advanced Segmentation and Insight Generation

• Performing Cohort Analysis to track long-term trends and behavior
• Techniques for RFM (Recency, Frequency, Monetary) Analysis
• Clustering and Market Basket Analysis for Product Strategy
• Modeling Customer Lifetime Value (CLV) and its strategic implications
• Analyzing drivers of churn, retention, and loyalty

UNIT 3: Forecasting and Predictive Modeling

• Introduction to Predictive Modeling for Business Forecasting
• Using Regression Analysis to understand causal relationships
• Time Series Analysis for Sales, Demand, and Resource Planning
• Building and Interpreting Decision Support Models
• Evaluating Model Accuracy and Understanding Model Risk

UNIT 4: Experimentation and Causality

• Fundamentals of A/B Testing Design and Interpretation
• Ensuring Statistical Significance and Controlling for External Variables
• Measuring the True Incremental Impact of Interventions
• Quasi-Experimentation Techniques (e.g., Difference-in-Differences)
• Avoiding common pitfalls in correlation vs. causation analysis

UNIT 5: Communication and Strategy Execution

• Building a Persuasive Business Case for Change based on Data
• Calculating the Financial ROI and Opportunity Cost of Insights
• Developing Executive-Ready Data Presentations and Dashboards
• The Art of Data Storytelling for Senior Leadership
• Monitoring and measuring the post-implementation success of a strategy