Accenture Song’s trend research consistently shows that today’s consumers expect every brand interaction to feel uniquely relevant. Brands that meet this expectation don’t just build better experiences — they see measurable gains in customer loyalty, engagement, and conversion.
Historically, the biggest barrier to hyper-personalisation was cost and complexity. But thanks to advances in AI and generative AI (GenAI), marketers can now tailor content, offers, and journeys in real time — unlocking smarter, more efficient ways to engage individuals at scale.
It’s Not Just Plug-and-Play AI
However, real personalisation isn’t as simple as booting up Midjourney or ChatGPT. To deliver effective, scalable hyper-personalisation, brands must look beyond tools. Success depends on strong foundations — including robust data infrastructure, thoughtful technology choices, operational readiness, and responsible data practices.
What I’ve Learned Working with Leading Brands
Since ChatGPT burst onto the scene in November 2022, I’ve had the privilege of working with some of Accenture Song’s leading clients. We’ve evolved from testing small proof-of-concept pilots to building enterprise-scale capabilities. Across every engagement, one truth has stood out:
Data is the secret sauce
It’s what powers AI to deliver relevance, impact, and differentiation. Without a strong data foundation, models produce generic outputs that miss the mark — lacking brand voice, customer resonance, and contextual accuracy.
The Five Data Pillars of Hyper-Personalisation
What types of data matter most for GenAI to succeed in hyper-personalisation?
Customer Data & CDP
Understanding the customer is critical but speed matters too. A Customer Data Platform (CDP) allows for real-time integration, analysis, decisioning and activation across channels.
Content Metadata & DAM
Generative AI thrives on well-structured content. A Digital Asset Management (DAM) system, enriched with metadata and performance signals, allows AI to understand what content works avoiding duplication and enhancing content reuse.
Channel & Performance Data
To improve personalisation, you need to measure it. Channel and performance data show what’s working and feed the optimisation loop allowing models to adapt messaging, offers, and creative based on live results.
Product Data & PIM
Structured product data, managed through a Product Information Management (PIM) system, gives GenAI the building blocks to generate accurate, brand-aligned product content and recommendations.
Contextual Data
What’s happening right now - device type, location, time of day, in-session behaviour - can dramatically boost relevance. Contextual signals help AI deliver experiences that are timely as well as personal.
Conclusion: Get the Data Right, Then Scale the Personalisation
To unlock the true power of GenAI in marketing, brands must first get their data house in order. A connected, structured, and enriched data ecosystem enables AI to generate experiences that are personal, performant, and scalable.
The brands leading in this space aren’t just experimenting with GenAI, they’re building the intelligent engines that power it and integrating with a workbench, set of automated production tools operated by an human-agentic workforce.
Authored by Matt Addison, Marketing Data & AI Lead, Accenture Song. Accenture Song are Partners of The Marketing Society
