Opening Keynote: Seven MarTech Trends for 2026, and How to Navigate Them with Jonathan Goh (Medibank)

MarTech Trends 2025-2026: Human-Centered Approach

• Core thesis: Competitive advantage shifts from technology to human translation

• Same platforms/systems available to all organisations

• Differentiation comes from making machines understand humans and humans trust machines

• Need humans in the loop to avoid losing marketing essence

• Key emerging technologies mentioned but not focus:

• Agentic AI; automated campaigns, hyper-personalisation

• First-party data, privacy-centric architecture

• Composable unified stacks, intelligent experience design

• “Complexification” concept (Oliver Spaulding): Humans respond to complexity by creating more complexity

• Organisations becoming unnecessarily complicated

• Challenge: Navigate through vendor noise where everyone claims same capabilities

Five Critical Martech Trends

• Shared MarTech Ownership

• Move beyond marketing vs IT vs data ownership debates

• Marketing owns the story/customer relationship, Data governs integrity, Technology enables execution

• Champion cross-functional teams with shared KPIs

• Privacy as shared leadership responsibility

• Marketing to Humans vs Machines

• Current obsession: Geotargeting, A/B testing, optimisation metrics

• Problem: Becoming fluent in platform language but losing emotional connection

• Solution: Focus on translation and empathy over targeting precision

• Redefine success beyond clicks - measure sentiment, resonance, retention

• Data with Dignity

• Customers fear misrepresentation, not data collection

• Data as conversation entry point, not commodity

• Design for reciprocity, not extraction

• Privacy as experience, not just compliance

• Intelligence without empathy = manipulation

• Systems vs Stacks

• 40 platforms in 2015 = advanced; 40 platforms in 2025 = 40 failure points

• Focus on orchestration and streamlining, not accumulation

• Build systems of trust where creativity, data, experience flow seamlessly

• Architect for adaptability, not accumulation

• Leadership Debt

• Invisible drag on transformation like technical debt

• Accumulated consequence of neglected leadership responsibilities

• AI amplifies culture - scales both good and bad

• Assessment checklist: Clarity, connection, capability, courage

• Gap between speed of change and speed of trust defines success

Key Takeaways

• Humanise before optimise - next horizon is augmentation, not automation

• Trust as new infrastructure - data/systems/teams only work when people believe

• Leadership as ultimate technology booster

• Future MarTech leaders defined by character, not code

• Brands winning with most human systems, not biggest stacks

• Technology only as human as people who build it

Partner Keynote: From Data to Delight – How Metcash Evolve their Customer Experience with Braze

Metcash Business Model & Challenges

• Australia’s largest wholesale/distribution company supporting 6,500 independent retailers

• Brands include IGA, Mitre 10, Total Tools, Celebrations, Bottle-O

• Complex tech environment: 58 different POS systems with inconsistent data access

• Not real-time data initially - “maybe tomorrow, maybe not”

• Started digital transformation 2018 with basic IGA website

• Had to build retailer trust for consumer-facing communications

• Acting as middleware between retailers and their shoppers

IGA Rewards Loyalty Program Evolution

• Launched 2018 with 1 store, now 1,100+ stores (adding 20-50 weekly)

• Pivotal 2021 decision: cash-back model instead of points

• Direct cash value eliminates confusion about point worth

• Bypasses POS system limitations across fragmented retailer network

• Uses payment rails rather than POS integration

• Current program features:

• 150 live personalised offers available now

• Flat dollar amounts perform better than percentages

• Virtualised Visa card for spending cash-back (Apple/Google Pay integration)

Technology Stack & Braze Implementation

• Partnership began during COVID when forced to rapidly deploy e-commerce

• Current architecture:

• Braze: Communication layer for push notifications, personalisation

• AWS: Machine learning models and data processing

• TalentOne: Promotion engine for cash-back mechanics

• Segment: Customer data platform

• Real-time campaign capabilities demonstrated with $100k giveaway event

• Shoppers scan loyalty card at till, instant winner notifications

• Some winners received $4k, minimum $100

AI Strategy & Marketing Philosophy

• Heavy AI experimentation but cautious about over-reliance

• Risk of “bland and generic” content when fully automated

• “Fancy autocomplete” - doesn’t understand human behaviour yet

• Always maintain “human in the loop” for psychological triggers

• Practical AI applications:

• Contextual campaigns (weather-triggered offers, e.g., 100% cash-back on ice cream when >36°C)

• Query optimisation and data visualisation

• Copy generation with human oversight for brand voice

• Data philosophy: only collect what provides real shopper value

• Location sharing enables personalised, contextual experiences

• Transparency about data use builds trust

Partner Breakout: Purpose & Progress – Breast Cancer Network Australia Driving Change

Rachel’s Career Journey & Purpose Philosophy

• 6.5 years at Movember setting up systems, marketing, fundraising practices

• Worked closely with Raj building organisational sophistication

• Left for career break, then joined Breast Cancer Network Australia (BCNA)

