Mastering Marketing Mix Modelling in the AI Era
You're under pressure. Budgets are tight, stakeholders demand proof, and marketing performance feels harder to measure than ever. Attribution is messy, channels overlap, and leadership wants answers - not assumptions. You know MMM matters, but the models feel outdated, slow, and disconnected from real-time decisions. Meanwhile, AI is transforming how the top companies forecast, optimise, and prove ROI. If you're not leveraging AI-powered MMM, you're falling behind - quietly. Others are already using machine learning to model cross-channel impact with precision, simulate future spend, and deliver board-ready recommendations with confidence. Mastering Marketing Mix Modelling in the AI Era is your structured path from uncertainty to authority. This course guides you from foundational concepts to advanced AI-integrated modelling in just 30 days, with a fully developed, defensible MMM framework you can present to leadership. One recent learner, Priya M., Market Insights Lead at a global CPG brand, applied the course framework to her Q3 marketing plan. She identified $2.3M in underperforming spend, reallocated budget to high-impact channels, and presented a data-driven forecast that secured a 40% increase in next quarter’s budget. She didn’t just prove value - she became the go-to strategic advisor in her division. This isn’t theoretical. You’ll build a live, transparent, AI-enhanced model using your own data structure, step by step. You’ll gain clarity, credibility, and career momentum - the trifecta every analytical marketer needs to advance. You’ll learn how to isolate true channel contribution, incorporate incrementality, work with sparse datasets, and future-proof your skillset against rising automation. This course is the bridge from reactive reporting to proactive strategy. Here’s how this course is structured to help you get there.Course Format & Delivery Details Mastering Marketing Mix Modelling in the AI Era is a self-paced, on-demand learning experience designed for working professionals. Enrol anytime, access immediately, and progress at your own speed - no deadlines, no live sessions, no scheduling conflicts. Immediate, Lifetime Access with No Time Pressure
You gain instant access to all course materials online. Complete the programme in as little as 25 hours, or spread it over weeks - your pace, your priorities. Most learners complete the core model-building framework within two weeks and begin applying insights to their roles immediately. - Self-paced, on-demand learning with no fixed start or end dates
- Typical completion time: 25–30 hours, with first actionable insights in under 10 hours
- Lifetime access to all content, including future updates at no additional cost
- 24/7 global access from any device, fully mobile-friendly and responsive
Hands-on Support & Trusted Certification
You’re not learning in isolation. Each module includes structured guidance, real-world templates, and direct pathways to applying your work in your current role. You also receive instructor-reviewed feedback pathways on key submissions to ensure you stay on track. Upon completion, you earn a Certificate of Completion issued by The Art of Service - a globally recognised credential trusted by Fortune 500 teams, consulting firms, and marketing leaders. This certificate validates your mastery of modern, AI-integrated MMM and strengthens your professional credibility on LinkedIn, resumes, and internal promotions. Risk-Free Investment with Guaranteed Results
We remove all risk with a 30-day, 100% money-back guarantee. If the course doesn’t deliver measurable clarity, practical tools, or career ROI, simply request a refund - no questions asked. - One straightforward price with no hidden fees, subscriptions, or add-ons
- Secure payment via Visa, Mastercard, and PayPal
- After enrolment, you’ll receive a confirmation email, and your access details will be delivered separately once your course materials are fully configured
Designed for Real Marketers, Real Data, and Real Constraints
You don’t need a PhD in statistics or a data science team. This course works even if: - You’ve never built a marketing mix model before
- Your organisation uses siloed or incomplete data
- You’re time-constrained and need fast, credible insights
- Your leadership questions marketing’s ROI
- You’re transitioning from traditional analytics to advanced modelling
Our alumni include media planners, performance marketers, insights managers, and growth leads from industries ranging from retail to SaaS. They’ve succeeded not because they had perfect datasets - but because this course teaches you how to build defensible models even with real-world limitations. This works even if your current tools are basic, your data is fragmented, or you’re the only one in your team focused on attribution. You’ll walk through every gap, leverage tested workarounds, and deploy a model that withstands scrutiny. Your success is our priority. That’s why every step is engineered for clarity, applicability, and measurable impact - so you finish not just informed, but empowered.
Module 1: Foundations of Modern Marketing Mix Modelling - Understanding MMM: Definition and core purpose in the AI era
- How MMM differs from attribution, incrementality, and MTA
- When to use MMM versus other measurement approaches
- Core components: Dependent and independent variables
- Defining marketing spend categories and channel granularity
- Baseline versus incremental sales: Separating natural demand
- The role of control variables: Competitor activity, pricing, seasonality
- Why MMM is essential for budget optimisation and forecasting
- Limitations of traditional MMM and where AI enhances accuracy
- Use cases across industries: Retail, CPG, SaaS, e-commerce
- Common misconceptions and how to address stakeholder skepticism
- Setting realistic expectations for model performance and confidence
- Understanding linear versus non-linear channel effects
- Introduction to saturation and diminishing returns curves
- How external shocks impact model reliability
- Case study: From overattribution to true channel value
Module 2: Data Preparation and Variable Engineering - Identifying required data sources: CRM, ad platforms, sales systems
- Data resolution: Daily, weekly, and monthly trade-offs
- Aggregating spend data across digital and offline channels
- Aligning marketing spend with time periods consistently
- Handling missing data: Imputation methods and conservative assumptions
- Creating control variables: Economic indicators, weather, holidays
- Competitive spend proxies when direct data is unavailable
- Adjusting for media quality differences within channels
- Building media weight variables using GRPs and impressions
- Normalising spend for inflation, currency, and market size
- Creating lagged variables to capture delayed response effects
- Adstock transformation: Concept and practical implementation
- Selecting optimal decay rates and carryover periods
- Using benchmarks to calibrate adstock assumptions
- Creating channel-specific saturation functions
- Feature scaling and variable standardisation techniques
- Dimensionality reduction: When and how to simplify inputs
- Avoiding multicollinearity in marketing variable selection
- Validating data alignment across sources
- Documentation framework for audit-ready datasets
Module 3: Core Modelling Frameworks and Statistical Principles - Ordinary Least Squares (OLS) regression: When and how to apply it
- Assumptions of linear regression and how to test them
- Detecting and correcting for heteroscedasticity
- Handling autocorrelation in time series marketing data
- Interpreting p-values, R-squared, and adjusted R-squared
- Understanding confidence intervals for coefficient estimates
