Hyper-personalisation examples from today’s most successful brands show that generic messaging simply doesn’t cut it any more. 71% of consumers now expect personalised interactions from the companies they engage with. Even more telling? 76% express frustration when brands fail to deliver these tailored experiences.
The message is clear: customers want businesses to understand their unique preferences and deliver relevant content exactly when they need it. This demand has pushed forward-thinking brands to embrace hyper-personalisation strategies.
What is an example of hyperpersonalisation?
Hyper-personalisation goes beyond basic segmentation. It actually leverages AI-powered systems, real-time behavioural data, and smart automation to create engaging, individualised experiences across multiple touchpoints.
Unlike traditional personalisation that might simply use a customer’s name in an email – we’ve all seen it, right?, hyper-personalisation uses specific data profile including:
- Past purchase history
- Real-time browsing behavior
- Location data
- Time-based interactions
- Device preferences
- Channel engagement patterns
This deeper approach enables brands to craft messaging that resonates on a fundamentally more relevant level. But does the investment pay off? According to McKinsey, companies that excel at personalisation generate 40% more revenue than their counterparts.
But how are leading brands actually implementing these strategies? And what tangible results are they achieving?
In this guide, we’ll explore 10 real-world hyper-personalisation examples that show how businesses are combining on-site and off-site automation to drive meaningful engagement, boost conversions, and build lasting customer loyalty.
Let’s examine these hyper-personalised content examples to uncover the best practices you can get inspired from, and apply to your own business strategy.
The core elements of effective hyper-personalisation
Before diving into specific examples, let’s explore what makes truly effective hyper-personalisation from basic customisation efforts. The most successful hyper-personalisation strategies share three critical components:
- Comprehensive data collection and analysis – Moving beyond simple demographics to understand behavioral patterns and preferences
- Real-time capability – Delivering personalised experiences at the exact moment of relevance
- Cross-channel consistency – Maintaining a cohesive experience across all touchpoints, from website to mobile app to email
With these principles in mind, let’s see how today’s market leaders are putting hyper-personalisation into practice.
10 powerful hyper-personalisation Examples from leading brands
1. Amazon – AI-Driven Product Recommendations
Amazon stands as a pioneer in the category of hyper-personalisation examples. Their recommendation engine continually adapts based on browsing history, purchase patterns, and wishlist activity.
When customers visit the site or app, they find a homepage filled with products specifically tailored to their interests. If they abandon their cart, Amazon follows up with targeted recovery emails or push notifications.
The company’s hyper-personalisation strategy extends far beyond simple “you might also like” suggestions.
Amazon’s algorithm analyses over 150 million user profiles to predict purchasing behaviour with accuracy. A study by Segment found that 49% of shoppers have purchased a product they didn’t initially intend to buy after receiving a personalised recommendation from Amazon.
What makes this work: Amazon’s success is based on dynamic segmentation that personalises recommendations across all channels—website, email, push notifications, and app notifications.
2. Netflix – Behaviour-based content suggestions
Netflix is another one of the perfect hyper-personalisation examples by customising everything from homepage layouts to content recommendations. They meticulously track:
- Watch history
- Viewing habits
- Preferred genres
- Similar users patterns
This data allows Netflix to automatically curate tailored shows, ensuring users discover content aligned with their preferences. For inactive users, Netflix sends targeted off-site reminders about new shows similar to their previous favourites.
What makes this work: Netflix’s competitive edge comes from leveraging AI to deliver content recommendations based on real-time behavioural data analysis.
3. Spotify – AI-Generated Playlists & Notifications
Spotify’s Discover Weekly, Release Radar and the latest Customed Defined playlists (see image above) represent hyper-personalised examples from content at their finest.
Using advanced predictive analytics, or Product Recommendations, Spotify creates custom playlists based on:
- Listening habits
- Liked songs and artists
- Time spent engaging with specific genres
- Customer profiling and AI-Learning
Additionally, users receive timely in-app push notifications when favourite artists release new music.
What makes this work: Spotify drives engagement through AI and predictive analytics that deliver relevant recommendations across multiple marketing channels. The secret, if you ask us? Making users feel seen and understood in the digital space.
4. Starbucks – Location-Based Mobile Offers
Starbucks leverages geolocation and AI to personalise promotions through its mobile app. When customers frequently visit specific locations and order particular drinks, the app suggests those same items for quick reordering.
