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Scaling Experimentation Into Revenue

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Overview

Over the past several years, I worked within Myer’s centralised Optimisation team, operating as part of a structured experimentation program powered by Dynamic Yield. The program functioned as a growth engine across e-commerce, driving conversion rate uplift, personalisation, and measurable commercial outcomes.

The FY25 Executive Report shows the scale of the program, delivering $42.2m in 12-month projected revenue across 45 tests, exceeding the $38.1m annual target (111% of target achieved)

This wasn’t isolated A/B testing. It was a systematic, commercially accountable optimisation program.

Problem Framing

As the program matured, the challenge shifted.

Early “quick wins” had largely been captured. Future uplift required:

  • Smarter prioritisation

  • Stronger governance

  • More complex experimentation

  • Cross-team alignment

  • Increased personalisation sophistication

At the same time, velocity was impacted by developer resourcing constraints, requiring tighter focus on high-value tests.

The question became:

How do we sustain meaningful revenue uplift while increasing program maturity?

My Role

Within this multi-year program, I operated at the intersection of:

  • UX strategy

  • Hypothesis-driven experimentation

  • Personalisation design

  • Workshop facilitation

  • Governance alignment

  • Cross-functional collaboration

I contributed not only to test design, but to shaping how experimentation was prioritised, structured, and scaled.

The impact extended beyond individual tests, into building a disciplined experimentation culture that balanced commercial growth with user experience integrity.

Phase 1: Foundations (FY22-23)

In FY22-23, the experimentation program was still maturing.

The program target was $11.4m, delivering $10.9m across 7 active months. The focus during this period was not only revenue generation, but building:
 

  • Measurement frameworks

  • Ramp governance

  • Significance standards

  • Code review processes

  • Cross-team enablement
     

The team implemented a defined measurement framework covering primary/secondary metrics, sample size estimation, significance testing, and revenue estimation alignment with Finance.
 

My contribution during this phase included shaping UX-driven hypotheses, contributing to early test ideation, and working within a disciplined ramp framework that prioritised low-risk rollouts.

This phase built the experimentation muscle.

Phase 2: Scaling & Maturity (FY24)

By FY24, the program had moved beyond foundational capability and into scaled commercial impact.

The revised annual revenue target of $48.4m was exceeded, delivering $49.4m in Q3 and $50.7m by Q4.
 

Performance improvements included:

  • 16.7% increase vs FY23 performance 

  • 15.8% reduction in cost per test 

  • ~$27.7m in lost year-1 revenue mitigated by not committing underperforming tests

This is where the program shifted from “running tests” to operating with commercial discipline.
 

Key advancements included:

  • Deep dive journey-based ideation cycles

  • Integration of Contentsquare for faster insight velocity

  • GA4 transition and data integration uplift

  • Dynamic Yield algorithm sophistication for personalisation
     

My role increasingly focused on hypothesis framing, UX-led experimentation, behavioural insight integration, and ensuring test concepts aligned with both commercial and user intent metrics.

Phase 3: Commercial Discipline & Governance (FY25)

FY25 marked a governance inflection point.

45 tests were delivered, generating $42.2m in 12-month projected revenue against a $38.1m target, achieving 111% of annual target.
 

Performance maturity included:

  • 22.2% increase vs FY24 program 

  • 25% reduction in cost per test 

  • ~$23.7m in lost revenue mitigated by not committing poor-performing tests
     

The team implemented refined prioritisation combining customer value, commercial impact, and capability scoring.
 

AI experimentation began with Shopping Muse, testing conversational product discovery. Despite strong engagement among high-intent users, overall adoption remained <1%, and scaling was responsibly halted.

This demonstrated evidence-based decision-making over hype-driven rollout.
 

I contributed through:
 

  • Designing urgency and social proof tests (e.g., PDP countdown timers, Top Rated tags)

  • Personalised homepage category row testing

  • Recommendation placement experimentation

  • Workshop facilitation for ideation alignment

Phase 4: Centre of Excellence Vision (FY26)

By FY26, the ambition expanded beyond optimisation velocity into operating model maturity.

In H1 FY26:
 

  • 14 tests delivered $25.1m in ramped revenue against a $30.2m target 

  • 57% experiment success rate

  • ~$3.7m in lost revenue mitigated
     

The program formally defined a Centre of Excellence vision focused on:
 

  • Standardised rituals

  • Governance checkpoints

  • Centralised validation

  • Shared documentation

  • Upskilling across Digital
     

This marked a transition from centralised execution to hybrid decentralised enablement.

The optimisation function evolved into:
 

A commercial validation layer embedded across the digital value chain.

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Personalisation & AI Integration
 

Across FY24–FY26, the program increasingly leveraged:
 

  • Dynamic Yield ML-driven recommendation engines

  • Mastercard data integration

  • Segment-specific personalisation

  • Journey-based algorithm testing
     

These initiatives focused on:
 

  • Combatting decision fatigue

  • Enhancing inspiration

  • Purposeful friction to improve CVR and AOV 
     

The strategic direction aligned experimentation with Myer’s North Star of $1.5–2B+ online sales.

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Latest Experimentation Themes (FY25–FY26)

As the program matured, experimentation moved beyond incremental UX tweaks and into higher-leverage behavioural and personalisation strategies aligned with Myer’s commercial North Star.

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1. Combatting Decision Fatigue
 

A key pillar of the FY25 strategy focused on reducing cognitive overload across the shopping journey.

This included:
 

  • Surfacing “Top Rated” indicators on PLPs to strengthen social proof 

  • Introducing urgency messaging such as PDP countdown timers 

  • Optimising product recommendation placements to guide faster decision-making
     

These tests demonstrated that behavioural cues, not just layout changes, materially influenced CVR and ATB.

The program increasingly leaned into psychology-informed experimentation.

 

2. Data-Driven Personalisation
 

Significant investment was made in maximising the value of Dynamic Yield and Mastercard-powered recommendation engines.
 

Recent initiatives focused on:
 

  • Personalised homepage category rows

  • Segment-specific algorithm optimisation

  • Testing “Visually Similar” and affinity-based recommendations

  • Search-triggered trending product suggestions
     

The emphasis shifted from generic optimisation to intent-driven personalisation, delivering the right products at the right time based on behavioural signals.

 

3. AI Exploration & Responsible Scaling

The team piloted Shopping Muse, a generative AI conversational shopping assistant.
 

While engaged users showed higher CVR and AOV, overall adoption remained below 1%, and commercial impact was neutral to slightly negative.
 

Rather than scaling prematurely, the recommendation was to reassess placement, value proposition, and integration into natural behaviours before further investment.
 

This reinforced a key principle of the program:

Evidence over trend-driven rollout.

 

4. Governance & CoE Maturity
 

In FY26, the experimentation function formally articulated a Centre of Excellence vision.
 

The focus expanded beyond running tests to:
 

  • Standardising rituals and documentation

  • Centralising validation checkpoints

  • Reducing test conflict through scheduling

  • Upskilling broader digital teams

  • Supporting decentralised execution with governance oversight
     

This marked a transition from centralised execution to scalable experimentation enablement.

Aggregate Impact (FY22–FY26)

Over four years, the experimentation program:
 

• Delivered well over $100m in projected incremental revenue
• Consistently exceeded revised revenue targets
• Reduced cost per test as maturity increased
• Mitigated tens of millions in negative revenue impact
• Increased experiment complexity and sophistication
• Embedded personalisation as a core commercial lever
• Transitioned toward a Centre of Excellence operating model
 

This wasn’t isolated optimisation work. It was building and maturing a revenue-driving system

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