- Model fit vs. model usefulness: Avoiding overfitting
- Stepwise regression and variable selection strategies
- Lasso and Ridge regression for marketing variable selection
- Introduction to Bayesian statistics for MMM
- Benefits of Bayesian approaches: Incorporating prior knowledge
- Selecting informative and weakly informative priors
- Markov Chain Monte Carlo (MCMC) sampling basics
- Gibbs sampling and Hamiltonian Monte Carlo overview
- Convergence diagnostics: R-hat, trace plots, effective sample size
- Bayesian vs. frequentist MMM: Trade-offs and use cases
- Using hierarchical models to share learning across regions
- Model validation: Walk-forward testing and holdout periods
- Cross-validation techniques for time series data
- Residual analysis: Identifying structural model gaps
Module 4: AI and Machine Learning Integration in MMM - Why AI improves MMM: Speed, non-linearity, and automation
- Types of ML models used in MMM: GLMs, GAMs, boosted trees
- Generalised Additive Models (GAMs) for flexible response curves
- Gradient boosting for handling complex interactions
- XGBoost and LightGBM integration with MMM workflows
- Neural networks for high-dimensional marketing data
- AutoML tools for rapid model iteration and comparison
- Hyperparameter tuning for optimal model performance
- Feature importance analysis using SHAP values
- Partial dependence plots to visualise channel impact
- Using ensembles to combine multiple MMM approaches
- Natural language processing for incorporating media content tone
- Image recognition to assess creative quality in TV and digital
- Time series forecasting models: Prophet and ARIMA hybrids
- Deep learning for offline-online response alignment
- AI-powered gap detection: Identifying missing variables
- Automated adstock and saturation estimation
- Real-time model updating with streaming data
- Transfer learning: Applying models across markets
- Bias detection in AI-driven MMM outputs
Module 5: Model Calibration, Validation, and Diagnostics - Setting up train, validation, and test datasets
- Choosing evaluation metrics: MAPE, RMSE, MAE
- Interpreting model error in business context
- Simulating out-of-sample performance
- Backtesting models against historical budget changes
- Detecting overfitting using holdout performance
- Walk-forward analysis for dynamic validation
- Residual diagnostics: Patterns, trends, and outliers
- Checking for omitted variable bias
- Validating channel coefficient stability over time
- Stress-testing models with extreme scenarios
- Sensitivity analysis: How changes affect outputs
- Confidence bands for forecasted impact
- Bootstrapping to estimate parameter uncertainty
- Bayesian credible intervals vs. frequentist confidence intervals
- Model updating frequency: Quarterly, monthly, or event-triggered
- Version control for model iterations
- Documentation standards for audit and compliance
- User acceptance testing with stakeholders
- Creating model validation reports for leadership
Module 6: Saturation, Diminishing Returns, and Non-Linearity - Understanding the law of diminishing returns in marketing
- Power transformations: Square root and logarithmic
- Michaelis-Menten function for saturation modelling
- Logistic and Hill functions for S-shaped response curves
- Selecting response functions based on channel type
- Estimating saturation thresholds for digital and offline
- Interpreting diminishing returns in spend optimisation
- Using non-linear least squares for curve fitting
- Combining adstock and saturation in one transformation
- Visualising response curves for stakeholder communication
- Channel-specific elasticity: How spend drives marginal gains
- Optimal spend levels: Where marginal cost equals marginal return
- Forecasting impact of doubling or halving spend
- Example: TV versus search versus social media curves
- Calibrating curves using A/B test results
- Incorporating competitive saturation effects
- Dynamic saturation based on market conditions
- Model validation using holdback experiments
- Presenting diminishing returns insights to finance teams
- Building interactive dashboards for spend simulation
Module 7: Attribution of Impact and Incrementality Testing - Measuring true incremental impact of marketing activities
- Designing geo-based lift tests for offline channels
- Time-based holdout testing for digital campaigns
- Using control groups to measure baseline sales
- Calculating net lift and statistical significance
- Integrating test results into MMM coefficient calibration
- Using Bayesian priors informed by A/B tests
- Blending observational and experimental data
- Addressing endogeneity and reverse causality
- Instrumental variable approaches for causal inference
- Regression discontinuity design in marketing contexts
- Difference-in-differences for campaign evaluation
- Using synthetic controls for market comparisons
- Validating MMM against controlled experiments
- Quantifying attribution error in current models
- Confidence scoring for channel impact estimates
- Communicating uncertainty to decision-makers
- Creating attribution heatmaps across channels
- Time-delayed attribution windows
- Multi-touch influence within MMM frameworks
Module 8: Forecasting, Optimisation, and Scenario Planning - Building forward-looking MMM simulations
- Setting assumptions for future spend and market conditions
- Creating multiple budget allocation scenarios
- Defining optimisation objectives: Revenue, margin, ROI
- Linear programming for budget reallocation
- Quadratic programming for non-linear response curves
- Constraint-based optimisation: Minimum spends, caps, rules
- Using Python’s PuLP and SciPy for optimisation
- Monte Carlo simulation for risk-aware forecasting
- Generating confidence intervals for forecasts
- Scenario planning: Best case, worst case, most likely
- Simulating economic downturns or market expansions
- Competitive response modelling in forecasts
- Product launch impact on baseline and incremental demand
- Introducing new channels into the model
- Forecasting halo and cannibalisation effects
- Long-range planning: 6, 12, 18-month forecasts
- Presenting trade-offs between growth and efficiency
- Automating scenario reports for regular review
- Building executive dashboards for ongoing use
Module 9: Cross-Channel Integration and Marketing Ecosystems - Modelling interdependencies between channels
- First and upper-funnel channel synergy effects
- Search and social interaction terms in regression
- TV’s role in boosting digital performance
- Online-to-offline (O2O) attribution modelling
- Geofencing and location-based validation
- CRM and email as retention versus acquisition drivers
- Paid, owned, earned media integration
- Partnership and co-marketing impact assessment
- Influencer marketing as a measurable variable
- Events and experiential marketing proxies
- Pricing and promotion interaction with media
- Distribution and availability as control factors
- Channel clustering: Grouping similar tactics
- Modelling media mix at market, region, and store level
- Granular targeting: DMA, postcode, audience segments
- Local media and affiliate network integration
- Dynamic creative impact on response curves
- Platform-specific algorithms and delivery efficiency
- Full-funnel journey synthesis within MMM
Module 10: Implementation, Governance, and Stakeholder Alignment - Building stakeholder buy-in for MMM adoption
- Identifying key influencers and decision-makers
- Creating tailored communication for finance, marketing, and leadership
- Translating technical outputs into business insights
- Designing executive summaries and one-pagers
- Visual storytelling with charts, heatmaps, and dashboards
- Running internal workshops to socialise findings
