Author: Victor Churchill, a Product Designer at Andela. He is an exceptional Product Designer with over 7 years of experience identifying and simplifying complexities with B2B, B2C and B2B2C solutions. Victor has a proven track record of conducting research analyses utilising these insights to drive business growth and impact.
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After taking the world by storm in just a few years AI has now come to change marketing and UX design. With people adapting to new advertising techniques too quickly the only way to really engage with potential customers is to create a meaningful connection on a deeper level. That’s where hyper-personalised user experiences step in.
In a world of marketing, hyper-personalised user experiences are a way to drive conversions by presenting to viewers highly relevant offers or content. At the heart of this technology lies powerful AI that crunches vast amounts of user data in order to tailor content to a specific user. To do this it goes through multiple sources of information like user behavior data (clicks, search queries, time spent on each page), demographic and profile data (age, location, language), and contextual data (device model, time of day, and browsing session length).
After gathering all the data it could collect, AI segments users into different categories based on the goals of the campaign: frequent buyers and one-time visitors, local and international shoppers, and so on. Algorithms then analyse potential ways for improving user experience. Based on the results, the software decides to prioritise one form of content or feature over the other for said user. For example, a fintech app notices that a user frequently transfers money internationally and starts prioritising currency exchange rates in their dashboard.
As Senior Product Designer at Waypoint Commodities, I always draw parallels between hyper-personalised user experiences and the way streaming platforms like Netflix and Spotify operate. These services personalise product recommendations based on the customer’s spending preferences and tastes. This way users get experiences that feel custom-made, which can dramatically increase engagement, time spent on platform, and conversion rates. A report from McKinsey revealed that 71 percent of consumers expected companies to deliver personalised interactions, and 76 percent got frustrated when it didn’t happen. The numbers are even higher if we speak about the US market, where up to 78% of customers are more likely to recommend brands with hyper-personalised interactions.
This trend is most visible in fintech and e-commerce, where user experience is critical for driving conversions, building trust, and keeping customers engaged. In these spheres additional friction such as irrelevant recommendations, or a lack of personalization can lead to lost revenue and abandoned transactions.
In order to create a hyper-personalised design it is important not to overstep. A study by Gartner revealed that poor personalisation efforts risk losing 38% of existing customers, emphasising the need for clarity and trust in personalisation strategies. The situation can backfire if users feel like they are being constantly watched. To avoid this, I always follow a few simple but essential principles when designing for personalisation.
Be transparent.
When you show something hyper-personalised to your customer, add a simple note saying ‘Suggested for you based on your recent purchases‘ or ‘Recommended for you based on your recent activity‘. This way users are informed about the channels you get information from, and your recommendations don’t come as a shock for them.
Don’t forget to leave some control to the user.
Even if you fine-tune your system to perfectly detect the needs of customers, some people can still find the recommendations irrelevant. This is why it’s important to allow customization through buttons like ‘Stop recommending this‘ and ‘Show more like this‘.
Don’t overuse personal data.
Even though sometimes it can feel like everybody is used to sharing data with advertisers, violating personal borders can usually lead to unsatisfying results. According to a survey by KPMG, 86% of consumers in the US expressed growing concerns about the privacy of their personal data. And 30% of participants said they are not willing to share any personal data at all.
Be subtle in your personalization and don’t implement invasive elements that mention past interaction too explicitly or use sensitive data. For example, don’t welcome a user with the words ‘Worried about your credit score?‘ or ‘Do you remember the shirt you checked out at 1:45 AM last night?‘.
Be clear about AI usage.
AI-driven personalisation lifts revenue by 10-15% on average, reports say. However, if the majority of the decisions in the recommendation system is made by artificial intelligence, people have a right to know that. Don’t put too much stress on it — just mention the important part with a little message saying that your suggestions are powered by AI. This way you can avoid misunderstanding.
Even though current systems already work well at detecting the needs of the customers, there’s still room for improvement. The hyper-personalised user experiences of the future could learn to read new data like voice, gestures and emotions or even anticipate needs before users even express them. It is clear that in the future AI-driven UX design will only become better, and now is the best time to embrace this technology.
Absolutely insightful read! As a Product Designer, I’ve seen firsthand how hyper-personalised experiences when done right can transform user engagement and retention. The key is balancing relevance with respect for privacy. AI offers powerful tools for creating tailored experiences, but we must always design with transparency, user control, and ethical use of data at the core. Great reminder that meaningful connections drive real impact.
Victor, as a project manager, I couldn’t agree more with your take on hyper-personalized user experiences! Your breakdown of how AI leverages user data to tailor content is spot-on and aligns perfectly with the goals we strive for in delivering impactful projects. The way you highlight transparency, user control, and respecting privacy really resonates deeply and I must say that these principles are key to ensuring our teams build solutions that foster trust and engagement. I especially appreciate your examples, like adding “Suggested for you based on recent activity” or “Show more like this” buttons, which are practical ways to balance personalization with user comfort. The stats you shared, like ‘71% of consumers’ expecting personalized interactions, underscore the urgency for our projects to prioritize more AI-driven UX to drive conversions, especially in fintech and e-commerce! Your points about avoiding invasive tactics is also a great reminder for us to keep user privacy at the forefront of our planning.
I am ecstatic and excited to see how AI will continue to shape the future of UX—fantastic insights for guiding our next project!
Brilliantly articulated! I’ve been working on personalization models for a payments platform and I’ve seen firsthand how much impact AI-driven UX can make and that’s not just in increasing conversions but in building long-term trust. What stood out to me here is your emphasis on transparency and control. We actually A/B tested a version of our home screen where we added a simple ‘Recommended for you based on your bill payments’ tag v/s one without it. The tagged version had 14% higher click-through and users described the experience as ‘thoughtful’ rather than ‘targeted.’
What I’d love to explore further is how we can apply similar personalization in low-data scenarios, like new users or privacy-conscious audiences. Maybe using anonymized behavioral patterns or federated learning models could be the way forward. This article lays down a very practical foundation.