More impressively, when customers walk near a Starbucks location, they receive targeted push notifications offering discounts or reminding them to use accumulated loyalty points.
What makes this work: Starbucks’ location-based marketing strategy effectively drives foot traffic and encourages repeat purchases by meeting customers where they are.
5. Grammarly – AI-Driven Weekly Writing Reports
Grammarly shows how data-driven personalisation can simultaneously improve user experience and drive revenue. Their weekly writing reports analyse user activity to deliver personalised insights that include:
- Writing productivity metrics (word count, active days)
- Tone and clarity improvements over time
- Specific enhancement opportunities based on individual writing patterns
For users, these insights provide tangible progress tracking while highlighting areas for improvement. But Grammarly’s strategy extends beyond engagement—these reports function as powerful upselling tools that showcase premium features without aggressive sales tactics.
The company’s cross-channel approach creates a seamless experience:
- On-site: Real-time AI-powered grammar, tone, and clarity suggestions
- Off-site: Automated weekly email reports with personalised performance data
This dual approach maintains user engagement even when they’re not actively using the product. Have you measured how your automated communications reinforce your product’s core value proposition?
What makes this work: Grammarly drives premium conversions by using AI-powered performance tracking to deliver personalised insights that simultaneously boost engagement and create natural upselling opportunities.
6. Primor – Product Recommendations & Customer Engagement via Web Push & Email
Primor, the Spanish leading beauty retailer, centers its hyper-personaliation strategy on its accurate product recommendation system that works across multiple channels.
When beauty shoppers browse skincare items but leave without purchasing, Primor’s system activates a coordinated re-engagement approach:
- AI-powered product recommendations that analyse browsing behaviour, purchase history, and category affinity
- Web push notifications highlighting these personalised recommendations with time-sensitive offers
- Cart recovery emails featuring abandoned items alongside complementary product suggestions
This multi-touchpoint approach has shown impressive potential for beauty retailers looking to increase conversions and basket size. When implemented effectively, such recommendation systems can significantly impact customer engagement and satisfaction.
How effectively does your recommendation system translate customer browsing behaviour into actionable insights that drive additional revenue?
What makes this work: Primor achieves higher recovery rates by combining intelligent product recommendations with strategic multi-channel delivery that guides customers back to purchase with items they’re genuinely interested in.
Curious about how Primor achieved these results? As one of our valued clients, they’ve seen firsthand how Frizbit’s recommendation engine can transform browsing data into revenue opportunities. Get in touch with our expert team to learn more about their journey.
Asics – Personalised Cart Recovery Emails
Asics is another one of our hyper-personalised content examples on how brands can effectively function as both personal shoppers and salespeople for their online customers through strategic automation.
Their approach focuses on leveraging customer purchase behaviour to deliver timely, relevant follow-ups.
When shoppers add items to their cart but leave without completing the purchase, Asics doesn’t let the opportunity slip away.
Their system automatically triggers high-quality reminder emails specifically designed to bring customers back to complete their transactions.
What sets Asics’ approach apart is the attention to detail in these recovery communications:
- Emails feature the exact abandoned products with clear imagery
- Copy addresses common purchase barriers with targeted messaging
- Timing is optimised based on customer engagement patterns
For eCommerce leaders, this raises an important question: How much revenue are you leaving on the table by not implementing sophisticated cart recovery automation?
What makes this work: Asics transforms lost sales opportunities into completed transactions by deploying personalised, timely follow-ups that address specific customer actions rather than sending generic promotional messages.
8. Cleo – AI Chatbot for Financial Assistance
Cleo, an AI-powered finance assistant, personalises user interactions by analysing:
- Individual spending habits
- Budgeting trends
- Personal savings goals
The chatbot delivers custom financial advice via SMS and in-app messages, helping users manage their finances more effectively.
What makes this work: Cleo’s user retention stems from AI-driven chatbots that provide real-time personalised assistance exactly when users need financial guidance.
9. BiciEscapa – Web Push Notifications for Sales Funnel Retargeting
BiciEscapa, the online bike retailer, has mastered sales funnel retargeting through strategic web push notifications.
Rather than relying solely on ads, social media or email, they’ve implemented a full-funnel notification strategy that responds to specific customer behaviours throughout the purchase journey.
