Machine Learning Applications in Marketing Training Course

Participants can assess whether customer and campaign data is fit for a defined modelling use case.

• Customer identity resolution using deterministic keys and probabilistic matching rules
• CDP, CRM, web analytics and transaction tables profiled for analytical fitness
• Grain, keys and time windows defined before joining customer data
• Feature availability checked against the intended scoring timestamp
• Missingness patterns separated from true zeroes and absent events
• Data lineage records source, transformation, owner and refresh frequency

Participants can specify repeatable datasets for marketing model development and scoring.

• SQL joins combine customer, product, interaction and campaign response records
• Window functions calculate recency, frequency and rolling behavioural measures
• Cohort definitions preserve consistent observation and outcome periods
• Target labels constructed from measurable customer actions and business rules
• Deduplication rules prevent repeated customers distorting model training samples
• Training datasets partitioned by time rather than random customer selection

Participants can identify data quality, privacy and consent controls required for marketing models.

• Completeness, accuracy, timeliness and consistency checks applied to customer attributes
• Consent status incorporated into audience eligibility and scoring-data specifications
• Purpose limitation assessed before combining behavioural and transactional data
• Sensitive attributes identified for restricted use and fairness assessment
• Retention schedules align analytical datasets with approved business purposes
• Privacy impact assessment questions incorporated into model intake documentation

Participants can investigate customer behaviour and select segmentation methods appropriate to the decision.

• Distribution analysis identifies skew, outliers and sparse customer behaviours
• RFM analysis establishes a transparent baseline for customer prioritisation
• K-means clustering groups customers using scaled behavioural feature sets
• Cluster quality assessed through silhouette scores, stability and commercial interpretability
• Personas derived from segment profiles without treating clusters as causal facts
• Segment migration analysis tracks movement between behavioural customer states

Participants can create time-valid behavioural, transactional and campaign features for predictive modelling.

• Recency, frequency and monetary features derived from dated transaction histories
• Customer tenure and lifecycle stage encoded for retention models
• Engagement features capture opens, clicks, sessions and content interactions
• Campaign exposure variables distinguish delivered, viewed and acted-on communications
• Lagged features prevent future information entering historical model predictions
• Feature dictionary documents definition, source, refresh logic and permitted use

Participants can design and assess propensity models for conversion and customer acquisition decisions.

• Binary classification predicts conversion, purchase or lead-qualification outcomes
• Logistic regression establishes interpretable conversion-propensity baseline models
• Decision trees identify interaction patterns across customer and campaign features
• Gradient boosting models compare nonlinear accuracy against operational interpretability
• Propensity scores translated into priority bands and contact-volume thresholds
• Precision at k measures audience quality under fixed campaign capacity

Participants can specify a churn model and convert risk scores into retention actions.

• Churn definitions distinguish cancellation, inactivity, downgrade and reduced purchasing
• Observation windows separate early warning signals from confirmed churn outcomes
• Survival analysis estimates time-to-churn rather than binary risk alone
• Hazard rates reveal periods of heightened attrition risk
• Retention priority combines churn probability, customer value and intervention cost
• Save offers tested against natural retention and customer-treatment constraints

Participants can select customer lifetime value methods for acquisition, retention and investment decisions.

• Historical customer value separates realised margin from forecast future value
• Cohort-based lifetime value models estimate repeat purchasing patterns
• BG/NBD models estimate transaction frequency for noncontractual customer relationships
• Gamma-Gamma models estimate expected monetary value from purchase histories
• Discount rates and contribution margins incorporated into lifetime value calculations
• Acquisition bids constrained by predicted value, payback period and risk

Participants can design recommendation and next-best-action approaches for relevant customer interventions.

• Collaborative filtering recommends items from similar customer interaction patterns
• Content-based filtering uses product attributes and customer preference signals
• Association rules identify products frequently purchased within shared baskets
• Cold-start strategies address new customers, products and sparse histories
• Next-best-action policies combine eligibility, propensity, value and contact constraints
• Recommendation quality assessed through precision, diversity, novelty and coverage

Participants can distinguish likely responders from customers whose behaviour is changed by treatment.

