How and why create an online product recommendation system in beauty

76% of consumers are more likely to buy products when offered personalized recommendations and experiences.

And given that consumer preferences are as diverse as the products on offer, an online product recommendation system has become more than just a luxury – it’s a necessity.

So, as consumer preferences continue to diversify, matching them with the right products through AI-driven personalization is crucial. This approach lies at the very core of the hyper-personalization trend that’s dominating the sector.

And ultimately, the question now is not if, but how to effectively implement and leverage product recommendation systems to meet the rising demand for customized beauty solutions. 

So, let’s explore the strategy and significance behind adopting such a system in beauty.

Why hyper-personalization?

Before we delve into the product recommendation system itself, we have to say a few words about this all-encompassing trend.

Hyper-personalization transcends traditional marketing by merging individualized engagement and product suggestions.

In the context of the beauty industry, the role of hyper-personalization becomes not just beneficial, but essential for success. And the reason is simple – the personal connection between a consumer and a product is paramount.

Why? Because the beauty industry’s diverse consumer base has an equally diverse set of needs, preferences, and expectations.

Traditional one-size-fits-all approaches are, therefore, increasingly inadequate, as they fail to recognize and address the nuances of individual consumer profiles.

Hyper-personalization addresses this gap by enabling brands to craft experiences and recommendations that resonate on a personal level, significantly enhancing customer satisfaction and loyalty.

It ensures that customers find products that complement their skin tone, face shape, and other personal attributes, leading to a more satisfying and effective shopping experience.

Here’s how product recommendations can be personalized using beauty AI tools.

✧ Skin tone and personalized product recommendations

While the beauty industry has seen the emergence of foundation-matching tools, many fall short in terms of user-friendliness, accuracy, and inclusivity. Recognizing these challenges, Arbelle introduces a groundbreaking Shade Finder. 

This tool is the first in the industry to leverage the comprehensive Monk Skin Tone Scale, simplifying and personalizing the foundation selection process like never before. 

By accurately matching a customer’s skin tone against this comprehensive scale, our system can suggest the most flattering foundation shades from your product collection. This tool ensures that every recommendation is deeply personalized, catering to the diverse beauty needs of all customers. 

It exemplifies our commitment to inclusivity and precision in beauty, ensuring that each individual finds products that truly resonate with their unique skin tone, enhancing their natural beauty and confidence.

✧ Face shape and personalized product recommendations

Face shape detection tools leverage AI and computer vision to analyze facial features (face, lips, eyes, brows) and detect shapes. This, in turn, makes it possible for the recommendation of looks and specific makeup products to be fully tailored to enhance or balance specific shapes. 

Moreover, an unmatched benefit of this tool is that it recommends not only which product to use but also how it should be applied to make specific face features more or less prominent.

This level of personalization ensures that recommendations go beyond generic suggestions. It offers looks that customers might not have considered but are perfectly suited to their unique facial structure.

✧ Seasonal colors and personalized product recommendations

Personalized looks based on the seasonal color analysis system, considering skin tone, hair color, eye color, and face shape, will revolutionize how recommendations are made. 

This approach allows for the recommendation of products and looks that not only suit the individual’s physical attributes but also align with color theory principles for harmonious and flattering aesthetics. 

And with Arbelle’s seasonal color system, powered by AI and computer vision, your customers will get their ideal looks instantly – after a quick face scan and without the need for a beauty advisor.  

Ultimately, this offers a brand-new dimension of personalization in beauty retail. 

The role of technology in hyper-personalization

The advent of advanced technologies has been a game-changer in achieving hyper-personalization in the beauty industry.

AI and machine learning algorithms play a pivotal role in analyzing vast amounts of data to identify patterns, preferences, and potential needs of individual consumers. This technology can process information from various sources, including direct customer interactions, social media behavior, and even real-time environmental factors, to make highly accurate product recommendations.

And we mustn’t forget about the crucial role of computer vision here, either. When it comes to product recommendations, computer vision is invaluable due to the user-friendliness and wealth of information it can instantly extract from a picture of a face.

Furthermore, augmented reality (AR) has transformed the try-before-you-buy experience, allowing customers to see how products will look on them before making a purchase.

This technology, combined with computer vision and AI-driven insights, can offer personalized recommendations on shades of makeup or types of products that will best suit the customer’s facial features, skin tone, and personal style.

