How Hyper-personalization is Impacting Digital Experiences

Offering exceptional digital experiences for your customers should be easy and accessible. This is why we partnered with Latrobe university to explore how we can leverage AI to give you the tools to create them. 

This partnership focuses on developing cutting-edge personalization tools for smarter, faster, and more intuitive online experiences.

We’re exploring how real-time data, AI-powered language models, and geo-contextual insights can transform digital interactions.

Our goal is to create adaptive digital journeys that respond instantly to user needs while protecting privacy.

Let's dive into what hyper-personalization is, how Core dna applies it, and why it benefits both users and businesses. 

The future is about connecting your goals with ours—instantly, seamlessly, and ethically.

First, what is Hyper-personalization?

Hyper-personalization goes beyond simple content recommendations like the a Netflix or Google would do. 

 It focuses on aligning content with user intent in real time, making digital experiences smoother and more relevant.

Traditional personalization relies on past interactions and set rules. Hyper-personalization, however, reacts instantly by analyzing user behavior, context, and intent signals. The goal is to reduce friction and deliver the right content before users ask for it.

This process happens in three stages:

• Zero-Shot Personalization: The system has no prior data. It uses geo-location, device type, referral source, and time of visit to guess intent.
• Few-Shot Personalization: As users engage, AI analyzes clicks, time on page, and navigation paths to refine recommendations.
• Headshot Personalization: If users submit a form or sign up, AI tailors content precisely based on their profile and interests.

Let's apply this logic to Core dna’s website:

1. Zero-shot: A visitor lands on the site during Black Friday. Core dna highlights automation deals based on the event.
2. Few-shot: Their clicks show interest in CMS pricing. A chatbot offers a comparison guide to help them decide.
3. Headshot: They sign up for a webinar. Core dna sends follow-ups with industry-specific insights.

Hyper-personalization enhances engagement, reduces frustration, and increases conversions by providing a smoother, more intuitive experience.

A key distinction in our research is that user objectives evolve. 

For example, during the holiday season, a visitor’s primary goal might be to find a Christmas gift for their loved ones so all their actions are different from their own preferences whereas in March, their focus could to buy something for them which entails different likes. 

Traditional systems struggle to adapt to such evolving intent. Our work ensures content and experiences continuously adjust based on the engagement phase and real-time behavioral data.

Dynamic content as a pillar for hyper-personalization

In this research phase, we use ourselves as test subject and see how we can implement dynamic content hyper-personalization in Core dna digital experience strategy.

We starts by understanding the user’s journey. Leads come from different places—Google searches, AI chatbots, or marketing campaigns. Each visitor has a different intent, and the goal is to identify and respond to that intent in real time.

User Journey & Dynamic Content in Action

Core dna’s AI-driven personalization adjusts site content automatically. This includes chatbots, dynamic layouts, and predictive content recommendations that help users find exactly what they need.

For example, if a visitor lands on the website via a google query "best CMS for franchises",  the core dna chatbot can instantly ask:

“Are you looking for multi-location management or centralized content control?”

This real-time personalization helps visitors quickly find relevant case studies, feature comparisons, or pricing details.

Let's try to apply it to another use case: Clark Rubber

This would apply to any type of business. Let's take a look at one of our clients Clark Rubber. As a pools and outdoor equipment franchise, hyper-personalization would allow the website to dynamically adjust content based on user behavior and location.

For example:

• Zero-shot: A visitor from Sydney lands on the site in summer. The homepage highlights “High temps this weekend? Get your pool ready!”.
• Few-shot: As they browse pool maintenance products, the site suggests bundles based on frequently purchased items.
• Headshot: At checkout, the system offers personalized discounts based on past purchases.

This real-time adaptation boosts engagement and helps users feel understood and supported throughout their journey.

The Role of Large Language Models in Hyper-personalization

Hyper-personalization at scale needs advanced technology to work. It must process real-time user data, adjust content instantly, and follow ethical data practices. Core dna is using AI and Large Language Models (LLMs) to improve personalization while protecting user privacy.

How LLMs Enhance Personalization

LLMs, like GPT-based models, are changing how businesses personalize content. These AI systems can create real-time, data-driven content that matches a user’s intent, location, and current trends.

For example, a sports retailer sees a spike in searches for Nike sneakers in Melbourne. AI can instantly generate an article about "the top 10 nike sneakers people are wearing in Melbourne right now", linking to relevant products.

This approach helps businesses:

• Deliver relevant, localized content that matches user interest.
• Automate content updates based on trends and searches.
• Create an AI-driven experience that adjusts in real time.

From Zero-Shot to Headshot Personalization

Core dna’s CMS is building a personalization engine that adapts in three stages:

• Zero-shot: The system makes an educated guess about intent using location, time, and referral source.
• Few-shot: AI refines content based on click behavior, browsing time, and navigation paths.
• Headshot: The system personalizes recommendations, emails, and interactions based on form fills and chat inputs.

