Answear.com is one of the fashion e-commerce leaders in CEE with over 400 brands, operating in 10 European markets, achieving PLN 683M revenue in 2021 ($164M).

The challenge: Answear.com outfit inspiration on e-commerce

The challenge Answear team faced was to streamline users’ discovery among over 100.000 products available on e-commerce. 

The company was looking for an opportunity to create a space that will provide inspiration and enhance user interaction with the platform.


Answear’s goal was to increase user engagement in the shopping experience, resulting in increased average order value (AOV) and increased conversion rate (CR). Understanding of the context and common goals led us to the implementation of the getdressed AI styling system on Answear product pages.

The product: Artificial Intelligence for outfit creation

The plan was to implement a system of outfit inspirations on the website. Answear.com already tried the manual creation of dedicated outfits and adding them to the website, however, this approach was not optimal.

Our AI styling technology was creating up to 5 outfits for each product. The outfits were in different styles, prepared for different customer personas, and consisted of 4 products including the main browsed one.


Check out outfits generated by getdressed for Answear.com in real-time. Mobile-version, womenswear: 

he solution: Dynamically created outfit recommendation


Thanks to the getdressed system we were able to offer dynamically created unique recommendations for more than 840.000 sessions for customers visiting the store. This enabled users to move from navigating products in a given eCommerce store to navigating styles and selecting the most inspiring ones.

During the test period, Answear.com noted the rise of the average order value (AOV) by over 5% and the conversion rate by 2%


By using the getdressed solution, we changed the static structure of the product page into a place where users can get inspiration and better understand if certain products fit their style. This allows for more interaction and improves the shopping experience for each user.


Check out outfits generated by getdressed for Answear.com in real-time. Web-version, menswear & womenswear: 

Results after implementation of getdressed AI styling technology:

average shopping cart value (AOV) increased by more than 5%

4 MLN of outfits generated for 840K users sessions

increased user engagement in the purchase process by giving them an additional inspirational element for interaction

facilitated the discovery of products from the so-called long tail for each user by increasing the variety of products viewed by them

Brands cross-presented in outfits, brands available on Answear.com:

Simultaneously to the test performed on the Hungarian market, the getdressed solution was also implemented on the Slovakian market. In the Slovakian market, we did not see the expected increases, so the getdressed team together with the Answear team had a discussion on what this might be due to and what market-specific consumer behavior might have influenced this. Therefore the PoC stage will be repeated for 3 months, new UX solutions proposed by the Answear.com team, using getdressed technology will be applied.

Next steps: Discovery of new spaces to introduce AI styling technology to users

By inspiring, we allowed for deeper interaction between the user and the store. The collaboration with Answear.com will be continued, as the company decided to search for more spaces on e-commerce, where our technology can bring value to the users.

We conducted the test simultaneously on the Hungarian and Slovakian markets. In our subjective evaluation, the presented recommendations were valid and mostly in line with the style of the presented product. The results obtained in the Hungarian market show the potential of the solution. On the Slovak market, we did not obtain similar increases during the test. The relatively short testing period might have been influenced by other factors, so we will want to repeat the test while introducing some changes in the way we present the recommended products.

We see great potential in this type of solution for our users.


From a technical point of view, the preparation and operation of the test went flawlessly. During the test, the product selection mechanism was stable, reliable and we did not observe any performance problems. We believe that further optimization would bring even better results.

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