- Addressing political resistance to budget reallocation
- Establishing MMM as a governance process, not a one-off
- Setting up quarterly MMM reviews and refresh cycles
- Integrating MMM with annual planning and budget cycles
- Defining roles: Owner, analyst, validator, reviewer
- Data governance and update protocols
- Change log and version tracking for transparency
- Audit readiness and compliance considerations
- Vendor and agency collaboration using MMM outputs
- Negotiating media contracts based on efficiency metrics
- Linking MMM insights to performance KPIs
- Training regional teams on MMM fundamentals
- Scaling MMM across global markets
Module 11: Advanced Topics and Emerging Trends - Privacy-preserving MMM: Models without cookies or PII
- Federated learning for decentralised data environments
- Differential privacy in model training
- Cookieless and IDFA-compliant measurement approaches
- Using aggregated data from Google Ads, Meta, TikTok
- On-device conversion measurement integration
- Contextual and cohort-based targeting in MMM
- Generative AI for scenario narrative creation
- Automated insight generation from model outputs
- Large language models for stakeholder communication drafting
- Automated anomaly detection in marketing performance
- Real-time MMM for agile optimisation
- Streaming data pipelines for live updates
- Edge computing for low-latency model inference
- Integration with CDPs and marketing clouds
- API-driven MMM for workflow automation
- Multi-touch attribution fusion with MMM
- Short-term versus long-term brand building quantification
- Emotional resonance and creative quality metrics
- Future of AI in predictive marketing governance
Module 12: Certification Project and Real-World Application - Step-by-step guide to building your own MMM from scratch
- Selecting a business objective and defining scope
- Creating a data collection and preparation checklist
- Choosing appropriate model architecture
- Implementing adstock and saturation transformations
- Running regression and interpreting coefficients
- Validating model performance with diagnostics
- Generating forecasts and optimisation scenarios
- Designing a board-ready presentation deck
- Writing a clear, actionable executive summary
- Preparing appendices for technical reviewers
- Incorporating real data constraints and assumptions
- Building a defensible, transparent model narrative
- Submission framework for Certificate of Completion
- Instructor feedback pathway on key components
- Iterating based on review comments
- Finalising your professional portfolio piece
- Sharing your model with stakeholders securely
- Planning next steps: Scaling, automation, integration
- Joining The Art of Service alumni network for ongoing support
- Understanding MMM: Definition and core purpose in the AI era
- How MMM differs from attribution, incrementality, and MTA
- When to use MMM versus other measurement approaches
- Core components: Dependent and independent variables
- Defining marketing spend categories and channel granularity
- Baseline versus incremental sales: Separating natural demand
- The role of control variables: Competitor activity, pricing, seasonality
- Why MMM is essential for budget optimisation and forecasting
- Limitations of traditional MMM and where AI enhances accuracy
- Use cases across industries: Retail, CPG, SaaS, e-commerce
- Common misconceptions and how to address stakeholder skepticism
- Setting realistic expectations for model performance and confidence
- Understanding linear versus non-linear channel effects
- Introduction to saturation and diminishing returns curves
- How external shocks impact model reliability
- Case study: From overattribution to true channel value
Module 2: Data Preparation and Variable Engineering - Identifying required data sources: CRM, ad platforms, sales systems
- Data resolution: Daily, weekly, and monthly trade-offs
- Aggregating spend data across digital and offline channels
- Aligning marketing spend with time periods consistently
- Handling missing data: Imputation methods and conservative assumptions
- Creating control variables: Economic indicators, weather, holidays
- Competitive spend proxies when direct data is unavailable
- Adjusting for media quality differences within channels
- Building media weight variables using GRPs and impressions
- Normalising spend for inflation, currency, and market size
- Creating lagged variables to capture delayed response effects
- Adstock transformation: Concept and practical implementation
- Selecting optimal decay rates and carryover periods
- Using benchmarks to calibrate adstock assumptions
- Creating channel-specific saturation functions
- Feature scaling and variable standardisation techniques
- Dimensionality reduction: When and how to simplify inputs
- Avoiding multicollinearity in marketing variable selection
- Validating data alignment across sources
- Documentation framework for audit-ready datasets
Module 3: Core Modelling Frameworks and Statistical Principles - Ordinary Least Squares (OLS) regression: When and how to apply it
- Assumptions of linear regression and how to test them
- Detecting and correcting for heteroscedasticity
- Handling autocorrelation in time series marketing data
- Interpreting p-values, R-squared, and adjusted R-squared
- Understanding confidence intervals for coefficient estimates
- Model fit vs. model usefulness: Avoiding overfitting
- Stepwise regression and variable selection strategies
- Lasso and Ridge regression for marketing variable selection
- Introduction to Bayesian statistics for MMM
- Benefits of Bayesian approaches: Incorporating prior knowledge
- Selecting informative and weakly informative priors
- Markov Chain Monte Carlo (MCMC) sampling basics
- Gibbs sampling and Hamiltonian Monte Carlo overview
- Convergence diagnostics: R-hat, trace plots, effective sample size
- Bayesian vs. frequentist MMM: Trade-offs and use cases
- Using hierarchical models to share learning across regions
- Model validation: Walk-forward testing and holdout periods
- Cross-validation techniques for time series data
- Residual analysis: Identifying structural model gaps
Module 4: AI and Machine Learning Integration in MMM - Why AI improves MMM: Speed, non-linearity, and automation
- Types of ML models used in MMM: GLMs, GAMs, boosted trees
- Generalised Additive Models (GAMs) for flexible response curves
- Gradient boosting for handling complex interactions
- XGBoost and LightGBM integration with MMM workflows
- Neural networks for high-dimensional marketing data
- AutoML tools for rapid model iteration and comparison
- Hyperparameter tuning for optimal model performance
- Feature importance analysis using SHAP values
- Partial dependence plots to visualise channel impact
- Using ensembles to combine multiple MMM approaches
- Natural language processing for incorporating media content tone
- Image recognition to assess creative quality in TV and digital
- Time series forecasting models: Prophet and ARIMA hybrids
- Deep learning for offline-online response alignment
- AI-powered gap detection: Identifying missing variables
- Automated adstock and saturation estimation
- Real-time model updating with streaming data
- Transfer learning: Applying models across markets
- Bias detection in AI-driven MMM outputs
Module 5: Model Calibration, Validation, and Diagnostics - Setting up train, validation, and test datasets
- Choosing evaluation metrics: MAPE, RMSE, MAE
- Interpreting model error in business context
- Simulating out-of-sample performance
- Backtesting models against historical budget changes
- Detecting overfitting using holdout performance
- Walk-forward analysis for dynamic validation
- Residual diagnostics: Patterns, trends, and outliers
- Checking for omitted variable bias