• Purpose definition: “Don’t search for purpose - purpose is something you do”

• About being deliberate, aware, intentional in how you live

• Gives agency and momentum toward meaningful work

• Core driver: Community and connection

• Bringing people together around meaningful causes

• Building movements that create change

• Helping people work on something bigger than themselves

Brand Transformation at BCNA

• Moving from traditional “pink lady” positioning to challenger brand approach

• Historical fierceness in advocacy work not reflected in current brand

• Need to show up as both caring/empathetic AND fierce advocate for change

• New early detection focus campaign

• “Know your normal” concept - daily face mirror habit applied to breast awareness

• Breast booths activation at Bondi with educational video

• Departure from sea-of-pink approach during Breast Cancer Awareness Month

• Brand transformation project underway

• 26-year-old organisation needs relevance for modern women

• Cultural/societal expectations have changed significantly

• Targeting spectrum from 50-60s (different mindset than 20 years ago) to younger demographics

Technology & Partnership Strategy

• Focus on maximising existing tech rather than acquiring new tools

• Current Salesforce transformation not fully utilised

• 3-year implementation program lacked proper adoption/training

• Staff confidence in technology/data often the real barrier

• Partnership approach: Genuine collaboration vs. outsourcing accountability

• External capability must build internal muscle simultaneously

• Partner relationship quality crucial for success

• Example: Sean working 1 day/week helping team with reporting sophistication and data insights

• Hiring challenges in not-for-profit sector

• Lower salaries but strong purpose/values alignment

• Looking for attitude over just skills/experience

• Need capability (leadership attributes, critical thinking) vs. just competencies (hard skills)

• AI will be game-changer for health sector efficiency and impact

• Current team dynamics: High empathy/caring (superpower with shadow side)

• Emotional attachment can cloud judgment on resource allocation

• Difficulty letting go of ineffective initiatives due to emotional connection

Panel Discussion: Ownership in MarTech – Who Should, and Who Does, Own What in the Digital Marketing Ecosystem?

MarTech Ownership Models

• Poll results: 28% marketing-owned, 58% hybrid model currently

• 70% prefer hybrid model (10% increase from current state)

• Only 6% want pure tech ownership

• Hybrid model benefits across financial services and retail

• Single accountability for regulated environments (ASIC compliance)

• Value prioritisation closer to business impact

• Joint partnership enables better feature development

Organisational Structure & Roles

• Key players beyond marketing/tech:

• Data teams (150-200 specialists at university level)

• Legal/privacy teams for ethical oversight

• Risk & compliance for regulatory guardrails

• Vendors as strategic partners (not just suppliers)

• Role delineation in hybrid models:

• Marketing: Customer-facing experience, investment decisions, recency rules

• Tech: Architecture, integrations, modular platform design

• Data: Foundation layer, often split between marketing and IT teams

• Formula One analogy: Marketing as drivers, tech as car builders, data as fuel providers

Capability Building & Team Development

• T-shaped marketers concept: Deep expertise + broad cross-functional knowledge

• Key hiring attributes: Curiosity and resilience over specific technical skills

• Learning approaches:

• Community of practice models (email, comms, data specialists)

• Bottom-up grassroots champions

• Top-down organisational change programs

• Just-in-time learning vs traditional training sessions

• Growth mindset programs for organisational change adaptation

Managing Complexity & Partnership

• University example: 1200+ technologies requiring integration awareness

• Partnership vs enabler mentality shift needed

• Communication strategies:

• Focus on business impact, not technical features

• Clear OKRs and shared accountability

• Regular cross-functional conversations

• Complexity management through automation/AI creates both simplification and new complexities

• Valve gaming company model: No hierarchy, collective ownership culture

Partner Breakout: Beyond Insurance – How Bupa Is Redefining Personalisation in Healthcare

AI-Powered Personalisation Platform Overview

• Modern customer expectations mirror Netflix/Amazon experience standards

• Real-time personalisation vs traditional batch processing

• Competition extends beyond industry boundaries (Apple offering credit cards, Amazon running supermarkets)

• Four core pillars for next-best-action engines:

• Data analytics foundation

• Real-time decisioning capability

• Experience layer integration

• Channel connectivity

• Formula 1 analogy: Processing millions of data points per second for instant decisions

Bupa’s Customer Engagement Transformation

• Bold ambition: World’s most customer-centric healthcare company

• Challenge: 4.5 million customers requiring personalised healthcare at scale

• Partnership with Pega spanning 5 years

• Current integration scope:

• 7 channels integrated (more planned)

• 15+ multi-touchpoint journeys always active

• 150+ always-on propositions

Implementation Results & Metrics

• Contact details update campaign: 30% increase in customer reach

• Dental health journey (18+ months live):

