Hyper-personalisation in eCommerce: How AI is shaping the future of customer loyalty

Consumers expect tailored shopping experiences, and much of the time, they get them: 73% of customers said most companies treat them as unique individuals, which leapt up from 39% between 2023 and 2024. AI-enhanced shopping is already shaping this personalised eCommerce landscape — $229 billion (19%) of global online holiday season sales were influenced by AI recommendations, offers and customer support in 2024.  

At the same time, internet users are more aware of how their data is being used and may find tracking intrusive meaning personalisation is reaching an inflection point. Tailored experiences help drive brand affinity, but careless data use and poor protection from retailers could ruin the relationship. How can brands continue to build loyalty and win the trust of data-conscious customers? 

In the age of AI, eCommerce is poised to benefit from hyper-personalisation at scale, with the potential to create custom experiences that can move the needle on revenue and loyalty. See what hyper-personalisation will look like and explore AI’s role in the shift toward treating customers as individuals on every digital buying journey.  

What hyper-personalisation looks like 

Personalisation in eCommerce can serve up options such as product and brand suggestions based on buying history or basic demographic trends. Hyper-personalisation tailors the experience much more — users may be presented with results based on their location, recent clicks and interactions.  

While traditional personalisation is reactive, hyper-personalisation works in real-time, offering customised touchpoints based on current actions as well as predictions of what a user will do next. Predictive analytics can forecast future actions and behaviours, using machine learning and AI alongside unified customer data platforms and insights from data warehouses to deliver the most relevant options and craft bespoke journeys.  

Examples of hyper-personalisation in eCommerce include: 

• Real-time product suggestions (Next-Best-Action). Sequential recommendation models can predict a user’s next action based on a string of events, so they can be served hyper-personalised product and brand suggestions. 
• Targeted social media content. Ads can be served to very specific audiences informed by previous interactions (known as retargeting), or lookalike audiences can be created based on predicted interests and behaviours.  
• Unique loyalty rewards. Birthday gifts, points and incentive offers can be issued based on interests and predicted future purchases. This helps incentivise loyalty and turn buyers into repeat customers.  
• Location-based recommendations. Retailers can combine eCommerce and bricks and mortar shopping experiences, with in-app offers when customers are close to a store or invitations to events for customers in certain locations.  

What moves loyalty metrics? 

Retailers looking to boost loyalty must strike a delicate balance, ensuring hyper-personalisation is helpful, not unsettling. This can be a common complaint, particularly among US and UK consumers, with 56% of survey respondents in these locations feeling “creeped out by personalised ads”. At the same time, hyper-personalisation is a non-negotiable part of the eCommerce experience, and 7 in 10 Gen Z and millennial consumers said they would quit a brand that doesn’t use it.  

How can retailers meet these needs without intruding? The power of choice and careful, pared-back data use are key to gaining consumer confidence: “options to opt out”, “minimal data collection” and “clear privacy policies” were the top three criteria that would make survey respondents feel more comfortable with personalised ads. Brand loyalty is fragile amid cost-of-living crises and rising inflation globally, so instilling confidence is an important first step. To focus on retention, companies are expected to invest more heavily in personalised loyalty programmes to drive repeat business and boost lifetime value — loyalty rewards, customer service, quality and pricing were found to be the top factors driving brand loyalty. In fact, 72% of customers said they would remain loyal to companies that deliver faster service, and 65% said a more personalised experience would win their loyalty. There is significant opportunity for AI-driven experiences that ensure customers are treated as individuals, presenting very real prospects to deepen relationships and secure loyalty.  

