Industry-Specific Personalisation Examples That Drive Real Results in 2025

In today’s digital-first world, personalisation is no longer a nice-to-have—it’s an expectation. But personalisation looks very different depending on the industry. Beauty brands use augmented reality to let shoppers try products virtually. Luxury houses rely on AI concierges to give VIP treatment. Streaming platforms serve up content you didn’t even know you wanted. Retailers predict your next purchase before you do. And travel companies create custom packages tailored to your style and budget.

In this article, we’ll explore five key industries where personalisation is driving growth, with real examples, hard data, and lessons you can apply to your own business.

1. Beauty Industry: Virtual Try-Ons & AI Skin Analysis

The beauty sector has embraced AI-driven personalisation faster than most industries. Generative AI and AR tools allow brands to tailor marketing messages and experiences at scale.

• Virtual try-ons let customers see how makeup or hair colour will look on them in real time.
  
• AI skin analysis recommends skincare routines based on uploaded photos and lifestyle questionnaires.
  

Studies show that these tools can boost conversion rates by up to 40% and reduce product returns by 25% (Exploding Topics, 2024; McKinsey, 2025) (explodingtopics.com; mckinsey.com).

Example: L’Oréal’s ModiFace technology allows shoppers to test products virtually, both online and in-store, increasing purchase confidence and reducing hesitation.

Lesson for other industries: Give customers a way to experience your product before buying, even if it’s a digital simulation.

2. Luxury Sector: AI Concierge & Hyper-Personal Service

In luxury markets, personalisation is about exclusivity, attention to detail, and emotional connection.
LVMH’s internal AI tool, MaIA, handles over 2 million requests per month from 40,000 employees across 75 brands (Pymnts, 2025). It assists with:

• Personalised marketing campaign development.
  
• Price optimisation by region and customer profile.
  
• Product design based on customer feedback.
  

Example: Dior uses customer purchase history to send VIP clients early invitations to private events or limited edition releases.

Lesson: In high-end markets, personalisation is not just about the product—it’s about creating memorable experiences.

3. Streaming Platforms: Tailored Content Recommendations

For streaming services, personalisation is the core of their business model.
Netflix’s recommendation algorithm uses your viewing history, ratings, and behaviour of similar users to suggest what to watch next. This system saves the company over USD 1 billion annually in customer retention (Netflix Tech Blog, 2025) (netflixtechblog.com).

Example: Spotify offers playlists like “Discover Weekly” and “Daily Mix,” making users feel like the service truly understands their taste.

Lesson: Great recommendations combine personal data with trending content to keep experiences fresh yet familiar.

4. Retail & eCommerce: Predictive Product Suggestions

Retailers and eCommerce giants like Amazon use AI recommendation engines to drive sales. These personalised suggestions account for around 35% of Amazon’s total revenue (amitysolutions.com).

Example: Walmart personalises its online store homepage for each customer, showing items they’re likely to need based on season, location, and shopping history.

Lesson: Behavioural data + contextual data = powerful personalisation that feels relevant and timely.

5. Travel Industry: Bespoke Packages & Dynamic Pricing

Travel companies use personalisation to improve both bookings and satisfaction.
Expedia leverages AI to analyse customer preferences and past trips, then offers travel packages that align with budget, destination preferences, and timing. AI forecasting has improved by 15–20%, helping the company optimise pricing and availability (AInvest, 2025).

Expedia even offers an Instagram Trip Matching feature—turning social media reels into bookable travel itineraries (expedia.com).

Example: Airbnb uses past searches and seasonal availability to recommend destinations and activities tailored to each user.

Lesson: Anticipating a traveller’s needs—even before they start searching—can significantly improve conversion rates.

6. Ethics & Privacy in Personalisation

Across industries, trust is the foundation of effective personalisation. Brands that excel at it generate 40% more revenue than their competitors (aws.amazon.com). But without clear boundaries, personalisation can feel invasive.

Customers expect:

• Transparency about how their data is used.
  
• Control over what data they share.
  
• Fairness in algorithms to avoid bias.
  

As Mr. Phalla Plang, Digital Marketing Specialist, I believe:

“Personalisation works best when it’s respectful. If customers feel you’re protecting their data, they’ll reward you with loyalty and more useful insights.”

7. Tools That Power Personalisation Across Industries

No matter your sector, these tools help deliver targeted, engaging experiences:

• Dynamic Yield – Real-time personalisation for retail, travel, and finance.
  
• Salesforce Marketing Cloud – End-to-end customer journey personalisation.
  
• Optimizely – Experimentation and personalisation at scale.
  
• Bloomreach – AI-powered commerce experience platform.
  
• Segment – Customer data platform for organising and activating first-party data.
  

8. Key Lessons for Businesses

• Beauty: Offer interactive experiences before purchase.
  
• Luxury: Make every interaction feel exclusive.
  
• Streaming: Keep recommendations fresh but personal.
  
• Retail: Use behaviour and context to predict needs.
  
• Travel: Anticipate demand and personalise before the search begins.
  

9. The Future of Industry Personalisation

In the next few years, expect:

• Immersive experiences – AR/VR shopping, virtual tourism.
  
• Predictive personalisation – Anticipating needs with minimal input.
  
• Ethical AI frameworks – Transparent, fair, and bias-free personalisation.
  

Brands that master these approaches will not only grow revenue but also secure customer loyalty for the long term.

References

AInvest. (2025). Expedia AI-driven surge: Blueprint for future travel tech. Retrieved from https://www.ainvest.com/news/expedia-ai-driven-surge-blueprint-future-travel-tech-2508/
Amity Solutions. (2025). Amazon’s AI retail strategy. Retrieved from https://www.amitysolutions.com/blog/amazon-ai-retail-strategy
AWS. (2025). Revenue management and the role of personalisation. Retrieved from https://aws.amazon.com/blogs/industries/revenue-management-and-the-role-of-personalization/
Exploding Topics. (2024). Beauty gadget trends. Retrieved from https://explodingtopics.com/blog/beauty-gadgets-trends
Expedia. (2025). Trip matching feature on Instagram. Retrieved from https://www.expedia.com/newsroom/now-live-expedia-launches-industry-first-feature-that-turns-reels-on-instagram-into-bookable-travel-itineraries/
McKinsey. (2025). How beauty players can scale GenAI in 2025. Retrieved from https://www.mckinsey.com/industries/consumer-packaged-goods/our-insights/how-beauty-players-can-scale-gen-ai-in-2025
Netflix Tech Blog. (2025). Netflix personalisation at scale. Retrieved from https://netflixtechblog.com
Pymnts. (2025). LVMH deploys AI tools across operations. Retrieved from https://www.pymnts.com/artificial-intelligence-2/2025/lvmh-deploys-ai-tools-across-operation-seeking-efficiency-and-customer-retention/