Visual search is playing an increasingly central role in e-commerce. Thanks to artificial intelligence, it helps customers to find their desired products quickly and efficiently by enabling them to search using photos and discover visually similar items. This AI-based visual search leads to a seamless shopping experience, increases customer satisfaction through faster searches and increases conversion rates through more accurate recommendations.

Request advice now

Ecommerce IT ‐ 11. July 2024

Visual search in use

Many e-commerce companies are already using visual search to improve the shopping experience and simplify product searches. This technology is particularly widespread for furnishings and clothing, but it is also becoming increasingly relevant in other sectors. Some prominent examples of the implementation of visual search are:



Customers upload photos of items of clothing and receive similar product suggestions from the range.


The “StyleSnap” function allows customers to upload a photo of a fashion style, whereupon Amazon displays similar items.


IKEA’s “Place” app allows customers to virtually place furniture in their home and find similar products based on uploaded images.


The ASOS “Style Match” function helps customers to find similar fashion items based on photos.

H&M and Forever 21

Both companies have integrated a visual search function into their apps to make it easier for customers to find fashion items using photos.

All companies benefit from higher conversion rates, as visual search significantly improves the shopping experience by making it quicker and easier for customers to find exactly what they are looking for. This increases satisfaction and leads to more loyalty and repeat purchases, which ultimately increases sales.

New approach: data vectorization

Traditional visual search methods such as manual tagging and simple classification models are often inaccurate, time-consuming and difficult to scale. Manual tagging requires each image to be manually tagged with descriptions, which is very time-consuming with large amounts of data. Simple classification models categorize images based on a few features, which often leads to inaccurate search results.
Data vectorization offers a solution here by converting images into numerical vectors that precisely represent their essential features. By using AI and machine learning, search results become more relevant and the processing of large volumes of images becomes more efficient. This enables companies to perform faster and more accurate visual searches that meet the requirements of modern databases and scale with them.

Visual search in e-commerce: your new strategy for success

Medienwerft positioned itself early on to meet the growing challenges in the field of machine learning and visual search. We develop tailor-made solutions based on state-of-the-art data vectorization technology to help our customers make their visual search more efficient and precise. Thanks to our expertise, companies can make the most of the latest developments in visual search and strengthen their competitiveness.
AI chat, content generation or AI-supported process optimization: where do you want to start?

Request AI advice now!

    Get in touch with us

    All fields marked with * are required for ordering and processing. Your person-related data will be used by us solely for the purpose of processing your inquiry according to our privacy policy.


    Find out everything important about Medienwerft – experts in brands & ecommerce for over 25 years – here:

    About us


    „Behind every impressive online solution there is a well thought out technological concept. My team of system analysts, database experts, front-end developers, back-end professionals and experienced designers ensure that everything runs smoothly. Feel free to talk to us.“

    Frank Meier

    Managing Director

    Tel: +49 40 / 31 77 99-0