Personalisation at Scale Using AI
https://open.spotify.com/episode/5AhIYQlhBUulvhEfUTbzTM?si=G5GPMhZASVG6vwU9c6jKEA
How to use AI to tailor experiences across channels while staying ethical, transparent, and commercially effective
This session helps marketers understand how AI can support personalisation at scale in a way that improves customer relevance, supports customer lifetime value, and respects privacy, consent, and trust. It combines current regulatory guidance, strong academic theory, and practical marketing application.
Why personalisation at scale matters now
AI has made it possible to tailor experiences, recommendations, timing, and content across far more touch points than manual teams could manage alone.
Reputable industry research now frames AI personalisation as a driver of growth, efficiency, retention, and brand relevance, especially when it is embedded across workflows rather than used in isolated pilots.
Reflection question:
Where would better personalisation make the biggest difference in your current customer journey?
What “personalisation at scale” actually means
Personalisation at scale is the ability to tailor content, offers, journeys, and service interactions across large audiences using data, automation, and AI.
McKinsey describes this through “next best experience”, which focuses on what a customer most needs in a given moment rather than sending the same message to everyone.
Reflection question:
Do your current personalisation efforts focus on customer need in the moment, or mainly on campaign efficiency?
The first theory to anchor this work: relationship marketing
Relationship marketing treats long-term customer relationships as a core source of value.
Academic work on customer management and customer lifetime value shows that selective retention and stronger relationships improve long-term marketing performance, which is why personalisation should be judged over time, not only by short-term clicks.
Reflection question:
Are you designing personalisation to improve short-term response, long-term value, or both?
The second theory: service-dominant logic
Service-dominant logic argues that value is created in use and in experience, not simply in the product itself.
In practice, this means personalisation should improve the customer’s actual experience of finding, choosing, using, and staying with the offer.
Reflection question:
Which part of the customer experience would feel most valuable if it were better tailored?
The third theory: privacy calculus and the personalisation-privacy paradox
Privacy calculus explains that people weigh perceived benefits against privacy costs when deciding what data to share.
The personalisation-privacy paradox shows the tension clearly: customers often appreciate relevance, yet feel uncomfortable when personalisation becomes opaque, invasive, or manipulative.
Reflection question:
Where might your customers feel the benefit of personalisation, and where might they feel the cost?
A simple strategic test before using AI
Before building any AI personalisation use case, ask:
Does this help the customer achieve something faster, more clearly, or with less effort?
Does it support a meaningful marketing objective such as retention, conversion quality, or lifetime value?
Can we explain it clearly and govern it responsibly?
These tests reflect current best practice from McKinsey, the ICO, and the EDPB, all of which emphasise value, purpose, and accountability.
Reflection question:
Which personalisation idea in your organisation would fail one of these three tests?
What customers now expect
Current consultancy evidence suggests customers increasingly expect tailored experiences, yet also want stronger trust, data protection, and transparency around how AI shapes interactions.
Accenture’s recent work points directly to consent-based personalisation and transparency as key trust builders, especially for loyalty-rich relationships.
Reflection question:
What would your customers most likely expect you to personalise, and what would they expect you to leave alone?
Data foundations come before AI
McKinsey’s latest AI and agentic AI research is consistent on one point: scaled AI depends on strong data foundations, not tool enthusiasm.
For marketers, this means personalisation should start with usable data architecture, clear IDs, governed data flows, and agreed data definitions.
Reflection question:
What is the weakest part of your current data foundation for personalisation?
Know your data types
The most useful personalisation inputs usually include:
First-party data from direct interactions
Zero-party data that customers intentionally provide, such as preferences and profile choices
Behavioural data from browsing, purchase, usage, and service interactions
The ICO’s AI guidance makes clear that purpose, adequacy, and fairness must shape what data you collect and use.
Reflection question:
Which data type do you underuse today: preference data, behavioural data, or transactional data?
Data minimisation still applies
The ICO is explicit that AI systems often want lots of data, yet organisations still need to collect only the data they actually need and no more.
This is especially important in marketing personalisation, where teams can be tempted to capture excessive detail “just in case”.
Reflection question:
What personal data are you currently collecting that you could stop collecting without reducing value?
Governance is part of the marketing job
AI personalisation sits inside data protection law, not outside it.
The ICO and EDPB both emphasise lawfulness, fairness, transparency, accountability, data protection by design, and role clarity across AI development and deployment.
Reflection question:
Who in your organisation owns personalisation governance today, and is that clear enough?