Why LLMs Matter in Hyper-personalization

LLMs process large amounts of user data to make real-time decisions. They help businesses determine:

• What content to show based on intent.
• How to engage users at different stages.
• When to provide recommendations to improve user experience.

Core dna is exploring new ways to use LLMs for dynamic content adaptation. Personalization will soon go beyond past purchases. It will consider real-time factors like weather, trending products, and search behavior to make every interaction feel unique and relevant.

Real-world applications and business impact

Hyper-personalization can transform industries by helping businesses engage customers in real time. Companies that use it can increase conversions, improve user experiences, and build stronger relationships with their audiences.

eCommerce: creating smarter shopping experiences

E-commerce brands can adjust product displays, pricing, and promotions based on real-time user behavior. Instead of relying on past data, AI can predict what a shopper wants—even without prior browsing history.

For example, hyper-personalization can:

• Adjust product recommendations based on click patterns and time spent on pages.
• Show different promotions based on weather, trends, or user location.
• Reduce bounce rates by matching content with user intent as soon as they land on the site.

By delivering personalized shopping experiences, eCommerce businesses can increase sales and keep customers engaged.

B2B websites: personalizing the buyer’s journey

B2B customers have different needs depending on where they are in the buying process. Some visitors look for educational resources, while others are evaluating options or ready to buy.

Hyper-personalization helps businesses:

• Identify user intent and provide the right content at the right time.
• Dynamically adjust landing pages to highlight relevant case studies, pricing, or product demos.
• Reduce decision-making time by offering tailored recommendations based on user behavior.

With this approach, B2B companies can turn website visitors into leads faster and create a smoother buying journey.

Retail & franchises: driving local engagement

Retailers and franchises can use geo-contextual personalization to tailor experiences for local customers. By analyzing location, browsing patterns, and regional trends, businesses can show:

• Promotions that match seasonal demand and local events.
• Product recommendations based on regional buying trends.
• Location-based offers that make marketing campaigns more effective.

This real-time personalization helps franchises stay competitive and relevant, ensuring customers see offers that matter to them.

Hospitality & travel: customizing experiences for every traveler

The travel industry depends on personalization, and hyper-personalization makes it even better. AI can adjust recommendations based on real-time user behavior, ensuring that travelers see content relevant to their trip planning stage.

For example, businesses can:

• Show different content to users booking a last-minute flight vs. planning months in advance.
• Offer personalized travel itineraries and dynamic booking options based on user preferences.
• Use AI-powered chatbots to guide travelers through their planning process.

This creates a seamless booking experience and increases the chances of repeat customers.

Healthcare & Wellness: Delivering Personalized Support

Hyper-personalization in healthcare allows businesses to offer relevant health and wellness recommendations while keeping user data private. Instead of relying only on past interactions, AI can predict emerging needs and suggest solutions proactively.

Wellness brands can:

• Provide personalized health programs, fitness plans, and mental wellness resources.
• Offer interactive self-assessments to guide users to the right services.
• Use behavioral data to suggest relevant health content without storing personal details.

By combining personalization with privacy, healthcare businesses can boost user trust and engagement.

AI, privacy, and ethical considerations

Hyper-personalization has huge benefits, but it also raises concerns about privacy and ethical data use. Users want personalized experiences, but they also want to control how their data is used. Core dna ensures AI-powered personalization respects privacy without invading personal space.

Hyperpersonalization is powerful, but privacy must come first. Core dna’s approach prioritizes anonymity while still delivering rich, tailored experiences.

Unlike traditional tracking, Core dna does not follow individuals. Instead, AI analyzes patterns and trends to enhance the experience without invading privacy.

Privacy-preserving personalization: Patterns, not people

Core dna focuses on behavior patterns, not individual tracking. Instead of collecting personal details, our AI models analyze trends and engagement habits. This allows businesses to deliver relevant experiences without compromising user privacy.

To ensure ethical personalization, Core dna follows these principles:

• Consent-driven data collection – Users should know what data is being used and why.
• Anonymized insights – AI detects patterns across users, rather than tracking individuals.
• Transparent decision-making – Users remain informed and in control of their experiences.

Building Trust Through Ethical AI Practices

Good personalization builds trust—but when it feels invasive, engagement drops. Users enjoy relevant, timely content, but businesses must ensure it never crosses the line into manipulation.

Core dna, in collaboration with Latrobe University, is researching the fine balance between personalization and privacy. Our goal is to make AI smarter and more ethical, so users feel helped, not watched.

By prioritizing privacy, transparency, and user control, businesses can deliver personalization that feels helpful—not intrusive.