Cosmetics eCommerce Store

A customer visits your cosmetics eCommerce store looking for a moisturizer for dry, sensitive skin. They know what they need, but they do not know which product is right for them. They search, browse a few categories, apply filters and open several product pages. Several products seem relevant, but none gives them enough confidence to make a decision.

After several minutes, they leave without buying.

Table of Contents

There was no checkout error. The website was available, the products were in stock and the customer could technically complete the purchase. The problem happened much earlier in the journey. The store made it difficult to discover the right product.

This is a particularly important problem for cosmetics and beauty eCommerce because customers do not always shop by product name. They may search by skin concern, hair type, ingredient, shade, finish, formulation or the result they want to achieve. As the product catalog grows, connecting those needs with the right products becomes increasingly difficult.

For an eCommerce team, this can be easy to overlook. The store may have hundreds or thousands of products, detailed product pages and a working search function, yet customers can still struggle to find what they are looking for.

The question is therefore not simply whether your store has enough products.

Can customers actually find the right one?

Why Product Discovery Is Different in Cosmetics eCommerce

Product discovery in cosmetics is rarely as straightforward as searching for an exact product specification.

A customer shopping for a laptop may already know the processor, memory or storage they want. A cosmetics customer may start with a problem rather than a product.

They may be looking for something to help with dry skin, uneven skin tone, frizz, hair damage or a particular makeup finish. They may know the ingredient they prefer but not which products contain it. A customer choosing a foundation may need to consider shade, undertone, coverage and finish before deciding which product is suitable.

This creates a gap between how customers describe their needs and how eCommerce catalogs are structured.

A retailer may organize products around categories such as moisturizers, serums, foundations and cleansers. The customer may think in terms of "something lightweight for oily skin" or "a serum for dark spots."

If the eCommerce experience cannot connect those two ways of thinking, customers are left to do the work themselves.

This is why product discovery becomes an eCommerce technology and experience problem rather than simply a navigation problem.

1. Your Product Catalog May Not Reflect How Customers Search

One of the first places to investigate is the product data behind the store.

A cosmetics product can have dozens of characteristics that influence whether it is relevant to a particular customer. Product type is only one part of the picture. Ingredients, skin type, concern, shade, undertone, finish, formulation, size, fragrance and other attributes may all influence the buying decision.

If those characteristics are stored inconsistently, missing from the catalog structure or only included inside long product descriptions, the eCommerce system may struggle to use them effectively.

For example, imagine a customer searches for a "fragrance-free moisturizer for sensitive skin." If fragrance information and skin-type compatibility are not structured as usable product attributes, the store may have relevant products but still struggle to identify them.

This is why product information is more than content that appears on a product page. It is data that can influence search, filtering, merchandising, recommendations and other parts of the shopping experience.

For larger catalogs, a Product Information Management (PIM) system can help businesses maintain structured product information and distribute it consistently across eCommerce and other sales chann

The technology itself is not the objective. The objective is to make product information structured enough for both customers and eCommerce systems to use effectively

2. Your Search May Understand Product Names Better Than Customer Intent

A search box can be working perfectly from a technical perspective and still provide a poor customer experience.

Consider a customer searching for:

  • "lightweight moisturizer for oily sensitive skin

The customer is expressing several requirements at once. They are not necessarily looking for a product whose name contains those exact words.

A basic keyword-based search may return products containing "moisturizer" while giving little importance to "oily," "sensitive" or "lightweight."

That creates a discovery problem.

Modern eCommerce search can use product attributes, synonyms, relevance rules and semantic or AI-assisted techniques to interpret more complex searches and return more relevant products. But the quality of the result still depends on the information available behind the search system.

This is why simply installing a more advanced search solution does not automatically solve poor product discovery.

The search technology needs to understand the structure and meaning of the catalog.

3. More Products Can Make Product Discovery Harder

Growing the catalog is usually a positive sign for a cosmetics retailer. More products can attract more customers and address more needs.

But more products also create more choices.

A customer looking for a moisturizer may now have dozens of options. If the store presents them all without helping the customer narrow the selection, the larger catalog can make the shopping experience more difficult rather than easier.

Filters can help, but only when they reflect the way customers actually make decisions.

For cosmetics, useful filtering may involve attributes such as:

  • Skin type
  • Skin concern
  • Ingredients
  • Product type
  • Finish
  • Shade
  • Hair type
  • Formulation
  • Fragrance
  • Price

The most useful filters will depend on the product category, catalog structure and the decisions customers need to make before purchasing.

The important point is that filters should reduce uncertainty, not simply expose every attribute stored in the database.

Merchandising can also help eCommerce teams control how products are presented for particular searches, categories or campaigns. This becomes increasingly important when the catalog contains similar products that need to be differentiated by customer need.

4. Your Product Page May Not Answer the Questions Customers Have

A customer can find the right category and still fail to find the right product.

This often happens when product pages provide plenty of information but not the information the customer needs to make a decision.