Profiling and automated decision-making need special care
Personalisation often involves profiling, which UK GDPR and EU guidance treat as a regulated activity.
The ICO and EDPB both stress that profiling and automated decision-making require transparency, fairness, and clear lawful basis, especially where decisions significantly affect people.
Reflection question:
Which of your current personalisation activities would count as profiling, even if you do not label it that way internally?
Acquisition use case: media and audience personalisation
AI can improve acquisition by helping teams:
Score audiences more intelligently
Tailor creative by segment
Suppress wasteful impressions
Sequence messages by intent
This works best when the logic is transparent and the underlying segment definitions are clearly tied to business goals, not vague assumptions.
Reflection question:
Where in acquisition are you still sending broad messages that could be made more relevant?
Web and app use case: experience personalisation
Practical examples include:
Personalised homepages
Product or content recommendations
Dynamic offers or content blocks
Next-step prompts based on behaviour
The strategic question is whether each change improves the customer task, not only the conversion rate. Service-dominant logic and perceived-control research both support this view.
Reflection question:
Which page or app step would benefit most from a more relevant experience?
Email and lifecycle use case
AI can support:
Send-time optimisation
Subject line variation
Lifecycle sequencing
Retention or reactivation triggers
Content blocks matched to known preferences
McKinsey’s “next best experience” model is particularly useful here because it frames email as part of a wider decisioning system, not as a separate channel.
Reflection question:
Which lifecycle moment would benefit most from more intelligent timing or content selection?
Commerce use case: recommendations and merchandising
Strong practical applications include:
Recommended products
Bundles
Replenishment prompts
Personalised sorting or navigation
Academic and practitioner work both suggest this can improve customer satisfaction and loyalty when relevance is strong and the logic feels fair and useful.
Reflection question:
Where in your buying journey would recommendation logic feel most useful to the customer?
Service use case: next best action
AI personalisation is increasingly being used in service, retention, and support, not only in media and content.
McKinsey’s “next best experience” approach captures this well by coordinating decisions across acquisition, service, and retention around the customer’s current need.
Reflection question:
What customer service or retention moment currently feels generic when it should feel more responsive?
Transparency needs design, not legal wording alone
The ICO states that people affected by AI-assisted decisions should be given explanations, and that explanation is expected whichever type of AI-assisted decision is made.
Research also shows transparency interacts with trust and perceived control, so poor disclosure design can reduce confidence rather than improve it.
Reflection question:
If a customer asked, “Why am I seeing this?”, could you explain it in one plain-English sentence?
Give customers control they can actually use
Useful control mechanisms include:
Clear preference centres
Channel choices
Topic choices
Pause and frequency controls
Meaningful opt-outs from specific types of personalisation
Academic work on transparency and control suggests perceived control can reduce privacy concern and strengthen the customer experience.
Reflection question:
What control could you give customers that would make your personalisation feel more acceptable?
Fairness and bias need checking before scale
The ICO’s fairness guidance for AI is explicit that teams make value-laden choices about what data to include and why, and these choices affect fairness outcomes.
Recent ethics research on AI in marketing also highlights responsibility, oversight, and the risk of ethically questionable decisions being scaled through automation.
Reflection question:
Which customer group could be unintentionally disadvantaged by your current data or decision rules?
Build a simple operating model
A workable personalisation model usually needs:
Marketing ownership of use cases and customer value
Data and analytics support
Legal and privacy input
Product or channel owners
Agreed review and approval points
McKinsey’s latest AI work keeps returning to the same scaling factors: strategy, data, operating model, talent, and adoption.
Reflection question:
Which role or capability is missing from your current personalisation setup?
Measure more than clicks
Useful scorecards should include:
Conversion quality
Retention or repeat purchase
Customer lifetime value
Unsubscribe or opt-out rates
Complaint volume
Customer trust or satisfaction signals
This keeps personalisation tied to long-term value and helps catch harm early.
Reflection question:
What one non-click metric would tell you whether personalisation is actually helping customers?
Use an experiment-and-learn loop
A practical cycle looks like this:
Define the use case
Define the data needed
Check lawful basis and governance
Test a controlled experience
Measure uplift and downside
Refine, document, and scale
This is safer and more effective than trying to personalise everything at once.
Reflection question:
What is the smallest personalisation experiment you could run safely in the next 30 days?
Final Reflections
The strongest AI personalisation programmes do four things well, they:
Solve a real customer need
Use disciplined data foundations
Explain themselves clearly
Protect trust while improving value
That is how personalisation becomes a strategic capability rather than a collection of automated tactics.
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