- Validating channel coefficient stability over time
- Stress-testing models with extreme scenarios
- Sensitivity analysis: How changes affect outputs
- Confidence bands for forecasted impact
- Bootstrapping to estimate parameter uncertainty
- Bayesian credible intervals vs. frequentist confidence intervals
- Model updating frequency: Quarterly, monthly, or event-triggered
- Version control for model iterations
- Documentation standards for audit and compliance
- User acceptance testing with stakeholders
- Creating model validation reports for leadership
Module 6: Saturation, Diminishing Returns, and Non-Linearity - Understanding the law of diminishing returns in marketing
- Power transformations: Square root and logarithmic
- Michaelis-Menten function for saturation modelling
- Logistic and Hill functions for S-shaped response curves
- Selecting response functions based on channel type
- Estimating saturation thresholds for digital and offline
- Interpreting diminishing returns in spend optimisation
- Using non-linear least squares for curve fitting
- Combining adstock and saturation in one transformation
- Visualising response curves for stakeholder communication
- Channel-specific elasticity: How spend drives marginal gains
- Optimal spend levels: Where marginal cost equals marginal return
- Forecasting impact of doubling or halving spend
- Example: TV versus search versus social media curves
- Calibrating curves using A/B test results
- Incorporating competitive saturation effects
- Dynamic saturation based on market conditions
- Model validation using holdback experiments
- Presenting diminishing returns insights to finance teams
- Building interactive dashboards for spend simulation
Module 7: Attribution of Impact and Incrementality Testing - Measuring true incremental impact of marketing activities
- Designing geo-based lift tests for offline channels
- Time-based holdout testing for digital campaigns
- Using control groups to measure baseline sales
- Calculating net lift and statistical significance
- Integrating test results into MMM coefficient calibration
- Using Bayesian priors informed by A/B tests
- Blending observational and experimental data
- Addressing endogeneity and reverse causality
- Instrumental variable approaches for causal inference
- Regression discontinuity design in marketing contexts
- Difference-in-differences for campaign evaluation
- Using synthetic controls for market comparisons
- Validating MMM against controlled experiments
- Quantifying attribution error in current models
- Confidence scoring for channel impact estimates
- Communicating uncertainty to decision-makers
- Creating attribution heatmaps across channels
- Time-delayed attribution windows
- Multi-touch influence within MMM frameworks
Module 8: Forecasting, Optimisation, and Scenario Planning - Building forward-looking MMM simulations
- Setting assumptions for future spend and market conditions
- Creating multiple budget allocation scenarios
- Defining optimisation objectives: Revenue, margin, ROI
- Linear programming for budget reallocation
- Quadratic programming for non-linear response curves
- Constraint-based optimisation: Minimum spends, caps, rules
- Using Python’s PuLP and SciPy for optimisation
- Monte Carlo simulation for risk-aware forecasting
- Generating confidence intervals for forecasts
- Scenario planning: Best case, worst case, most likely
- Simulating economic downturns or market expansions
- Competitive response modelling in forecasts
- Product launch impact on baseline and incremental demand
- Introducing new channels into the model
- Forecasting halo and cannibalisation effects
- Long-range planning: 6, 12, 18-month forecasts
- Presenting trade-offs between growth and efficiency
- Automating scenario reports for regular review
- Building executive dashboards for ongoing use
Module 9: Cross-Channel Integration and Marketing Ecosystems - Modelling interdependencies between channels
- First and upper-funnel channel synergy effects
- Search and social interaction terms in regression
- TV’s role in boosting digital performance
- Online-to-offline (O2O) attribution modelling
- Geofencing and location-based validation
- CRM and email as retention versus acquisition drivers
- Paid, owned, earned media integration
- Partnership and co-marketing impact assessment
- Influencer marketing as a measurable variable
- Events and experiential marketing proxies
- Pricing and promotion interaction with media
- Distribution and availability as control factors
- Channel clustering: Grouping similar tactics
- Modelling media mix at market, region, and store level
- Granular targeting: DMA, postcode, audience segments
- Local media and affiliate network integration
- Dynamic creative impact on response curves
- Platform-specific algorithms and delivery efficiency
- Full-funnel journey synthesis within MMM
Module 10: Implementation, Governance, and Stakeholder Alignment - Building stakeholder buy-in for MMM adoption
- Identifying key influencers and decision-makers
- Creating tailored communication for finance, marketing, and leadership
- Translating technical outputs into business insights
- Designing executive summaries and one-pagers
- Visual storytelling with charts, heatmaps, and dashboards
- Running internal workshops to socialise findings
- Addressing political resistance to budget reallocation
- Establishing MMM as a governance process, not a one-off
- Setting up quarterly MMM reviews and refresh cycles
- Integrating MMM with annual planning and budget cycles
- Defining roles: Owner, analyst, validator, reviewer
- Data governance and update protocols
- Change log and version tracking for transparency
- Audit readiness and compliance considerations
- Vendor and agency collaboration using MMM outputs
- Negotiating media contracts based on efficiency metrics
- Linking MMM insights to performance KPIs
- Training regional teams on MMM fundamentals
- Scaling MMM across global markets
Module 11: Advanced Topics and Emerging Trends - Privacy-preserving MMM: Models without cookies or PII
- Federated learning for decentralised data environments
- Differential privacy in model training
- Cookieless and IDFA-compliant measurement approaches
- Using aggregated data from Google Ads, Meta, TikTok
- On-device conversion measurement integration
- Contextual and cohort-based targeting in MMM
- Generative AI for scenario narrative creation
- Automated insight generation from model outputs
- Large language models for stakeholder communication drafting
- Automated anomaly detection in marketing performance
- Real-time MMM for agile optimisation
- Streaming data pipelines for live updates
- Edge computing for low-latency model inference
- Integration with CDPs and marketing clouds
- API-driven MMM for workflow automation
- Multi-touch attribution fusion with MMM
- Short-term versus long-term brand building quantification
- Emotional resonance and creative quality metrics
- Future of AI in predictive marketing governance
Module 12: Certification Project and Real-World Application - Step-by-step guide to building your own MMM from scratch
- Selecting a business objective and defining scope
- Creating a data collection and preparation checklist
- Choosing appropriate model architecture
- Implementing adstock and saturation transformations
- Running regression and interpreting coefficients
- Validating model performance with diagnostics
- Generating forecasts and optimisation scenarios
- Designing a board-ready presentation deck
- Writing a clear, actionable executive summary
- Preparing appendices for technical reviewers
- Incorporating real data constraints and assumptions
- Building a defensible, transparent model narrative
- Submission framework for Certificate of Completion