And within this beauty industry’s transformation – merging hyper-personalization, AI, AR, and computer vision – we see a shift toward personalized recommendation systems.

Implementing an AI-driven online product recommendation system

According to a report by Mordor Intelligence, the recommendation engine market is expected to reach $28.7 billion by 2029. And this is at a CAGR of 33.06%, seeing as currently, in 2024, the market value stands at $6.88 billion. 

Slowly but surely, therefore, a growing number of businesses are implementing a digital transformation strategy, with AI technologies at the heart of it. The focus is, of course, on improving the customer experience, learning about their needs and preferences, and, ultimately, improving sales. 

An AI product recommendation system stands out as the perfect solution to achieve all of this. And creating one involves several key steps, each crucial for delivering accurate and relevant product suggestions.

➥ Building the foundation

Implementing an online product recommendation system starts with a robust foundation of data collection and analysis. By understanding customer demographics, preferences, and specific beauty-related attributes through advanced technologies like skin tone detection and face shape finders, brands can offer highly personalized product recommendations.

➥ Harnessing the power of AI and machine learning

The integration of AI and machine learning into your beauty brand’s webshop marks a revolutionary step in personalized online shopping. And one of the standout applications of AI here is the virtual try-on feature, which sets a new standard for personalization in the beauty sector. 

From the moment customers land on your site, AI and machine learning work together to deliver personalized product suggestions and immersive try-on experiences, ensuring that each recommendation is tailored to their unique beauty profile.  

Integrated within a virtual makeup try-on, product recommendations become even more accurate and personalized as they factor in the visual compatibility of products with individual consumers.

➥ Continuous improvement through customer feedback

An effective recommendation system evolves by integrating customer feedback and insights, refining its accuracy and relevance over time. This dynamic approach ensures that recommendations stay aligned with changing consumer preferences and emerging beauty trends.

Why prioritize product recommendations?

For beauty brands, making product recommendations a cornerstone of their technological advancement is not just beneficial—it’s strategic. Here are several compelling reasons.

✧ Discoverability and complementary products

With extensive product catalogs, beauty brands face the challenge of ensuring that customers can easily find products that suit them. A recommendation engine can surface ideal products and combinations, enhancing discoverability and suggesting complementary items, like the perfect shade of lipstick to match a foundation.

✧ Cross-selling and upselling opportunities

Beauty brands uniquely benefit from the ability to cross-sell by suggesting additional products and upsell by recommending premium alternatives. This dual strategy opens up multiple avenues to increase revenue.

✧ Assistance and personalization

Much like an in-store expert, a well-programmed product recommendation system can offer guidance and make suitable recommendations, providing a level of personalized assistance that today’s beauty consumers appreciate.

✧ The need for enhanced product recommendations

While many brands employ product recommendation engines to upsell and cross-sell, there’s a noticeable gap in refinement, relevancy, and effectiveness. The opportunity to innovate is vast, with the potential to leverage AI for creating true 1-to-1 recommendations that resonate with individual customers throughout their journey, not just as a one-off interaction.

All in all: Why Implement an AI product recommendation system?

The benefits of implementing an AI personalized recommendation system are manifold, impacting both the brand and its customers.

✧ Brands and personalized product recommendations

✧ Customers and personalized product recommendations

Step ahead with a smart product recommendation system

The implementation of an online product recommendation system is a strategic move for beauty brands aiming to stay competitive in a highly personalized market.

By leveraging AI and machine learning for personalized product recommendations, brands can enhance shopping experiences, foster loyalty, and drive sales.

For customers, these systems provide a level of service and personalization that was once unimaginable. This, in turn, makes it easier and more enjoyable to find products that truly suit their individual needs and preferences.

So, in the ever-evolving landscape of beauty retail, personalized recommendation systems stand out as a key differentiator, driving the industry toward a more customer-centric future.

At Arbelle, we specialize in leading the charge toward unparalleled personalization in the beauty industry. Our top-notch solutions, such as virtual makeup try-on and AI shade finder, are grounded in the principles of hyper-personalization. They’re powered by the latest computer vision and AI technology, designed to transform your approach to customer engagement and satisfaction.

Contact us today, and let’s start driving your brand towards an even more successful, personalized future.