About the Course
Organizations invest in experimentation because they want results they can prove in conversion optimization, product design, and digital journey improvement. To do that well, you need to demonstrate hypothesis design, random assignment, statistical power planning, guardrail metric selection, analysis of variance in results, and disciplined decision logging, all of which align closely with the logic of controlled testing and evidence-based change. In practice, A/B testing and experimentation design sits at the intersection of user behavior analysis, measurement integrity, and business decisioning, so weak test design creates expensive ambiguity instead of insight.
This course turns scattered experimentation habits into a repeatable system grounded in valid hypotheses, sample size calculation, experiment design documentation, control and treatment setup, and result interpretation. You will practice building experiment briefs, metric trees, power estimates, and post-test decision summaries, while being introduced to multivariate testing, sequential testing cautions, and experimentation governance patterns at a practical overview level. This course teaches you how to design valid A/B tests through hypothesis framing, sample sizing, and result interpretation so you can make decisions with confidence. It also shows you how to document experiments clearly enough for product, marketing, analytics, and leadership review.
Many teams face budget limits, traffic constraints, overlapping releases, and incomplete event tracking, which makes experimentation harder than the theory suggests. This training is built for professionals who must deliver reliable A/B testing outcomes under real-world pressure, where the cost of a bad decision is lost revenue, wasted development time, or misleading stakeholder confidence.
Target Audience
This course is designed for professionals who plan, run, measure, or govern controlled experiments across digital products, marketing funnels, and customer journeys.
- Product Managers responsible for prioritizing and validating feature experiments
- Growth Marketing Managers running landing page and conversion tests
- Data Analysts calculating power, significance, and result reliability
- UX Researchers testing interface changes and behavior hypotheses
- Experimentation Specialists managing test calendars and variant governance
- Digital Product Owners aligning experiments with roadmap decisions
- Conversion Rate Optimization Specialists improving funnel performance through structured testing
- Analytics Managers reviewing experiment integrity and stakeholder readouts
- Customer Insight Managers translating test findings into journey changes
- Marketing Operations Leads coordinating tags, tracking, and test execution
Course Objectives
This course equips you to plan, execute, and measure A/B testing initiatives that improve decision quality, protect statistical validity, and support confident rollout decisions.
- Assess current experimentation maturity using a test governance checklist, metric tree, and event-tracking audit.
- Apply hypothesis-driven experiment design to define control, variant, success metrics, and guardrail metrics.
- Design sample size and statistical power plans using effect size, traffic estimates, and confidence thresholds.
- Build experiment briefs and decision logs that document randomization, duration, and analysis rules.
- Calculate test duration and sample requirements for traffic-constrained A/B tests using spreadsheet-based planning.
- Evaluate results against confidence intervals, false-positive risk, and pre-defined stopping rules.
- Navigate product, marketing, and analytics approval paths for overlapping experiments and release constraints.
- Synthesize findings into stakeholder-ready experiment readouts, rollout recommendations, and post-test action plans.
Requirements & Prerequisites
Prerequisites: Working familiarity with digital product metrics such as conversion rate, CTR, retention, or activation; basic comfort reading dashboards and spreadsheets; no coding required for completion. Familiarity with hypothesis testing, Google Analytics 4, Optimizely, VWO, or similar experimentation tools is helpful but not required. Participants should bring a laptop for hands-on sample size calculations, metric mapping, and experiment planning exercises. Advanced statistical methods are introduced at an operational level, not as programming or engineering implementation.
Local Application and Business Return
How participants can apply the training in local operating conditions, and the return their organisation can plan for.
How participants apply this
Expected ROI
Training Methodology
This is a practical, outcome-driven course designed to turn experimentation aspiration into measurable action and credible reporting.
Methodology includes:
- Hands-on sample size calculation using traffic, effect size, and power worksheets.
- Scenario simulation for a low-traffic landing page test under release constraints.
- Diagnostic review of an experiment plan against a randomization and bias checklist.
- Stakeholder mapping for product, analytics, and marketing approval of test decisions.
- Case analysis from e-commerce, SaaS, media, and financial services experimentation patterns.
- Workshop to build a complete experiment brief and decision log template.
- Reflection exercise comparing current test habits against power, validity, and governance benchmarks.
Upcoming Sessions
Next available dates worldwide
Certification
Recognized credentials that advance your career
Participants who complete the A/B Testing and Experimentation Design Training Program earn a Trainingcred Certificate of Achievement, demonstrating professional competence and alignment with global standards in learning and development.
NITA Accredited
Accredited by the National Industrial Training Authority, ensuring programs meet nationally recognized standards of quality and relevance.
CPD Certified
Recognized by the CPD Certification Service, ensuring every program meets internationally benchmarked standards of professional excellence.
Why this course earns its place on your CV
Accredited training, practitioner trainers, and peers on the same career track — the three things real expertise is built on.
Effective Learning & Skill Development
- Build expertise with structured, outcome-driven learning.
- Equip individuals and teams with skills that grow with industry needs.
- Reinforce learning through real-world scenarios, case studies and practical exercises.
Career Growth & Professional Advancement
- Apply what you learn with a proven methodology that ensures lasting impact.
- Develop immediately usable skills that translate directly into workplace success.
- Gain the expertise needed for career advancement and leadership roles.
Training Optimization & Learning Excellence
- Tailor training to industry-specific challenges and organizational goals.
- Use data-driven insights and automation to enhance training effectiveness.
- Evaluate progress and ensure long-term learning success.
Tools and platforms relevant to this field
Examples Australia teams may encounter, and that may be featured in training where they support the confirmed course scope.
These are field-relevant examples, not a promise that every tool will be covered. Exact coverage depends on the confirmed course scope, participant needs, and delivery format.
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Adobe Target AdobeUsed to set up and run controlled experiments on web and app experiences, with targeting and testing workflows for conversion optimisation.
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Optimizely Experimentation OptimizelyUsed by product and growth teams to randomise variants, measure outcomes, and manage experimentation programs at scale.
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VWO Testing WingifyUsed to run A/B tests on landing pages and funnels, helping teams compare variants against conversion goals.
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GrowthBook GrowthBookUsed by product and engineering teams to organise experiments, track results, and support decision-making from controlled tests.
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Mixpanel MixpanelUsed to instrument product behaviour and analyse whether experiment variants change activation, retention, or engagement.
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Power BI MicrosoftUsed to build experiment readouts and share results with stakeholders in a format that is easy to interpret.