Working with Frizbit, BiciEscapa deployed a structured retargeting framework that covers the complete customer journey:
- Subscription stage: Initial welcome notifications that establish the relationship
- Category browsing: Messages highlighting popular bike categories based on browsing patterns
- Product view stage: Targeted notifications about specific models viewed with compelling features
- Cart abandonment: Timely reminders about products left behind with limited-time incentives
- Post-purchase: Follow-up communications that enhance customer satisfaction and encourage reviews
What separates this approach from basic retargeting is the focus on timing and relevance. Web push notifications are triggered based on engagement recency and browsing depth, ensuring messages arrive when customer interest is highest.
For retailers facing cart abandonment challenges, web push notifications offer a direct re-engagement channel that stands out over crowded email inboxes. How many potential customers never see your recovery emails because they’re buried among dozens of promotional messages?
What makes this work: BiciEscapa captures lost revenue by meeting customers exactly where they left off in the purchase journey with timely, relevant push notifications that create urgency without being intrusive.
10. Delta Airlines – Real-Time Travel Updates
This approach to real-time communication has become increasingly important throughout the airline industry. At Frizbit, we’ve witnessed similar success implementing web push notification strategies for airlines. In a recent proof-of-concept project with a flagship carrier, our personalised messaging approach delivered an impressive 62.9% ROI.
Airlines face unique challenges in customer communication—the need for immediacy, the high value of each conversion, and the complexity of the travel journey all demand sophisticated personalisation solutions.
What makes this work: Delta’s customer satisfaction metrics improve through automated messaging systems that deliver real-time, customer-friendly service updates.
Want to see how web push notifications can work for your airline? Explore our airline solutions or read the complete case study to learn more about our proven approach.
The Future of Hyper-Personalisation: Key Takeaways
From AI-powered recommendations to real-time notifications, hyper-personalisation is revolutionizing how brands build customer relationships. The most successful hyper-personalisation examples share common elements:
- Strategic use of customer data
- Intelligent automation across multiple channels
- Seamless experiences at every touchpoint
- Contextually relevant messaging
By studying these hyper-personalisation best practices, you can develop strategies that resonate with your audience and drive meaningful results.
Implementing your own hyper-personalisation strategy
After examining these successful hyper-personalisation examples, you might be wondering how to implement similar strategies for your business. Here’s a practical framework to get started:
1. Consolidate Your Customer Data
Before you can personalise effectively, you need a comprehensive view of your customers. This means:
- Integrating data from all customer touchpoints (website, app, email, customer service)
- Creating unified customer profiles that update in real-time
- Identifying the most relevant behavioral indicators for your business model
Ask yourself: Do we have visibility into how customers interact across all our channels? If not, start by bridging those data gaps.
2. Segment with Sophistication
Basic demographic segmentation is no longer enough. Modern hyper-personalisation requires:
- Behavioural segmentation based on actions and interests
- Predictive segmentation that anticipates future needs
- Dynamic segments that update automatically as customer behavior changes
The goal is to move beyond traditional audience groups to create fluid segments that evolve with your customers.
3. Prioritise Automation
The examples above all leverage automation to deliver personalised experiences at scale. Consider:
- Which customer journeys could benefit most from automated personalisation?
- What triggers would make personalised communication most relevant?
- How can you use real-time data to make your automation more effective?
Remember that effective automation isn’t about eliminating the human touch—it’s about delivering the right message at moments when manual intervention isn’t the best option.
4. Test and Refine Continuously
Hyper-personalisation isn’t a “set it and forget it” strategy. The most successful implementations involve:
- Continuous A/B testing of personalised elements
- Monitoring key metrics to evaluate impact
- Regular refinement based on changing customer preferences
What works today may not work tomorrow, so build testing into your personalisation roadmap from the start.
Ready to transform your customer experience?
Don’t let your competitors outpace you in the race to deliver exceptional personalised experiences. Frizbit helps brands automate personalised re-engagement without relying on third-party cookies.
Our platform enables you to:
- Create hyper-personalised messaging based on real-time user behaviour
- Deploy cross-channel communication strategies that meet customers wherever they are
- Recover abandoned carts and browsing sessions with targeted, timely messaging
- Analyse campaign performance with detailed attribution reporting
And unlike many personalisation solutions, Frizbit doesn’t require complex implementation or technical resources. Most clients are up and running within days, not months.
Book your free Demo today and discover how our platform can help you implement these hyper-personalisation examples in your business.
Apply for our 30-day risk-free trial and see the impact of hyper-personalisation on your conversion rates, customer engagement, and bottom line. No commitment required, just real results that speak for themselves.