- Instructor feedback pathway on key components
- Iterating based on review comments
- Finalising your professional portfolio piece
- Sharing your model with stakeholders securely
- Planning next steps: Scaling, automation, integration
- Joining The Art of Service alumni network for ongoing support
- Ordinary Least Squares (OLS) regression: When and how to apply it
- Assumptions of linear regression and how to test them
- Detecting and correcting for heteroscedasticity
- Handling autocorrelation in time series marketing data
- Interpreting p-values, R-squared, and adjusted R-squared
- Understanding confidence intervals for coefficient estimates
- Model fit vs. model usefulness: Avoiding overfitting
- Stepwise regression and variable selection strategies
- Lasso and Ridge regression for marketing variable selection
- Introduction to Bayesian statistics for MMM
- Benefits of Bayesian approaches: Incorporating prior knowledge
- Selecting informative and weakly informative priors
- Markov Chain Monte Carlo (MCMC) sampling basics
- Gibbs sampling and Hamiltonian Monte Carlo overview
- Convergence diagnostics: R-hat, trace plots, effective sample size
- Bayesian vs. frequentist MMM: Trade-offs and use cases
- Using hierarchical models to share learning across regions
- Model validation: Walk-forward testing and holdout periods
- Cross-validation techniques for time series data
- Residual analysis: Identifying structural model gaps
Module 4: AI and Machine Learning Integration in MMM - Why AI improves MMM: Speed, non-linearity, and automation
- Types of ML models used in MMM: GLMs, GAMs, boosted trees
- Generalised Additive Models (GAMs) for flexible response curves
- Gradient boosting for handling complex interactions
- XGBoost and LightGBM integration with MMM workflows
- Neural networks for high-dimensional marketing data
- AutoML tools for rapid model iteration and comparison
- Hyperparameter tuning for optimal model performance
- Feature importance analysis using SHAP values
- Partial dependence plots to visualise channel impact
- Using ensembles to combine multiple MMM approaches
- Natural language processing for incorporating media content tone
- Image recognition to assess creative quality in TV and digital
- Time series forecasting models: Prophet and ARIMA hybrids
- Deep learning for offline-online response alignment
- AI-powered gap detection: Identifying missing variables
- Automated adstock and saturation estimation
- Real-time model updating with streaming data
- Transfer learning: Applying models across markets
- Bias detection in AI-driven MMM outputs
Module 5: Model Calibration, Validation, and Diagnostics - Setting up train, validation, and test datasets
- Choosing evaluation metrics: MAPE, RMSE, MAE
- Interpreting model error in business context
- Simulating out-of-sample performance
- Backtesting models against historical budget changes
- Detecting overfitting using holdout performance
- Walk-forward analysis for dynamic validation
- Residual diagnostics: Patterns, trends, and outliers
- Checking for omitted variable bias
- Validating channel coefficient stability over time
- Stress-testing models with extreme scenarios
- Sensitivity analysis: How changes affect outputs
- Confidence bands for forecasted impact
- Bootstrapping to estimate parameter uncertainty
- Bayesian credible intervals vs. frequentist confidence intervals
- Model updating frequency: Quarterly, monthly, or event-triggered
- Version control for model iterations
- Documentation standards for audit and compliance
- User acceptance testing with stakeholders
- Creating model validation reports for leadership
Module 6: Saturation, Diminishing Returns, and Non-Linearity - Understanding the law of diminishing returns in marketing
- Power transformations: Square root and logarithmic
- Michaelis-Menten function for saturation modelling
- Logistic and Hill functions for S-shaped response curves
- Selecting response functions based on channel type
- Estimating saturation thresholds for digital and offline
- Interpreting diminishing returns in spend optimisation
- Using non-linear least squares for curve fitting
- Combining adstock and saturation in one transformation
- Visualising response curves for stakeholder communication
- Channel-specific elasticity: How spend drives marginal gains
- Optimal spend levels: Where marginal cost equals marginal return
- Forecasting impact of doubling or halving spend
- Example: TV versus search versus social media curves
- Calibrating curves using A/B test results
- Incorporating competitive saturation effects
- Dynamic saturation based on market conditions
- Model validation using holdback experiments
- Presenting diminishing returns insights to finance teams
- Building interactive dashboards for spend simulation
Module 7: Attribution of Impact and Incrementality Testing - Measuring true incremental impact of marketing activities
- Designing geo-based lift tests for offline channels
- Time-based holdout testing for digital campaigns
- Using control groups to measure baseline sales
- Calculating net lift and statistical significance
- Integrating test results into MMM coefficient calibration
- Using Bayesian priors informed by A/B tests
- Blending observational and experimental data
- Addressing endogeneity and reverse causality
- Instrumental variable approaches for causal inference
- Regression discontinuity design in marketing contexts
- Difference-in-differences for campaign evaluation
- Using synthetic controls for market comparisons
- Validating MMM against controlled experiments
- Quantifying attribution error in current models
- Confidence scoring for channel impact estimates
- Communicating uncertainty to decision-makers
- Creating attribution heatmaps across channels
- Time-delayed attribution windows
- Multi-touch influence within MMM frameworks
Module 8: Forecasting, Optimisation, and Scenario Planning - Building forward-looking MMM simulations
- Setting assumptions for future spend and market conditions
- Creating multiple budget allocation scenarios
- Defining optimisation objectives: Revenue, margin, ROI
- Linear programming for budget reallocation
- Quadratic programming for non-linear response curves
- Constraint-based optimisation: Minimum spends, caps, rules
- Using Python’s PuLP and SciPy for optimisation
- Monte Carlo simulation for risk-aware forecasting
- Generating confidence intervals for forecasts
- Scenario planning: Best case, worst case, most likely
- Simulating economic downturns or market expansions
- Competitive response modelling in forecasts
- Product launch impact on baseline and incremental demand
- Introducing new channels into the model
- Forecasting halo and cannibalisation effects
- Long-range planning: 6, 12, 18-month forecasts
- Presenting trade-offs between growth and efficiency
- Automating scenario reports for regular review
- Building executive dashboards for ongoing use
Module 9: Cross-Channel Integration and Marketing Ecosystems - Modelling interdependencies between channels
- First and upper-funnel channel synergy effects
- Search and social interaction terms in regression
- TV’s role in boosting digital performance
- Online-to-offline (O2O) attribution modelling
- Geofencing and location-based validation
- CRM and email as retention versus acquisition drivers
- Paid, owned, earned media integration
- Partnership and co-marketing impact assessment
- Influencer marketing as a measurable variable
- Events and experiential marketing proxies
- Pricing and promotion interaction with media
- Distribution and availability as control factors
- Channel clustering: Grouping similar tactics
- Modelling media mix at market, region, and store level
- Granular targeting: DMA, postcode, audience segments