Think about what happens in a physical beauty store. A customer can ask a beauty advisor questions such as:

"Is this suitable for sensitive skin?"

"What finish does it give?"

"Will this work with the products I already use?"

"Which shade should I choose?"

"Is this better for dry or oily skin?"

A standard product page cannot have a conversation with every customer.

But it can be designed to answer the most important questions clearly.

For example, a foundation product page may need to make shade, undertone, coverage, finish and skin-type information easy to understand. A skincare product may need to explain ingredients, concerns, formulation and usage. A haircare product may need to make hair type and treatment suitability much clearer.

The challenge is therefore not simply adding more content to the product page. It is structuring and presenting the information customers need at the point where they are making a purchase decision.

5. Recommendations Should Help Customers Decide, Not Just Sell More Products

Product recommendations are often introduced as a way to increase average order value, but their role in cosmetics eCommerce can be broader.

A customer who has found one product may still need help understanding what else is relevant to their routine.

A skincare journey might naturally move from:

Cleanser → Serum → Moisturizer

A makeup journey could move from:

Foundation → Concealer → Setting Product

Recommendations can help customers discover those relationships without requiring them to search through the entire catalog.

However, recommendations become less useful when they are generic.

Showing the same "You may also like" products to every visitor does little to address an

individual customer's needs.

Product recommendations can become more relevant when they take product attributes, customer behavior, purchase history and context into account.

This is where personalization technology and recommendation engines can support product discovery.

The objective should not be to show customers more products. It should be to help them discover products that are more relevant to their needs.

6. Reviews Can Help Customers Discover the Right Product

Reviews are usually discussed as a trust or conversion element, but they can also support product discovery.

A customer may not simply want to know whether a product has a four-star rating. They may want to know whether it worked for someone with similar requirements.

For example:

  • Did it work for dry skin?
  • Was the foundation suitable for oily skin?
  • Did the shade look as expected?
  • Was the fragrance noticeable?
  • Did customers find the product lightweight?
  • Did it work well with a particular routine?

This makes the content inside reviews potentially valuable.

A review platform that allows customers to search, filter or surface relevant feedback can help shoppers find information that matters to their particular decision.

User-generated photos and videos can provide another layer of context, particularly for products where appearance, shade or finish is difficult to evaluate from standard product photography.

The technology behind reviews and UGC therefore deserves consideration as part of the wider product discovery experience, rather than treating reviews as an isolated widget placed at the bottom of a product page.

7. AI Can Help When Customers Describe What They Need

AI becomes particularly useful when customers can describe what they need but do not know the name of the product they are looking for.

Imagine a customer typing:

  • "I need a lightweight moisturizer for sensitive skin that doesn't feel greasy."

This is not a conventional product-name search.

An AI-assisted discovery experience can interpret the customer's description and connect it with relevant product attributes, catalog information and recommendations.

The same approach could help with questions around skincare routines, makeup preferences, hair concerns or product combinations.

But AI should not be treated as a replacement for good eCommerce fundamentals.

If product information is incomplete, attributes are inconsistent or the catalog is poorly structured, an AI layer may have less reliable information to work with.

That means AI product discovery should sit on top of a strong product data and eCommerce foundation rather than being used to hide weaknesses underneath it.

8. The Storefront Can Become Part of the Discovery Problem

Even when the underlying product data and search technology are working correctly, the storefront can make discovery unnecessarily difficult.

Customers may struggle with filters on mobile devices, category navigation may require too many clicks, product information may be difficult to scan or interactive elements may make the page slow to respond. When these issues are accompanied by slow page responses or poor frontend performance, Magento performance optimization can help identify whether the underlying implementation, custom functionality or third-party integrations are contributing to the problem.

For businesses using Magento or Adobe Commerce, frontend architecture becomes particularly important as the store adds more functionality to the shopping experience. A well-planned Magento website development approach can help ensure that catalog navigation, search, filtering and other customer-facing functionality work together as the store evolves.

Search interfaces, filters, recommendations, product configurators and other components all add complexity to the storefront.

This is where a frontend approach such as Hyvä Theme may be relevant for businesses evaluating their Magento or Adobe Commerce architecture. The decision should depend on the existing implementation, functionality requirements and broader technical roadmap rather than simply replacing the frontend for the sake of using a different technology.

Navigate Commerce is a Hyvä Gold Partner, so Hyvä can be considered alongside the wider Adobe Commerce implementation when it is relevant to the store's requirements.

For businesses also reviewing the broader customer journey as part of a frontend modernization project, the checkout experience should be considered alongside product discovery rather than treated as a completely separate part of the storefront.

How to Identify a Product Discovery Problem

You do not necessarily need to wait for customers to complain before investigating product discovery.

Your existing eCommerce data can provide useful clues.

Start with your internal search data. Look for searches that frequently produce no results, searches that lead to very few product views, and search terms that differ significantly from the product names, categories or attributes used in your catalog.

