Part 2: Ultra Personalized Product Recommendations

In this 10-part series, we will explore how AI is shaping Beauty and Fashion. Today we'll take a look at Part 2: Ultra Personalized Product Recommendations.

For a moment, close your eyes and imagine this - you step into a futuristic beauty store. A QR code instantly personalizes your shopping experience, keeping track of the products you view and your favorites. Using a tablet with augmented reality capabilities, you can virtually test products and pick your preferred color matches. Based on your interactions, the store provides custom product suggestions and offerings.

The future is closer than you imagined. For example, MAC Innovation Labs is already bringing experiences like this to life. It's not just about online clicks anymore. Now, both physical and virtual interactions shape a fluid shopping experience and more personalized recommendations.

So, what enables this ultra-personal experience? Today, we’ll explore how traditional recommendation systems work and how advancements such as Deep Learning is changing the game.

The Tech Behind the Glam

Content-Based Filtering

Let’s start with Content Based Filtering, a more traditional recommendation system.

Birchbox, a beauty subscription service, applies this approach. They use specific customer details like skin type, hair type, and style preferences to curate personalized monthly beauty boxes. Every box is tailored to match the unique profile of each subscriber. Product selection is based on the content (or attributes) specified in the customer details.

Source: Birchbox

Collaborative Filtering

Another widespread approach is Collaborative Filtering. Unlike basing recommendations on product features, this approach offers suggestions based on the preferences of similar users. A classic example is Netflix. Suppose you and your friend Jen have overlapping movie tastes. If Jen watches a new movie, it's likely to be recommended to you, thanks to the similarities in your viewing patterns.

Source: Netflix

Deep Learning

But what's truly changing the game is the rise of Deep Learning Recommendation Systems (DLRS). Platforms like YouTube harness DLRS using watch history, video imagery, user interactions, among many other inputs, to predict the next video you'd like to see. The magic of DLRS? It can dive deep into complex, varied data, spotting intricate patterns to better forecast user preferences.

Source: MAC Innovation Lab

The Power of Advanced Product Recommendation

For forward-thinking beauty retailers like MAC Innovation Labs, the potential of Deep Learning is enormous. Customers get an experience fine-tuned to their tastes, feeling as if a personal shopper is guiding them. For brands, this is revolutionary. They can connect more profoundly with customers, fostering loyalty and efficiently showcasing products that resonate with individual preferences. It's a win-win: brands understand and serve their audience better, while customers enjoy a shopping experience that feels truly personalized.

Final Thoughts

As technology continues to advance and our digital interactions grow, the horizons of ultra-personalized recommendations are expanding. The day might not be far when AI consistently suggests the pair jeans that perfectly hugs your shape or your iconic signature shade of lipstick. Are you ready for this future? We'd love to hear your thoughts.