- Local media and affiliate network integration
- Dynamic creative impact on response curves
- Platform-specific algorithms and delivery efficiency
- Full-funnel journey synthesis within MMM
Module 10: Implementation, Governance, and Stakeholder Alignment - Building stakeholder buy-in for MMM adoption
- Identifying key influencers and decision-makers
- Creating tailored communication for finance, marketing, and leadership
- Translating technical outputs into business insights
- Designing executive summaries and one-pagers
- Visual storytelling with charts, heatmaps, and dashboards
- Running internal workshops to socialise findings
- Addressing political resistance to budget reallocation
- Establishing MMM as a governance process, not a one-off
- Setting up quarterly MMM reviews and refresh cycles
- Integrating MMM with annual planning and budget cycles
- Defining roles: Owner, analyst, validator, reviewer
- Data governance and update protocols
- Change log and version tracking for transparency
- Audit readiness and compliance considerations
- Vendor and agency collaboration using MMM outputs
- Negotiating media contracts based on efficiency metrics
- Linking MMM insights to performance KPIs
- Training regional teams on MMM fundamentals
- Scaling MMM across global markets
Module 11: Advanced Topics and Emerging Trends - Privacy-preserving MMM: Models without cookies or PII
- Federated learning for decentralised data environments
- Differential privacy in model training
- Cookieless and IDFA-compliant measurement approaches
- Using aggregated data from Google Ads, Meta, TikTok
- On-device conversion measurement integration
- Contextual and cohort-based targeting in MMM
- Generative AI for scenario narrative creation
- Automated insight generation from model outputs
- Large language models for stakeholder communication drafting
- Automated anomaly detection in marketing performance
- Real-time MMM for agile optimisation
- Streaming data pipelines for live updates
- Edge computing for low-latency model inference
- Integration with CDPs and marketing clouds
- API-driven MMM for workflow automation
- Multi-touch attribution fusion with MMM
- Short-term versus long-term brand building quantification
- Emotional resonance and creative quality metrics
- Future of AI in predictive marketing governance
Module 12: Certification Project and Real-World Application - Step-by-step guide to building your own MMM from scratch
- Selecting a business objective and defining scope
- Creating a data collection and preparation checklist
- Choosing appropriate model architecture
- Implementing adstock and saturation transformations
- Running regression and interpreting coefficients
- Validating model performance with diagnostics
- Generating forecasts and optimisation scenarios
- Designing a board-ready presentation deck
- Writing a clear, actionable executive summary
- Preparing appendices for technical reviewers
- Incorporating real data constraints and assumptions
- Building a defensible, transparent model narrative
- Submission framework for Certificate of Completion
- Instructor feedback pathway on key components
- Iterating based on review comments
- Finalising your professional portfolio piece
- Sharing your model with stakeholders securely
- Planning next steps: Scaling, automation, integration
- Joining The Art of Service alumni network for ongoing support
- Setting up train, validation, and test datasets
- Choosing evaluation metrics: MAPE, RMSE, MAE
- Interpreting model error in business context
- Simulating out-of-sample performance
- Backtesting models against historical budget changes
- Detecting overfitting using holdout performance
- Walk-forward analysis for dynamic validation
- Residual diagnostics: Patterns, trends, and outliers
- Checking for omitted variable bias
- Validating channel coefficient stability over time
- Stress-testing models with extreme scenarios
- Sensitivity analysis: How changes affect outputs
- Confidence bands for forecasted impact
- Bootstrapping to estimate parameter uncertainty
- Bayesian credible intervals vs. frequentist confidence intervals
- Model updating frequency: Quarterly, monthly, or event-triggered
- Version control for model iterations
- Documentation standards for audit and compliance
- User acceptance testing with stakeholders
- Creating model validation reports for leadership
Module 6: Saturation, Diminishing Returns, and Non-Linearity - Understanding the law of diminishing returns in marketing
- Power transformations: Square root and logarithmic
- Michaelis-Menten function for saturation modelling
- Logistic and Hill functions for S-shaped response curves
- Selecting response functions based on channel type
- Estimating saturation thresholds for digital and offline
- Interpreting diminishing returns in spend optimisation
- Using non-linear least squares for curve fitting
- Combining adstock and saturation in one transformation
- Visualising response curves for stakeholder communication
- Channel-specific elasticity: How spend drives marginal gains
- Optimal spend levels: Where marginal cost equals marginal return
- Forecasting impact of doubling or halving spend
- Example: TV versus search versus social media curves
- Calibrating curves using A/B test results
- Incorporating competitive saturation effects
- Dynamic saturation based on market conditions
- Model validation using holdback experiments
- Presenting diminishing returns insights to finance teams
- Building interactive dashboards for spend simulation
Module 7: Attribution of Impact and Incrementality Testing - Measuring true incremental impact of marketing activities
- Designing geo-based lift tests for offline channels
- Time-based holdout testing for digital campaigns
- Using control groups to measure baseline sales
- Calculating net lift and statistical significance
- Integrating test results into MMM coefficient calibration
- Using Bayesian priors informed by A/B tests
- Blending observational and experimental data
- Addressing endogeneity and reverse causality
- Instrumental variable approaches for causal inference
- Regression discontinuity design in marketing contexts
- Difference-in-differences for campaign evaluation
- Using synthetic controls for market comparisons
- Validating MMM against controlled experiments
- Quantifying attribution error in current models
- Confidence scoring for channel impact estimates
- Communicating uncertainty to decision-makers
- Creating attribution heatmaps across channels
- Time-delayed attribution windows
- Multi-touch influence within MMM frameworks
Module 8: Forecasting, Optimisation, and Scenario Planning - Building forward-looking MMM simulations
- Setting assumptions for future spend and market conditions
- Creating multiple budget allocation scenarios
- Defining optimisation objectives: Revenue, margin, ROI
- Linear programming for budget reallocation
- Quadratic programming for non-linear response curves
- Constraint-based optimisation: Minimum spends, caps, rules
- Using Python’s PuLP and SciPy for optimisation
- Monte Carlo simulation for risk-aware forecasting
- Generating confidence intervals for forecasts
- Scenario planning: Best case, worst case, most likely
- Simulating economic downturns or market expansions
- Competitive response modelling in forecasts
- Product launch impact on baseline and incremental demand
- Introducing new channels into the model
- Forecasting halo and cannibalisation effects
- Long-range planning: 6, 12, 18-month forecasts
- Presenting trade-offs between growth and efficiency
- Automating scenario reports for regular review
- Building executive dashboards for ongoing use
Module 9: Cross-Channel Integration and Marketing Ecosystems - Modelling interdependencies between channels
