Build practical skills in advanced statistical analysis to solve complex business problems with data. Apply choice modeling, conjoint analysis, causal inference, multidimensional scaling, factor analysis, structural equation modeling, and marketing mix modeling to evaluate customer preferences, estimate treatment effects, uncover market opportunities, and optimize marketing investments. Develop Python implementations using industry-standard libraries, interpret results responsibly, and translate multiple analyses into stakeholder-ready recommendations that communicate uncertainty, justify business decisions, and drive strategic action.
Overview
Syllabus
- Course Introduction
- Learn how advanced statistical methods support business decisions by building predictive, causal, and descriptive models and turning results into stakeholder-ready recommendations.
- How Customers Make Trade-Offs
- Learn how choice modeling quantifies customer trade-offs, estimates feature preferences, interprets utility coefficients, and supports product, pricing, and marketing decisions.
- Multinomial Logit in Python
- Build multinomial logit choice models in Python by encoding data, fitting logistic regression, interpreting coefficients, and translating results into product insights.
- From Coefficients to Product Decisions
- Interpret choice model coefficients, evaluate feature trade-offs, perform what-if analyses, and prioritize product investments while recognizing the limits of model-based predictions.
- Conjoint Design & Part-Worth Utilities
- Apply conjoint analysis to compare feature importance, estimate willingness to pay, score product bundles, and support pricing and product decisions.
- Estimate Part-Worths from Survey Choice Data
- Implement conjoint analysis in Python to estimate part-worth utilities, rank attribute importance, and recommend high-impact product features.
- WTP and Pricing Implications
- Translate conjoint model outputs into pricing recommendations by estimating willingness to pay, scoring product bundles, and communicating pricing tradeoffs responsibly.
- Why Observational Customer Data Misleads You
- Explain how selection bias and confounding distort observational analyses, and compare matching and propensity weighting to improve causal estimates.
- Assumptions and Failure Modes
- Evaluate causal assumptions, diagnose common failure modes, and communicate observational results with appropriate caveats and next-step recommendations.
- Propensity Modeling and Balance Diagnostics
- Build propensity models, assess covariate balance, and determine whether observational data supports a credible causal analysis.
- Matching vs. IPW and Effect Estimation
- Estimate causal treatment effects with inverse probability weighting, compare adjusted and naive results, and quantify selection bias in business outcomes.
- Perceptual Spaces and Similarity
- Analyze perceptual maps to identify product similarity, cannibalization risk, and whitespace opportunities that inform positioning and portfolio decisions.
- Turning Maps into Strategy
- Develop positioning strategies by combining perceptual maps, preference data, and customer behavior to guide product, messaging, and portfolio decisions.
- MDS in scikit-learn
- Implement multidimensional scaling in scikit-learn to build perceptual maps, identify product similarities, and uncover positioning opportunities from feature data.
- Latent Variables: When Surveys Aren't Direct
- Discover latent customer segments with factor analysis by identifying underlying constructs, interpreting factor loadings, and generating scores for segmentation.
- Validity and Pitfalls
- Validate factor analysis results by identifying model pitfalls, evaluating construct validity, and communicating evidence-based customer insights responsibly.
- SEM: What it is and When it's Worth it
- Recognize when structural equation modeling is appropriate, interpret path models and fit indices, and distinguish SEM from factor analysis.
- Exploratory Factor Analysis (EFA)
- Implement exploratory factor analysis in scikit-learn by preparing survey data, extracting latent factors, and interpreting loadings and heatmaps.
- What MMM Are, and What They Can and Can't Tell You
- Explain how marketing mix models estimate channel impact, account for carryover and seasonality, and support budget decisions while recognizing causal limits.
- Build an MMM Baseline with Time-Aware Controls
- Build a marketing mix model baseline by engineering adstock features, adding time-aware controls, fitting Ridge regression, and evaluating forecast accuracy.
- Interpretation & Budget Implications
- Translate marketing mix model results into budget recommendations by interpreting channel coefficients, communicating uncertainty, and proposing validation steps.
- Insight Checklist for Stakeholders
- Communicate analytical findings with clear recommendations by matching evidence to decision stakes, explaining uncertainty, and leading with actionable insights.
- Create a Stakeholder-Ready Insight Artifact
- Synthesize results from multiple analyses into a stakeholder-ready memo that integrates evidence, communicates business impact, and recommends clear next steps.
- Course Conclusion
- Developed the skills to apply advanced statistical techniques, communicate actionable insights, and support confident, evidence-based business decisions.
- Subscription Tier Launch Planning
- You are a Customer Analytics Lead. Analyze multiple data sources to recommend which features, pricing, packaging, and marketing channels will maximize adoption of a new subscription tier.
Taught by
Anurag Srivastava
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