• Response models identify likely buyers regardless of campaign treatment assignment
• Uplift models estimate incremental response caused by a specific intervention
• Treatment and control groups provide evidence for causal campaign effects
• Four uplift segments distinguish persuadables, sure things, lost causes and sleeping dogs
• Qini curves compare incremental gains across ranked campaign audiences
• Contact policies exclude customers likely to purchase without intervention

Participants can develop forecasting specifications for demand, sales and campaign-volume planning.

• Time-series decomposition separates trend, seasonality, cycles and irregular variation
• Moving averages provide transparent baselines for short-term demand forecasts
• ARIMA models represent autocorrelation and differenced stationary series
• Exogenous variables incorporate price, promotions, holidays and media activity
• Forecast accuracy measured using MAE, RMSE, MAPE and bias
• Prediction intervals communicate uncertainty for inventory and campaign capacity planning

Participants can assess media-response modelling methods and their decision limitations.

• Marketing mix models estimate channel contributions from aggregated time-series data
• Adstock transformations represent delayed and decaying media effects
• Saturation curves model diminishing returns from increasing channel expenditure
• Channel coefficients interpreted cautiously under correlated media investment patterns
• Incrementality tests validate media-response assumptions through controlled comparisons
• Budget scenarios compare expected marginal returns across eligible media channels

Participants can design marketing experiments that provide credible evidence of incremental effects.

• Randomised controlled trials establish counterfactual evidence for marketing treatments
• Hypotheses specify audience, intervention, outcome metric and decision threshold
• Power calculations estimate sample sizes for detectable campaign effects
• Randomisation checks confirm balance across treatment and control populations
• Holdout groups measure incremental value beyond predictive model performance
• Sequential testing controls false discoveries during repeated campaign monitoring

Participants can evaluate model performance and set score thresholds based on business economics.

• Confusion matrices expose trade-offs between true positives and false positives
• ROC-AUC measures ranking discrimination across possible classification thresholds
• Precision-recall curves assess rare-event models with imbalanced customer outcomes
• Calibration plots compare predicted probabilities with observed event rates
• Profit curves incorporate contact cost, conversion value and treatment expense
• Thresholds selected according to capacity, risk appetite and customer experience

Participants can explain marketing model outputs and identify fairness and treatment risks.

• Global feature importance identifies variables influencing aggregate model predictions
• SHAP values explain individual predictions using additive feature contributions
• Partial dependence plots examine marginal relationships while noting correlation limits
• Fairness metrics compare selection, error and outcome rates between groups
• Proxy variables assessed for indirect discrimination and sensitive attribute inference
• Adverse customer outcomes documented with mitigation and escalation procedures

Participants can design an operational path from model scores to controlled marketing actions.

• Batch scoring schedules align predictions with campaign planning and execution cycles
• Real-time scoring requirements assessed for trigger-based customer interactions
• Audience rules combine model score, consent, exclusions and frequency caps
• APIs, CRM platforms and decision engines mapped to activation workflows
• Champion-challenger deployment compares incumbent rules against new model approaches
• Fallback rules protect campaigns when data feeds or scoring services fail

Participants can establish monitoring and governance controls for marketing machine learning models.

• Performance dashboards track discrimination, calibration, uplift and business value
• Data drift detects shifts in incoming customer and campaign characteristics
• Concept drift identifies changing relationships between features and customer outcomes
• Model documentation records purpose, data, assumptions, limitations and approvals
• Retraining triggers define review points after performance or data changes
• Governance forums assign accountability across marketing, analytics, risk and technology

Participants can create a practical implementation roadmap for a prioritised marketing model.

• Use-case prioritisation scores commercial value, feasibility, risk and data readiness
• Stakeholder map identifies sponsors, decision owners, builders and control functions
• Delivery backlog sequences data, modelling, activation and measurement requirements
• Model acceptance criteria define minimum analytical and operational performance
• Operating model assigns responsibilities for campaigns, controls and model maintenance
• Benefits tracking links model adoption to incremental commercial outcomes

Participants can build, review and present a complete machine learning marketing use-case package for a defined commercial decision.

• Decision brief states target customers, intervention, value hypothesis and success measures
• Data specification defines sources, grain, time windows and consent controls
• Feature plan documents behavioural signals, exclusions and leakage safeguards
• Model approach selects baseline, evaluation metrics and threshold economics
• Activation design translates scores into audiences, offers and treatment rules
• Governance checklist records fairness, monitoring, approval and retraining requirements