- First and upper-funnel channel synergy effects
- Search and social interaction terms in regression
- TV’s role in boosting digital performance
- Online-to-offline (O2O) attribution modelling
- Geofencing and location-based validation
- CRM and email as retention versus acquisition drivers
- Paid, owned, earned media integration
- Partnership and co-marketing impact assessment
- Influencer marketing as a measurable variable
- Events and experiential marketing proxies
- Pricing and promotion interaction with media
- Distribution and availability as control factors
- Channel clustering: Grouping similar tactics
- Modelling media mix at market, region, and store level
- Granular targeting: DMA, postcode, audience segments
- Local media and affiliate network integration
- Dynamic creative impact on response curves
- Platform-specific algorithms and delivery efficiency
- Full-funnel journey synthesis within MMM
Module 10: Implementation, Governance, and Stakeholder Alignment - Building stakeholder buy-in for MMM adoption
- Identifying key influencers and decision-makers
- Creating tailored communication for finance, marketing, and leadership
- Translating technical outputs into business insights
- Designing executive summaries and one-pagers
- Visual storytelling with charts, heatmaps, and dashboards
- Running internal workshops to socialise findings
- Addressing political resistance to budget reallocation
- Establishing MMM as a governance process, not a one-off
- Setting up quarterly MMM reviews and refresh cycles
- Integrating MMM with annual planning and budget cycles
- Defining roles: Owner, analyst, validator, reviewer
- Data governance and update protocols
- Change log and version tracking for transparency
- Audit readiness and compliance considerations
- Vendor and agency collaboration using MMM outputs
- Negotiating media contracts based on efficiency metrics
- Linking MMM insights to performance KPIs
- Training regional teams on MMM fundamentals
- Scaling MMM across global markets
Module 11: Advanced Topics and Emerging Trends - Privacy-preserving MMM: Models without cookies or PII
- Federated learning for decentralised data environments
- Differential privacy in model training
- Cookieless and IDFA-compliant measurement approaches
- Using aggregated data from Google Ads, Meta, TikTok
- On-device conversion measurement integration
- Contextual and cohort-based targeting in MMM
- Generative AI for scenario narrative creation
- Automated insight generation from model outputs
- Large language models for stakeholder communication drafting
- Automated anomaly detection in marketing performance
- Real-time MMM for agile optimisation
- Streaming data pipelines for live updates
- Edge computing for low-latency model inference
- Integration with CDPs and marketing clouds
- API-driven MMM for workflow automation
- Multi-touch attribution fusion with MMM
- Short-term versus long-term brand building quantification
- Emotional resonance and creative quality metrics
- Future of AI in predictive marketing governance
Module 12: Certification Project and Real-World Application - Step-by-step guide to building your own MMM from scratch
- Selecting a business objective and defining scope
- Creating a data collection and preparation checklist
- Choosing appropriate model architecture
- Implementing adstock and saturation transformations
- Running regression and interpreting coefficients
- Validating model performance with diagnostics
- Generating forecasts and optimisation scenarios
- Designing a board-ready presentation deck
- Writing a clear, actionable executive summary
- Preparing appendices for technical reviewers
- Incorporating real data constraints and assumptions
- Building a defensible, transparent model narrative
- Submission framework for Certificate of Completion
- Instructor feedback pathway on key components
- Iterating based on review comments
- Finalising your professional portfolio piece
- Sharing your model with stakeholders securely
- Planning next steps: Scaling, automation, integration
- Joining The Art of Service alumni network for ongoing support
- Measuring true incremental impact of marketing activities
- Designing geo-based lift tests for offline channels
- Time-based holdout testing for digital campaigns
- Using control groups to measure baseline sales
- Calculating net lift and statistical significance
- Integrating test results into MMM coefficient calibration
- Using Bayesian priors informed by A/B tests
- Blending observational and experimental data
- Addressing endogeneity and reverse causality
- Instrumental variable approaches for causal inference
- Regression discontinuity design in marketing contexts
- Difference-in-differences for campaign evaluation
- Using synthetic controls for market comparisons
- Validating MMM against controlled experiments
- Quantifying attribution error in current models
- Confidence scoring for channel impact estimates
- Communicating uncertainty to decision-makers
- Creating attribution heatmaps across channels
- Time-delayed attribution windows
- Multi-touch influence within MMM frameworks
Module 8: Forecasting, Optimisation, and Scenario Planning - Building forward-looking MMM simulations
- Setting assumptions for future spend and market conditions
- Creating multiple budget allocation scenarios
- Defining optimisation objectives: Revenue, margin, ROI
- Linear programming for budget reallocation
- Quadratic programming for non-linear response curves
- Constraint-based optimisation: Minimum spends, caps, rules
- Using Python’s PuLP and SciPy for optimisation
- Monte Carlo simulation for risk-aware forecasting
- Generating confidence intervals for forecasts
- Scenario planning: Best case, worst case, most likely
- Simulating economic downturns or market expansions
- Competitive response modelling in forecasts
- Product launch impact on baseline and incremental demand
- Introducing new channels into the model
- Forecasting halo and cannibalisation effects
- Long-range planning: 6, 12, 18-month forecasts
- Presenting trade-offs between growth and efficiency
- Automating scenario reports for regular review
- Building executive dashboards for ongoing use
Module 9: Cross-Channel Integration and Marketing Ecosystems - Modelling interdependencies between channels
- First and upper-funnel channel synergy effects
- Search and social interaction terms in regression
- TV’s role in boosting digital performance
- Online-to-offline (O2O) attribution modelling
- Geofencing and location-based validation
- CRM and email as retention versus acquisition drivers
- Paid, owned, earned media integration
- Partnership and co-marketing impact assessment
- Influencer marketing as a measurable variable
- Events and experiential marketing proxies
- Pricing and promotion interaction with media
- Distribution and availability as control factors
- Channel clustering: Grouping similar tactics
- Modelling media mix at market, region, and store level
- Granular targeting: DMA, postcode, audience segments
- Local media and affiliate network integration
- Dynamic creative impact on response curves
- Platform-specific algorithms and delivery efficiency
- Full-funnel journey synthesis within MMM
Module 10: Implementation, Governance, and Stakeholder Alignment - Building stakeholder buy-in for MMM adoption
- Identifying key influencers and decision-makers
- Creating tailored communication for finance, marketing, and leadership
- Translating technical outputs into business insights
- Designing executive summaries and one-pagers
- Visual storytelling with charts, heatmaps, and dashboards
- Running internal workshops to socialise findings
- Addressing political resistance to budget reallocation
- Establishing MMM as a governance process, not a one-off