Then look at category and product-page behavior. If customers are repeatedly using filters but rarely progressing to product pages or adding products to their carts, the filtering and discovery experience may not be helping them enough.

Customer support conversations can provide another useful signal. If shoppers regularly ask which product is suitable for a particular skin type, concern, shade or routine, that information may indicate that the website is not answering an important product- selection question.

Analytics can then help connect these observations with actual customer behavior.

The objective is not to assume that every low-converting product page has a discovery problem. It is to identify patterns that suggest customers are struggling to move from a need to a suitable product.

What to Check Before Changing Your eCommerce Technology

Finding a product discovery problem does not automatically mean that you need a new search engine, recommendation platform or eCommerce platform.

Before making a technology decision, look at the complete journey.

Start with product data.Are important attributes structured consistently across the catalog?

Then review search. Can customers search using the language they naturally use, or does the system depend heavily on exact product names?

Review navigation and filters. Do they help customers narrow choices, or do they simply expose a long list of attributes?

Examine product pages. Do they answer the questions customers need to make a purchase decision?

Look at recommendations and personalization. Are they based on meaningful product and customer information?

This sequence matters because replacing one component without understanding the wider architecture can simply move the problem somewhere else.

Product discovery problems can originate in different parts of an eCommerce implementation. The issue may involve product data, catalog architecture, search configuration, custom development, third-party integrations, frontend performance or the way multiple systems exchange information.

Navigate Commerce works with Magento and Adobe Commerce stores to investigate eCommerce challenges like these and identify where the customer journey is being affected.

Our work can include reviewing the existing eCommerce architecture, catalog and product attributes, search and filtering behavior, custom functionality, third-party integrations and frontend implementation.

For businesses using Magento or Adobe Commerce, this can also include evaluating whether the current storefront architecture is appropriate for the experience the business is trying to create. Where relevant, our Hyvä Gold Partner expertise allows us to assess Hyvä Theme and related Hyvä solutions as part of the wider technical roadmap.

The important starting point is not deciding which technology to buy.

It is understanding why customers are struggling to find the right products in the first place.

Once the underlying problem is clear, the appropriate solution may involve improving product data, changing search behavior, restructuring navigation, introducing personalization, improving the frontend or integrating a new technology into the existing Adobe Commerce ecosystem.

Conclusion

A cosmetics eCommerce store can have a large catalog, attractive product pages and a technically functional checkout and still make shopping difficult.

The problem may begin much earlier.

Customers often start with a need rather than a specific product name. They may know their skin concern, preferred ingredient, desired finish or hair type without knowing which product will meet that requirement. If the eCommerce experience cannot connect that need with the right product, customers are left to do too much of the discovery work themselves.

As the catalog grows, this becomes harder to manage.

Product information, search, filters, merchandising, recommendations, reviews, personalization, AI and storefront architecture all influence how easily customers can move from "This is what I need" to "This is the product I should buy."

That does not mean every cosmetics retailer needs to implement every available technology.

It means the technology behind the store should support the way customers actually search, evaluate and choose products.

If customers are visiting your cosmetics eCommerce website but still struggling to find the right products, the problem may not be the size of your catalog. It may be the technology, product data and structure connecting that catalog to the customer.

FAQs

Why is product discovery important for cosmetics eCommerce?

Cosmetics customers often search based on needs, concerns, ingredients, shades, finishes or other attributes rather than exact product names. A strong product discovery experience helps connect those requirements with relevant products and reduces the effort required to make a purchase decision.

What makes product discovery difficult for cosmetics websites?

Large catalogs, inconsistent product attributes, weak search relevance, limited filtering, generic recommendations and product pages that do not answer customer questions can all make discovery more difficult.

How does product data affect eCommerce search?

Search depends on the information available in the product catalog. If important attributes such as ingredients, skin type, shade or formulation are missing or inconsistently structured, search and filtering may have difficulty identifying relevant products.

Can AI improve product discovery for cosmetics?

AI can help interpret natural-language customer requests and connect them with relevant products, particularly when shoppers describe a need rather than a specific product name. However, AI works best when the underlying product information is accurate and well structured.

Does personalization help cosmetics eCommerce?

Personalization can make product discovery more relevant by using information such as browsing behavior, purchase history, customer preferences and product relationships. It should be introduced where there is a clear customer or business use case rather than simply added as a feature.

Is Magento or Adobe Commerce suitable for cosmetics eCommerce?

Magento and Adobe Commerce can support cosmetics retailers with complex catalogs, custom functionality, integrations and advanced eCommerce requirements. The appropriate platform and architecture depend on the size and requirements of the business.

Can Hyvä improve a Magento or Adobe Commerce storefront?

Hyvä can be considered as a frontend approach for Magento and Adobe Commerce stores where the business is reviewing storefront architecture, performance, maintainability and customer experience requirements. Its suitability should be evaluated against the existing implementation and project requirements.