• 375% increase in audience reach

• 700% increase in annual touch-points

• 2x uplift in digital engagement and dental service utilisation

• Focus on health outcomes, not just KPI achievement

Technical & Operational Considerations

• Team structure: ~50 people across multiple squads

• Real-time vs batch processing decisions based on customer value framework

• Real-time: In-clinic communications for immediate value clarity

• Delayed: Less time-sensitive engagements

• Cost-benefit analysis drives timing decisions

Strategic Direction

• Shift from reactive to preventative healthcare model

• Advanced analytics for risk factor identification

• Expanding integrated care ecosystem (dental, optical, hearing, mental health)

• Connecting care across Bupa’s service portfolio

Presentation: Progress Over Perfection – How Data Strategy Enabled Precision Personalisation at Domino’s

Domino’s Global Personalisation Strategy Overview

• Scaling personalisation across 12 markets, multiple languages, different operating models

• Started with platform migration, global templates, foundational journeys

• Created global framework allowing local market customisation

• Challenge: Moving from system alignment to actual personalisation implementation

Customer 360 Initiative Development

• Collection of initiatives enhancing personalisation/segmentation capability

• Profiling, attribute building, audience building, activation, reporting

• Attribute audit and cleanup

• Retired thousands of unused attributes across markets

• Manual process, no AI tools available

• Saved 2000 attribute calculations, reducing costs and processing time

• Collaboration between data team and local markets

Technical Solutions and Workarounds

• Hybrid approach using existing tools + fast-activating partners

• Weather-based content personalisation (rain drives pizza orders)

• Time-based menu personalisation (lunch vs dinner)

• Loyalty stamp card from failed progressive campaign

• Next best action and cross-sell during ordering

• Audience builder prototype

• Built from BI/Snowflake recording layer

• Reduced campaign setup from 8 hours to 20-30 minutes

• Added query template module for flexible reporting

• System role clarification: Real-time activities stay in campaign tools, complex personalisation moves to data tools

AI Implementation (Current Phase)

• Starting small with specific use cases

• Customer name cleanup for legacy/multilingual data

• Creative language filtering (top local team concern)

• Taste profile completion from recipe ingredients

• Individual customer next best action (vs previous segment-based)

• Seasonal/local categorisation beyond codes/bundles

• Human oversight maintained for all AI outputs

• Copy generation not adopted across languages due to context/consistency concerns

Key Takeaways and Audience Q&A

• Three main principles:

• Focus on controllables, tackle “one pizza at a time”

• Build for scale, enable localisation - global recipe with local seasoning

• Progress over perfection as design principle, not just mindset

• Technical stack: Everyone on shared Cortex platform with local additions

• Data/CRM collaboration essential for 360 success

• VWO tool enabled quick on-site personalisation experiments

• Order history primary signal for next best actions, enhanced with other touch-points

Closing Presentation: Customer Decisioning – The Brain of Modern MarTech

Marketing Technology Complexity Crisis

• 95% of Enterprise AI projects fail to deliver measurable ROI (MIT report)

• Marketing utilisation dropped 33% while investment increased 356%

• No Fortune 500 marketing leader can explain how investment leads to ROI (McKinsey)

• Problem: Too much data/technology creating integration challenges rather than smarter marketing

• Core issue: Organisations have forgotten how to decide what matters for each customer

AI Limitations in Customer Decisioning

• AI alone cannot solve customer decisioning challenges

• Current AI issues:

• Confident but frequently wrong

• Inconsistent answers to same questions

• Confirmation bias - will agree if argued with enough

• Legal/compliance teams cannot accept error rates

• Decisioning like iceberg:

• Visible: AI models and outputs (what everyone focuses on)

• Hidden: Governance, human judgment, rules, policies, constraints

• Over-investing in AI without supporting infrastructure creates fragility

Customer Decisioning Blueprint Framework

• Need practitioner-owned blueprint, not vendor-defined solutions

• T-shaped framework structure:

• Horizontal bar (enterprise alignment):

• Data foundation (pipelines, customer ID, interaction history)

• Business context (action catalog, proposition library)

• Intelligence layer (data science models, AI models, explainability)

• Vertical bar (decision value chain):

• Decision intelligence

• Real-time optimisation and re-decisioning

• Activation across channels

• Wrapper requirements:

• Trust and strategy governance

• Operating discipline

• Feedback loops and continuous measurement

• Value realisation tracking

Future of Marketing: AI Agents Evolution

• Current state: Human-in-the-loop (AI-assisted)

• Human initiated

• AI structuring/support

• Human refinement

• Future state: AI agents handle 80% of execution

• Humans focus on monitoring, strategy, governance (reverse 80/20 rule)

• Move from data collection to insight generation

• End goal: Autonomous/cognitive marketing with contextual, adaptive execution

Implementation Principles

• Foundation strength critical - supports all future AI/automation layers

• Requires enterprise alignment between data, technology, and business teams

• Must be explainable, transparent, accountable, and adaptive

• Framework needs to be scalable and support organisational growth

• Success depends on proper governance and operating discipline, not just technology