Description

This curriculum spans the technical and operational complexity of a multi-workshop program for building and maintaining machine learning systems in live marketing environments, comparable to an internal capability initiative for scaling ML-driven decisioning across global customer touchpoints.

Module 1: Defining Business Objectives and KPIs for ML-Driven Marketing

Selecting primary conversion metrics (e.g., CAC, LTV, ROAS) based on business model and channel maturity
Aligning machine learning outputs with executive-level OKRs while maintaining model interpretability
Deciding between last-touch, multi-touch, and algorithmic attribution frameworks for training data
Handling discrepancies between finance-reported revenue and marketing-attributed conversions
Establishing thresholds for statistical significance in A/B testing to trigger model retraining
Documenting data lineage for KPI calculations to ensure auditability across departments

Module 2: Data Infrastructure and Pipeline Design for Marketing ML

Choosing between batch and real-time ingestion for CRM, ad platform, and website event data
Designing schema evolution strategies for customer data platforms integrating offline and online sources
Implementing data quality checks for UTM parameter consistency across digital campaigns
Resolving identity resolution challenges across cookies, device IDs, and authenticated users
Configuring data retention policies to comply with privacy regulations while preserving model training windows
Architecting feature stores to enable consistent feature reuse across personalization and forecasting models

Module 3: Feature Engineering for Customer Behavior Modeling

Calculating recency, frequency, monetary (RFM) features from transaction logs with irregular purchase cycles
Deriving engagement decay curves for email and push notification interactions
Encoding categorical campaign attributes (e.g., creative version, audience segment) for model compatibility
Creating time-lagged features to capture delayed response effects in upper-funnel campaigns
Normalizing spend and impression data across disparate ad platforms with varying reporting granularity
Handling sparse behavioral features for cold-start users using demographic proxies

Module 4: Model Selection and Training for Marketing Use Cases

Choosing between logistic regression, gradient boosting, and neural networks for conversion prediction based on data volume and interpretability needs
Implementing stratified sampling to address class imbalance in low-conversion-rate campaigns
Training churn models with time-varying covariates using survival analysis techniques
Validating recommendation engine outputs against business rules (e.g., product availability, margin thresholds)
Calibrating probability outputs from classifiers to align with observed conversion rates
Managing model drift detection by monitoring feature distribution shifts across geographic markets

Module 5: Causal Inference and Incrementality Testing

Designing geo-based lift studies to measure true incrementality of digital ad spend
Implementing propensity score matching to evaluate campaign impact on non-randomized audiences
Integrating synthetic control methods when historical data limits randomized experimentation
Quantifying cannibalization effects between owned channels (e.g., email vs. push notifications)
Adjusting for external factors (e.g., seasonality, macroeconomic events) in marketing mix models
Deploying holdout groups for ongoing validation of lookalike audience performance

Module 6: Real-Time Decision Systems and Activation

Configuring model scoring latency SLAs for real-time bidding and personalization engines
Orchestrating model deployment across cloud and edge environments for low-latency use cases
Implementing fallback logic for scoring failures to maintain campaign delivery continuity
Mapping model output scores to business decision thresholds (e.g., send/don’t send email)
Coordinating model updates with campaign launch calendars to avoid operational conflicts
Integrating model decisions with third-party marketing automation platforms via API rate limiting

Module 7: Governance, Compliance, and Model Risk Management

Conducting fairness audits to detect bias in audience targeting models across demographic groups
Documenting model assumptions and limitations for legal review in regulated industries
Implementing data minimization practices in feature sets to comply with GDPR and CCPA
Versioning models and features to support reproducibility during regulatory audits
Establishing escalation paths for model performance degradation impacting revenue
Coordinating cross-functional reviews involving legal, privacy, and marketing before model deployment

Module 8: Performance Monitoring and Continuous Optimization

Building dashboards to track model prediction stability and business outcome correlation over time
Scheduling retraining cycles based on data freshness and concept drift detection
Quantifying the opportunity cost of model downtime during retraining or redeployment
Conducting root cause analysis when model-driven campaigns underperform baseline rules
Measuring feature importance decay to identify candidates for engineering refresh
Optimizing inference costs by pruning underperforming models from production rotation