Description

This curriculum spans the technical and organisational complexity of a multi-workshop program, equipping teams to build and maintain enterprise-grade channel optimisation systems comparable to those developed in strategic advisory engagements or internal data science capability builds.

Module 1: Defining Channel Performance Metrics Aligned with Business Outcomes

Selecting primary KPIs (e.g., CAC, LTV, ROAS) based on business model (B2B vs. B2C, subscription vs. transactional)
Mapping digital and offline channels to funnel stages (awareness, consideration, conversion) for consistent attribution
Standardizing metric definitions across departments to prevent misalignment between marketing and finance
Adjusting performance thresholds dynamically based on seasonality and macroeconomic indicators
Implementing incrementality testing frameworks to distinguish baseline demand from channel-driven lift
Designing custom dashboards that reflect channel-specific success criteria without data overload
Establishing data latency tolerances for real-time vs. batch reporting in performance evaluation
Integrating brand health metrics (e.g., share of voice, sentiment) into channel performance scoring

Module 2: Data Integration and Identity Resolution Across Touchpoints

Choosing between deterministic and probabilistic identity resolution based on data availability and privacy constraints
Implementing customer data platforms (CDPs) with clean room capabilities for cross-channel identity matching
Resolving conflicts between first-party data and third-party measurement discrepancies
Handling data silos in legacy CRM, ERP, and point-of-sale systems during integration
Designing data schemas that support both online session-level tracking and offline transaction reconciliation
Managing GDPR and CCPA compliance when stitching user journeys across devices
Establishing golden record protocols for customer identity with fallback matching logic
Evaluating trade-offs between data freshness and processing cost in real-time identity graphs

Module 3: Multi-Touch Attribution Modeling and Implementation

Selecting between attribution models (first-touch, last-touch, linear, time decay, position-based) based on funnel length and channel mix
Building custom algorithmic attribution models using Markov chains or Shapley values with production-grade code
Validating attribution outputs against controlled media mix model (MMM) results for consistency
Handling offline conversions (e.g., in-store, call center) in digital-first attribution frameworks
Adjusting for channel saturation and diminishing returns within attribution weight calculations
Documenting model assumptions and limitations for auditability by finance and compliance teams
Automating retraining cycles for attribution models based on data drift thresholds
Communicating attribution results to stakeholders without oversimplifying model uncertainty

Module 4: Media Mix Modeling for Strategic Channel Allocation

Specifying regression models (linear, log-linear, or distributed lag) based on data granularity and channel response curves
Defining appropriate time lags for offline channels (e.g., TV, OOH) in weekly or monthly models
Isolating external factors (competitor activity, weather, economic shifts) from channel performance signals
Selecting and validating control variables to prevent omitted variable bias
Estimating carryover and saturation effects for each channel using adstock transformations
Integrating MMM outputs with real-time bidding systems for tactical adjustments
Running scenario simulations for budget reallocation under constrained total spend
Establishing model governance processes for version control and stakeholder review

Module 5: Real-Time Bidding and Programmatic Channel Optimization

Configuring bid strategies (CPA, ROAS, budget pacing) in DSPs based on campaign objectives
Implementing audience exclusions and frequency capping to prevent overexposure and waste
Integrating first-party data segments into demand-side platforms with privacy-safe hashing
Monitoring bid landscape dynamics and adjusting floor price assumptions in real time
Diagnosing and resolving discrepancies between impression delivery and attribution data
Setting up automated alerts for abnormal spend patterns or delivery shortfalls
Optimizing creative rotation based on performance lift across audience segments
Managing supply path optimization (SPO) to reduce fees and improve viewability

Module 6: Cross-Channel Budget Allocation and Trade-Off Analysis

Building optimization models that balance short-term ROAS with long-term brand equity investment
Allocating shared budgets between performance and brand channels using constrained optimization
Quantifying opportunity costs when shifting spend from high-ROAS, low-volume channels to scalable but lower-efficiency channels
Modeling budget elasticity across channels to identify inflection points for scaling
Simulating competitive response scenarios when increasing spend in contested markets
Integrating channel-specific operational constraints (e.g., production lead times for OOH)
Reconciling top-down strategic goals with bottom-up channel capacity forecasts
Documenting allocation decisions with audit trails for finance and executive review

Module 7: Testing and Experimentation Frameworks for Channel Innovation

Designing geo-based lift tests with proper control group selection and statistical power analysis
Implementing A/B/n tests for creative, audience, or bidding variations within a channel
Isolating test effects from external market shocks using synthetic control methods
Scaling successful test results while accounting for the novelty effect and regression to the mean
Managing test sequencing to avoid interference between overlapping experiments
Establishing data retention and access protocols for post-test forensic analysis
Calculating minimum detectable effect sizes based on historical variance and spend levels
Integrating test results into long-term forecasting and planning cycles

Module 8: Governance, Auditability, and Cross-Functional Alignment

Creating standardized data dictionaries and metadata documentation for channel data assets
Implementing role-based access controls for budget, targeting, and reporting systems
Establishing change management protocols for model updates and data source migrations
Conducting quarterly model audits to validate assumptions and detect performance decay
Reconciling discrepancies between internal analytics and third-party measurement (e.g., Nielsen, Comscore)
Facilitating structured review cycles between marketing, finance, and data science teams
Documenting data lineage from source systems to final decision outputs
Managing vendor contracts and SLAs for external analytics and media platforms

Module 9: Scaling Optimization Systems and Future-Proofing Infrastructure

Architecting cloud-based data pipelines to support real-time channel performance monitoring
Selecting between monolithic and microservices architectures for optimization platforms
Implementing automated failover and redundancy for critical decisioning systems
Designing APIs for bidirectional communication between optimization engines and execution platforms
Planning for deprecation of third-party cookies and device IDs in identity resolution systems
Integrating AI-driven forecasting models with budgeting and planning tools
Establishing model versioning and rollback procedures for production systems
Scaling data storage and compute resources to handle increasing granularity (e.g., impression-level data)