- Setting up quarterly MMM reviews and refresh cycles
- Integrating MMM with annual planning and budget cycles
- Defining roles: Owner, analyst, validator, reviewer
- Data governance and update protocols
- Change log and version tracking for transparency
- Audit readiness and compliance considerations
- Vendor and agency collaboration using MMM outputs
- Negotiating media contracts based on efficiency metrics
- Linking MMM insights to performance KPIs
- Training regional teams on MMM fundamentals
- Scaling MMM across global markets
Module 11: Advanced Topics and Emerging Trends - Privacy-preserving MMM: Models without cookies or PII
- Federated learning for decentralised data environments
- Differential privacy in model training
- Cookieless and IDFA-compliant measurement approaches
- Using aggregated data from Google Ads, Meta, TikTok
- On-device conversion measurement integration
- Contextual and cohort-based targeting in MMM
- Generative AI for scenario narrative creation
- Automated insight generation from model outputs
- Large language models for stakeholder communication drafting
- Automated anomaly detection in marketing performance
- Real-time MMM for agile optimisation
- Streaming data pipelines for live updates
- Edge computing for low-latency model inference
- Integration with CDPs and marketing clouds
- API-driven MMM for workflow automation
- Multi-touch attribution fusion with MMM
- Short-term versus long-term brand building quantification
- Emotional resonance and creative quality metrics
- Future of AI in predictive marketing governance
Module 12: Certification Project and Real-World Application - Step-by-step guide to building your own MMM from scratch
- Selecting a business objective and defining scope
- Creating a data collection and preparation checklist
- Choosing appropriate model architecture
- Implementing adstock and saturation transformations
- Running regression and interpreting coefficients
- Validating model performance with diagnostics
- Generating forecasts and optimisation scenarios
- Designing a board-ready presentation deck
- Writing a clear, actionable executive summary
- Preparing appendices for technical reviewers
- Incorporating real data constraints and assumptions
- Building a defensible, transparent model narrative
- Submission framework for Certificate of Completion
- Instructor feedback pathway on key components
- Iterating based on review comments
- Finalising your professional portfolio piece
- Sharing your model with stakeholders securely
- Planning next steps: Scaling, automation, integration
- Joining The Art of Service alumni network for ongoing support
- Modelling interdependencies between channels
- First and upper-funnel channel synergy effects
- Search and social interaction terms in regression
- TV’s role in boosting digital performance
- Online-to-offline (O2O) attribution modelling
- Geofencing and location-based validation
- CRM and email as retention versus acquisition drivers
- Paid, owned, earned media integration
- Partnership and co-marketing impact assessment
- Influencer marketing as a measurable variable
- Events and experiential marketing proxies
- Pricing and promotion interaction with media
- Distribution and availability as control factors
- Channel clustering: Grouping similar tactics
- Modelling media mix at market, region, and store level
- Granular targeting: DMA, postcode, audience segments
- Local media and affiliate network integration
- Dynamic creative impact on response curves
- Platform-specific algorithms and delivery efficiency
- Full-funnel journey synthesis within MMM
Module 10: Implementation, Governance, and Stakeholder Alignment - Building stakeholder buy-in for MMM adoption
- Identifying key influencers and decision-makers
- Creating tailored communication for finance, marketing, and leadership
- Translating technical outputs into business insights
- Designing executive summaries and one-pagers
- Visual storytelling with charts, heatmaps, and dashboards
- Running internal workshops to socialise findings
- Addressing political resistance to budget reallocation
- Establishing MMM as a governance process, not a one-off
- Setting up quarterly MMM reviews and refresh cycles
- Integrating MMM with annual planning and budget cycles
- Defining roles: Owner, analyst, validator, reviewer
- Data governance and update protocols
- Change log and version tracking for transparency
- Audit readiness and compliance considerations
- Vendor and agency collaboration using MMM outputs
- Negotiating media contracts based on efficiency metrics
- Linking MMM insights to performance KPIs
- Training regional teams on MMM fundamentals
- Scaling MMM across global markets
Module 11: Advanced Topics and Emerging Trends - Privacy-preserving MMM: Models without cookies or PII
- Federated learning for decentralised data environments
- Differential privacy in model training
- Cookieless and IDFA-compliant measurement approaches
- Using aggregated data from Google Ads, Meta, TikTok
- On-device conversion measurement integration
- Contextual and cohort-based targeting in MMM
- Generative AI for scenario narrative creation
- Automated insight generation from model outputs
- Large language models for stakeholder communication drafting
- Automated anomaly detection in marketing performance
- Real-time MMM for agile optimisation
- Streaming data pipelines for live updates
- Edge computing for low-latency model inference
- Integration with CDPs and marketing clouds
- API-driven MMM for workflow automation
- Multi-touch attribution fusion with MMM
- Short-term versus long-term brand building quantification
- Emotional resonance and creative quality metrics
- Future of AI in predictive marketing governance
Module 12: Certification Project and Real-World Application - Step-by-step guide to building your own MMM from scratch
- Selecting a business objective and defining scope
- Creating a data collection and preparation checklist
- Choosing appropriate model architecture
- Implementing adstock and saturation transformations
- Running regression and interpreting coefficients
- Validating model performance with diagnostics
- Generating forecasts and optimisation scenarios
- Designing a board-ready presentation deck
- Writing a clear, actionable executive summary
- Preparing appendices for technical reviewers
- Incorporating real data constraints and assumptions
- Building a defensible, transparent model narrative
- Submission framework for Certificate of Completion
- Instructor feedback pathway on key components
- Iterating based on review comments
- Finalising your professional portfolio piece
- Sharing your model with stakeholders securely
- Planning next steps: Scaling, automation, integration
- Joining The Art of Service alumni network for ongoing support
- Privacy-preserving MMM: Models without cookies or PII
- Federated learning for decentralised data environments
- Differential privacy in model training
- Cookieless and IDFA-compliant measurement approaches
- Using aggregated data from Google Ads, Meta, TikTok
- On-device conversion measurement integration
- Contextual and cohort-based targeting in MMM
- Generative AI for scenario narrative creation
- Automated insight generation from model outputs
- Large language models for stakeholder communication drafting
- Automated anomaly detection in marketing performance
- Real-time MMM for agile optimisation
- Streaming data pipelines for live updates
- Edge computing for low-latency model inference
- Integration with CDPs and marketing clouds
- API-driven MMM for workflow automation
- Multi-touch attribution fusion with MMM
- Short-term versus long-term brand building quantification
- Emotional resonance and creative quality metrics
- Future of AI in predictive marketing governance