Hyper-personalisation in retail has shown huge advances over the past 12 months. Already an exciting concept, new technologies and AI advances are creating even more powerful and innovative approaches.  Retailers at all ends of the market are increasingly leveraging AI and machine learning to analyse datasets. Browsing behaviour, purchase history and real-time engagement can reveal critical consumer trends. Analysis shows the global market for AI in retail was valued at US$11.61 billion in 2024 and is expected to grow at a CAGR of 23 per cent from 2025 to 2030.

Consumers express a strong preference for personalisation. The 2024 Publicis Sapient Digital Commerce Survey found that a lack of personalisation was a source of dissatisfaction. Across retail, 67 per cent of consumers want more personalised interactions and real-time recommendations when shopping, especially Millennials (70 per cent). This is even higher IN travel and hospitality, where 71 per cent of consumers want personalised recommendations of places and activities based on their preferences.

Key elements of hyper-personalisation

Using AI enables the precision targeting of audiences as well as compelling personalised content at scale. Some of the key elements for retailers include:

• Leveraging data: retailers are increasingly focusing on capturing more customer data through loyalty schemes and investing in customer data platforms (CDPs) to integrate information from multiple channels. This helps create unified customer profiles, enabling more precise personalisation.
• AI and machine learning: The increasing accessibility of AI for organisations of all sizes allows even smaller brands to consolidate data from various channels, harness insights and identify actionable strategies. 
• Optimising content: AI can analyse user behaviour, preferences and engagement patterns in real time, and optimise content accordingly. This enables microtargeting, with each user getting a precisely tailored experience.
• Integrating offline and online: Hyper-personalisation supports a seamless shopping experience across different channels and can integrate the physical and digital dimensions, allowing consumers to engage in a much more seamless way. This includes using augmented reality (AR) and virtual reality (VR) for immersive experiences.
• Customer engagement: Agentic (highly adaptable) AI and Generative AI are enabling autonomous chatbots, voice assistants and messaging apps. These expand engagement opportunities, providing personalised interactions and support.
• Retail media networks: RMNs are attractive to media buyers as they enable the segmentation of customers for more specific and personalised targeting, getting the right product in front of the right consumer at the right time, enhancing the value for brand advertisers.

Examples of the power of data to personalise

Hotel chain Marriott wanted to find a way to cater to the unique preferences of its over 140 million customers. It built a platform that applies guest data and machine learning to curate the trip of a lifetime, staycation or ‘bleisure’ trip, with personalised suggestions and offers, all within Marriott’s Bonvoy loyalty ecosystem.

The platform allows Marriott’s employees to exercise greater agility when creating solutions for guests. It has resulted in a 100 per cent increase in year-on-year bookings.

Another successful example of hyper-personalisation is a global fast-food chain that has implemented an AI-powered app that enables personalised ordering for customers. The platform supports marketing campaigns and highlights new product categories, landing pages, loyalty program perks and new features.

The fast-food app has improved conversion, leading to a 44.6 per cent increase in revenue, a 44 per cent rise in transactions, and a 17.6 per cent increase in site visits. 

An Australian brand using hyper-personalisation to both drive sales and elevate the customer experience is Virgin Australia. The airline used SabreMosaic’s AI-driven platform to deliver tailored fare solutions with dynamic and competitive pricing across all booking channels, meeting customer needs and market demand. For customers, it means they can get personalised pricing, bundled services and rewards across their whole journey.

These are exciting times, but there must be caution around the deployment of these technologies. A growing body of research suggests that while people want personalised experiences, they remain wary about sharing their data without a clear benefit and transparency as to its use. At the same time, data privacy legislation is increasing worldwide.

Retailers must ensure they have robust and transparent data policies both for legal compliance and consumer trust. Brands that personalise through the ethical use of data, offering reciprocal benefits to their customers, will gain the advantage in a fiercely competitive sector.

By John Costello, Publicis Sapient

This article was first published by Inside Retail