How to Leverage Hyper-Personalisation in E-Commerce: AgileIntel's Approach to Real-Time Solutions

E-commerce now demands relevance for success. Customers expect brands to recognise their preferences in real time and deliver hyper-personalised experiences, anticipating their needs. Real-time hyper-personalisation is reshaping customer engagement, and at AgileIntel Research, we see it as the next competitive advantage. 

What is Real-Time Hyper-Personalisation? 

Hyper-personalisation combines traditional customer insights with real-time behavioural, transactional, and contextual signals. Unlike static personalisation, which depends on demographics or previous purchases, hyper-personalisation adjusts to customers' current actions in real-time. 

Examples of signals include: 

• Current browsing behaviour 

• Device and location 

• Time of day and seasonality 

• Real-time product availability 

AI and machine learning translate these signals into adaptive recommendations, offers, and content that enhance customer engagement and revenue. 

Why does it matter? 

Our analysis projects the market for hyper-personalisation to reach US$21.79 billion by 2024 with a projected CAGR of 11.8% through 2034, fuelled by advanced technologies and an increasing demand for customised customer experiences. 

Similarly, a McKinsey report on personalised marketing noted that 71% of consumers expect businesses to provide personalised interactions, while 76% express frustration when this expectation is not fulfilled. The research highlights how AI and generative AI are ushering in a new era of personalisation by enhancing relevance, imagery, tone, and messaging in real time. 

The Akeneo 2025 B2C Survey, conducted by the technology company known for its Product Information Management (PIM) and Product Experience Management (PXM) software, gathered insights from 1,800 consumers from 8 countries. The findings highlight that 57% of consumers indicate that personalisation would boost their loyalty to a brand. On average, nearly half of U.S. shoppers are willing to pay 28% more for personalised experiences. Additionally, the survey revealed that 64% of U.S. consumers have made purchases based on recommendations from influencers or experts. This demonstrates how tailored experiences now significantly impact buying behaviour. 

Industry Spotlights 

The following are examples of hyper-personalisation in action: 

Saks Global's Personalised Homepages: 

Luxury retailer Saks Global uses AI-driven algorithms to curate homepages for individual customers based on recent browsing. As per Vogue Business, this approach boosted revenue per visitor by 7% and improved conversion rates by nearly 10%.  

Amazon and Sephora Recommendation Engines: 

Amazon utilises real-time purchase history, browsing signals, and inventory information to enhance its recommendations, where real-time clickstream data fuels the "Frequently Bought Together" and "Customers Also Viewed" sections. 

Marketplaces with Social-Style Feeds: 

Etsy and eBay are implementing TikTok-inspired discovery feeds to enhance user engagement. Their highly personalised streams showcase real-time browsing, purchasing, and favouriting behaviours. 

AI-Based Beauty Diagnostics: 

Companies like Revieve, a software-as-a-service (SaaS) company headquartered in Finland, use AI and augmented reality (AR) to analyse skin or hair conditions and then deliver tailored routines and product recommendations. This real-time diagnostic approach bridges digital and physical experiences. 

Nike's Mobile App and SNKRS App Personalisation: 

Nike's mobile app leverages real-time purchase and browsing to provide highly personalised product suggestions and to offer exclusive rewards to individual users. The brand's SNKRS app employs live demand indicators to launch limited-edition shoes, customising releases based on user activity. 

Enablers and Challenges 

Implementing real-time hyper-personalisation requires robust data, technology, and governance. The following outlines the primary enablers and challenges organisations encounter. 

• Customer Data Platforms (CDPs) – These platforms consolidate behavioural and transactional data to create a unified customer profile, ensuring consistent personalisation.  

• AI and Machine Learning – These technologies help anticipate customer needs and preferences, improving relevance across various channels.  

• Real-Time Data Streaming – Solutions such as Apache Kafka or AWS Kinesis facilitate immediate reactions to customer interactions.  

• Composable Architecture – This approach utilises modular systems that support rapid testing and scaling of new applications.  

• Privacy and Trust – Following GDPR/CCPA regulations and maintaining transparent consent processes fosters customer trust.  

• Testing Frameworks – Implementing A/B and multivariate testing allows strategies to adapt to user behaviour. 

Challenges 

While the benefits are compelling, organisations face notable challenges: 

• Data Silos – Disparate systems hinder the ability to provide cohesive experiences.  

• Performance – Personalisation must be instantaneous, ensuring a seamless user experience.  

• Privacy Balance – Excessively intrusive suggestions can jeopardise user trust. 

• Bias in Algorithms – Without proper safeguards, personalisation may inadvertently marginalise specific groups.  

• Resource Demands – The costs associated with infrastructure and skilled personnel can be significant.  

• Omni-Channel Scaling – Achieving uniform personalisation across various touchpoints presents a challenge.  

• Measuring ROI – For many organisations, determining the direct impact of personalisation remains a complex task. 

  
  

How AgileIntel Research Can Help 

AgileIntel Research supports businesses in navigating hyper-personalisation. Our services include: 

• Data Audit & Strategy – Mapping signals, identifying gaps, and aligning personalisation with ROI goals. 

• Technology Advisory – Selecting the right CDPs, AI models, and real-time platforms for scalable solutions. 

• Use-Case Pilots – Designing pilots such as personalised homepages or abandonment triggers to validate impact before scaling. 

• Ethics & Compliance – Embedding privacy-first approaches and transparent consent frameworks. 

• Measurement & Optimisation – Establishing KPIs, testing frameworks, and continuous improvement cycles. 

Our approach delivers increased revenue per visitor, improved customer retention, and enhanced end-user trust. 

Roadmap for Adoption 

For e-commerce leaders contemplating hyper-personalisation, we suggest: 

• Map Customer Journeys – Determine the moments where personalisation can create the most significant effect, such as product discovery, cart abandonment, or repeat purchases. 

• Enable Real-Time Data Capture – Establish pipelines to gather behavioural signals, session data, and inventory updates immediately. 

• Start with Quick Wins – Initiate pilots like personalised product carousels, targeted promotions, or adaptive search results to showcase ROI quickly. 

• Scale with AI Models – After initial pilots succeed, broaden to more advanced use cases such as dynamic pricing, predictive offers, or personalised homepages. 

• Integrate Across Channels – Ensure that personalisation remains consistent across web, app, email, and physical stores, preventing fragmented experiences. 

• Prioritise Privacy and Transparency – Incorporate transparent consent processes and communicate how customer data is utilised to foster trust. 

• Measure and Iterate – Establish KPIs such as conversion lift, engagement time, and revenue per visitor. Employ A/B testing to refine strategies continuously. 

Final Insights 

Real-time, data-driven hyper-personalisation is transforming e-commerce. Success will favour those who provide adaptive experiences while upholding trust and performance. 

At AgileIntel Research, we assist organisations in designing, testing, and scaling hyper-personalisation strategies that are both innovative and responsible. The time to act for e-commerce businesses prepared to transition from promise to performance is now.