What’s to come? How AI is driving progress 

Emerging AI applications that will progress hyper-personalisation as a driver of customer loyalty include: 

• AI chatbots acting as shopping assistants. Chatbots are rapidly becoming an effective way to provide accurate, efficient customer support with simple enquiries and processes such as product returns. This frees up customer service agents to handle more complex enquiries that require human intervention, providing tailored support to suit each customer. Given that poor service was found to be the second most common reason customers stopped buying from a brand, meeting needs is crucial for retention. Amazon’s Rufus shopping assistant is one example, answering queries, making recommendations and facilitating product discovery just like a digital sales assistant, while integrated seamlessly within the interface. 
• Personalised search results. AI and machine learning can be used to personalise and improve on-site search results, analysing vast amounts of data inputs to suggest and serve up the right results quickly. This removes barriers to information and makes search results more engaging, which can help bolster brand perception and act as an extension of great customer service for the purpose of retention.  
• Recommendations in real time. While consumers can be inherently suspicious of cookies, 39% still agreed that personalised advertising is helpful for discovering new products they may want to buy. There is a place for personalised recommendations, and these can go beyond advertising, with messaging on-site, in-app and via email at key points along the customer journey.  
• Self-service product documentation and “help” articles. Generative AI can create articles at scale tailored to customers and their potential pain points, helping them self-serve and overcome barriers to buying. To identify topics, AI can parse data to reveal what people search for on the website and in the wider retail ecosystem, helping retailers create the right content to keep customers engaged.  
• Precise messaging. Generative AI will save time for marketing teams by creating targeted emails, ads, in-app offers and other content that can be hyper-personalised. These can be scheduled at optimum times to remind customers what’s in their cart, or around the time they may need to restock using replenishment propensity metrics, retaining them in the sales funnel.  
• Tailored offers. Along with hyper-personalised marketing content, generative AI can be used to create offers, promotions and loyalty rewards for the individual, making them more compelling than generic approaches.  
• AI avatars. In a bid to cut returns and increase confidence — classic levers used to understand loyalty — customers can create digital avatars based on themselves to virtually try on products. This approach is already being pioneered by brands such as Zalando, which also offers AI assistants and trend spotters to hyper-personalise and localise the experience.  

AI’s ability to create unique experiences without reliance on third-party cookies is important for customers’ comfort levels. UK consumers found ads based on browsing history and social media behaviour to be the most invasive, so the ability to deploy hyper-personalisation at scale using earned first-party and zero-party data is pivotal. While Google has backtracked from removing third-party cookies in Chrome, the direction of travel still needs to be privacy first to win trust and ongoing custom. 

Essential guardrails to protect retailers and customers 

Regulators are watching AI implementation closely. The EU’s AI Act will be fully applicable from August 2, 2026, directing that websites cannot use AI to “trick” users into spending money. This includes AI-enabled dark patterns designed to manipulate users into making substantial financial commitments — AI agents must be helpful, not coercive. The UK’s Guidance on AI and Data Protection also emphasises considerations of fairness, protecting people and vulnerable groups.  

To put effective guardrails in place, retailers must be able to provide human oversight, offering explanations for how and why AI models make the recommendations they do to avoid bias or manipulation. Technology choices should be carefully considered and audited regularly, and checks must be in place to avoid potential bias impacting offer eligibility and sales tactics. 

Measuring the success of personalised eCommerce experiences 

To get under the hood of hyper-personalisation and track success beyond click-through rates, retailers can consider these metrics: 

• Loyalty indicators: measuring repeat rates, active member numbers, points breakage, churn and lifetime value/average revenue per user. 
• Friction metrics: return rate (including reasons such as size or fit), “can’t find” queries and time to decision. 
• Trust signals: opt-in rates, preference updates, data deletion requests, negative feedback. 
• Perceived relevance: surveys to understand relevance, post interactions. 

What does the future hold? 

According to Harvard Business Review, organisations already consider AI and generative AI to be more than back-office efficiency enhancers and are prioritising customer-centric use cases. 40% are using AI chatbots for customer service, but it is still early days. The eCommerce retailers that succeed will be those that use AI’s full capabilities to make sure customers are heard, understood and have seamless experiences that feel natural. AI is now uplifting sales — to drive repeat custom and brand loyalty, retailers must focus on understanding their customers’ needs and perceptions, then building an ecosystem where hyper-personalisation makes mindful use of data and never side-steps into intrusion, instead offering genuine value. 

Talk to NashTech about customised AI use cases to drive profitability in eCommerce, or learn more about how our AI solutions achieve impact.