Marketing Experimentation and A/B Testing Training Course

Participants can turn marketing assumptions into measurable hypotheses with decision-ready success criteria.

• Falsifiable hypotheses written as If–Then–Because statements with measurable outcomes
• Conversion funnels mapped from acquisition source through post-purchase behaviour
• Primary, secondary and guardrail metrics selected before data collection begins
• Operational definitions specify eligible users, events, windows and attribution rules
• Proxy metrics assessed against longer-term customer and commercial outcomes
• Metric trees connect page changes to margin, retention and service demand
• Counter-metrics identify potential harm from local conversion gains

Participants can select a suitable experimental design for common marketing interventions and constraints.

• Randomised controlled trial design separates treatment effects from background variation
• Unit of randomisation chosen: user, session, account, store or region
• Treatment, control and holdout groups defined for marketing interventions
• Parallel, crossover and switchback designs matched to operational constraints
• Factorial designs test interactions among offer, message and channel variables
• Cluster randomisation addresses contamination in branches, teams and households
• Interference and spillover risks documented before launching shared-market tests

Participants can plan test duration and sample requirements while managing false-positive and false-negative risk.

• Baseline conversion rates and minimum detectable effects set planning assumptions
• Power analysis calculates sample size, duration and detectable business value
• Type I and Type II errors interpreted for marketing decisions
• Confidence intervals communicate plausible effect sizes beyond binary significance
• Two-proportion tests applied to conversion, click-through and completion rates
• Continuous outcomes analysed using means, variance and practical significance
• Unequal traffic allocation evaluated against risk, cost and learning speed

Participants can specify reliable measurement requirements and identify data issues that invalidate experiments.

• Measurement plans map hypotheses to events, properties, audiences and owners
• Event taxonomies use consistent names, parameters and version-control rules
• Google Analytics 4 events validated against source systems and consent settings
• Tag implementation checks prevent duplicate firing, missing values and misattribution
• Data quality audits test completeness, timeliness, uniqueness and reconciliation
• Identity resolution limits recognised across devices, channels and anonymous sessions
• Privacy-by-design controls address consent, retention and access permissions

Participants can launch and monitor experiments with controls for allocation, implementation and operational incidents.

• Traffic allocation rules balance learning speed, exposure risk and operational capacity
• Randomisation checks compare pre-treatment characteristics between assigned groups
• Sample ratio mismatch detection identifies allocation and implementation faults
• Experiment launch checklists cover targeting, creative, tracking and rollback procedures
• Monitoring dashboards distinguish technical failures from normal statistical volatility
• Stopping rules prevent peeking, premature calls and uncontrolled extensions
• Incident logs record changes, outages and deviations affecting interpretation

Participants can analyse experiment results and make proportionate decisions based on uncertainty and business value.

• Intention-to-treat analysis preserves validity despite noncompliance and crossovers
• Frequentist results reported with effect size, interval, p-value and sample
• Bayesian posterior probabilities interpreted for decision thresholds and expected loss
• CUPED variance reduction improves sensitivity using pre-experiment behavioural data
• Sequential testing methods control false positives during repeated monitoring
• Multiple-comparison corrections manage familywise error across variants and metrics
• Practical significance assessed against margin, capacity and customer-experience thresholds

Participants can evaluate audience-level effects without mistaking random subgroup variation for meaningful findings.

• Segment analyses pre-specify audiences, hypotheses and minimum sample requirements
• Treatment-effect heterogeneity distinguished from noisy subgroup differences
• Interaction terms test whether effects differ by channel, device or tenure
• Personalisation tests manage targeting eligibility, suppression and message frequency
• Uplift modelling compared with randomised tests for incremental response estimates
• Geo-experiments measure media incrementality where user-level randomisation is unavailable
• Segment results translated into targeting rules without overfitting

Participants can adapt experimental methods to channel-specific marketing, commercial and customer experience decisions.

• Landing-page experiments isolate value proposition, proof, friction and call-to-action changes
• Email tests separate subject line, send time, content and offer effects
• Paid-media incrementality tested through holdouts, geo-lifts and matched markets
• Pricing and promotion tests account for cannibalisation, stock and margin
• Onboarding experiments measure activation, repeat use and support contacts
• CRM journey tests coordinate channel sequencing, suppression and attribution windows
• Marketplace and public-service cases address trust, fairness and access outcomes

Participants can communicate test findings, retain organisational learning and plan a controlled experimentation programme.

• Experiment readouts state decision, design, results, uncertainty and recommended action
• Standard scorecards compare observed impact against pre-registered success criteria
• Result narratives separate evidence, interpretation, limitations and next tests
• Knowledge repositories capture hypotheses, variants, audiences, findings and reusable assets
• Portfolio reviews stop low-value tests and fund high-information opportunities
• Scaling plans define roles, tooling, governance and quarterly learning targets
• Personal application plans specify a first test, stakeholders and approval route