Hyper-personalisation Part 2.
Welcome to THINK-IN Vol. 1
Hyper-personalisation. Part 2.
Following on from part one of hyper-personalisation, we’re now looking at the outputs from the second group [See part 1 for more information].
How can we deliver seamless, personalised experiences without crossing the line on privacy and ethics?
Let’s get into it.
Eliminating the filter bubble in hyper-personalisation is crucial for better discovery, diversity, and user engagement. Here are three strategic solutions:
Algorithmic Serendipity
Instead of only reinforcing past behavior, introduce controlled randomness in recommendations.
🔹 Blend personalised content with new, unexpected suggestions.
🔹 Use multi-dimensional AI models that factor in novelty and diversity rather than just relevance.
🔹 Example: A mobility app could suggest alternative routes, eco-friendly transit, or nearby experiences, not just the usual commute.
User-Controlled Personalisation
Give users transparency and control over their recommendations.
🔹 Implement adjustable personalisation settings where users can widen or narrow their interest scope.
🔹 Show why certain content is recommended and allow users to tweak their preferences.
🔹 Example: A retail app could let users toggle between “familiar” and “explore” modes to break their shopping habits.
Cross-Domain Data Integration
Expand personalization beyond single-domain behaviour tracking.
🔹 Instead of basing recommendations on just past purchases or trips, integrate broader lifestyle data (while respecting privacy).
🔹 Example: A payment provider could suggest local businesses, experiences, or mobility options based on spending patterns—helping users break out of their usual choices.
Which solution do you think is the most impactful? Let’s turn it into a LinkedIn post! 🚀
Answer Attendees: Can you solve number 1?
Here’s a LinkedIn post tackling the filter bubble problem with Algorithmic Serendipity.
Are algorithms making us predictable?
Hyper-personalization should enhance our experiences—not trap us in the same cycle. Yet, most algorithms do just that.
🚨 Always the same coffee order.
🚨 Always the same commute route.
🚨 Always the same content.
Welcome to the filter bubble. 🔄
Personalization shouldn’t be a loop—it should be a launchpad. Here’s how we break free:
🔹 Blend AI-driven randomness into recommendations—so users discover new paths, shops, or mobility options.
🔹 Factor in novelty—not just past behaviour. A mobility app could suggest scenic routes, bike-share options, or co-working cafés nearby.
🔹 Let users control their feed—a toggle between “familiar” and “explore” modes gives them power over their own experiences.
Hyper-personalization should be a guide, not a cage.
Where’s the balance between relevance and discovery? Let’s discuss. 👇
#mobilitypayments #AI #hyperpersonalisation #userexperience
(This post was AI-generated.)
Now, let’s create a visual to match:
A grayscale cityscape with neon orange/yellow highlights, showing multiple pathways leading in different directions—symbolizing new discoveries breaking the filter bubble.
