Solving Complex Product Taxonomy Challenges of Fashion Retail With AI

Online stores might seem like they are easier to manage than physical ones. However, the consequences of not managing the digital product data and online catalogues in a right way can be severe. E-commerce site visitors should be able to navigate easily through your online store catalogue and find what they want quickly. Only by providing a good online customer experience, you will get shoppers to buy.

According to a Forrester report, "poorly architected retail sites" sell only half as much when compared to better structured sites. There are various ways to combat this disorganization, with a simple category classification, better navigation, richer filtering options and straightforward product descriptions.

To organize your e-commerce store better and make it easier for users to find the products they want, you need a well structured product taxonomy. Following your taxonomy when generating product data will help you maintain clean and well organized product catalogues, which will help your users to navigate through your products easily and purchase more quickly.

What is product taxonomy?

Product taxonomy is a well defined data structure of fashion categories and attributes which is supporting the method of categorizing, organizing, and systematically classifying items. That is why it is important to provide a thorough understanding of the products and their meaning to present them in a reasonable order.

The most popular taxonomic model of e-commerce is a hierarchy in which objects are first divided by high level categories (such as men’s clothing or men’s shoes) and then into more narrow categories (like pants, blouses or t-shirts), and ending with more detailed subsets and attributes (like colour, pattern, neckline type, length etc.). The product catalog is divided in this kind of segmentation, to make it more accessible and encourage logical relations between subsections.

For example, if the desired product is a long flowery green dress with short sleeves, the categorization would be:

Home > Women’s clothing > Dresses > Green Dresses (color filter) -> Flowery dresses (pattern filter) -> Short sleeves dresses (sleeves length filter)

What's important is that they're realistic and grounded in the knowledge of consumer behavior. A good retailer does not just group together a few categories that sound appealing. If you don't organize your product catalogue in a manner that allows people to browse easily, even the best website design in the world would not be sufficient for a good customer experience and sales conversion. Customers should be able to understand your product categories, attribution and descriptions, making it easy for them to get to the checkout page.

For online retailers with thousands of items in their catalogs, managing all this product data does not come easy. It comes with the expense of people spending a lot of time manually tagging products with their categories and attribution. Besides being time consuming, this process is also error prone, which may lead to bad customer experience and higher returns.

Product taxonomy and AI-powered automatic tagging

Thanks to the digital revolution and the rise of technologies like Artificial intelligence (AI), this manual process of data input can be made up to 90% more efficient with intelligent automatic tagging systems.

Automatic tagging is an artificial intelligence (AI)-based approach that eliminates the need for manual product tagging. It works by examining an image and identifying features associated with particular keywords, from a predefined fashion taxonomy. Thanks to advanced deep learning technologies, these algorithms speed up the labeling process and eliminate the need for human labeling. Automatic tagging organizes and tags images in the product catalog based on their attributes. This is a method for generating metadata for catalog assets.

Retailers may use these AI tagging tools in e-commerce to construct comprehensive product details, and at the same time save time and spread up time to market. AI-powered automatic tagging streamlines operations, removing crucial manual steps in the product data management, and enabling retailers to concentrate on more critical business decisions.

An automatic tagging engine like the one by Pixyle.ai can make your entire product tagging process smooth and painless, with an extensive fashion taxonomy. The importance of taxonomy isn’t just about tags, it’s also about good organization. Companies like Pixyle have also put a lot of effort into generating rich and detailed taxonomies. By having rich product data, retailers are improving their filtering options, product discovery, textual search, recommendations and sales conversions.

With this kind of automation, the copywriters become up to 90% more efficient. And it’s important to note that an AI-based tagging engine like Pixyle.ai can automate partly their work but can’t replace them entirely.

To find out more about Pixyle.ai, go to https://www.pixyle.ai/