March 4, 2021
Shopping has been experiencing a transformation, and the secret to survival has come from consumer experience. Sellers are launching new tools and programs, one after another, to meet customer needs. Yet, shoppers keep encountering one common problem: relevant product search.
According to stats from Retail Dive, 87% of buyers start searching for products on digital channels. Their needs are not, however, fulfilled by online stores. Shoppers expect to be able to scan for things and navigate across online catalogs quickly. Instead, they are continually facing the challenge of not being able to discover the products they are looking for.
Three studies have confirmed this by the Nielsen Norman Group, conducted over a period of 17 years. They published the results in 2018, highlighting several problems that online shoppers are facing:
Consumer habits and preferences are slipping behind word-based queries and irrelevant outcomes. Both customers and merchants lose many chances to communicate by focusing on having the right mix of terms without understanding the proper tags or meta tags. Consumers are disappointed and can not find the items they are looking for, while sellers fail to provide outstanding customer service.
The exhaustive product tagging behind product search is prevalent for retailers with thousands or millions of stock storage units from numerous sources. Shoppers can't find what they are looking for if these items are not correctly and reliably tagged, contributing to lost sales opportunities. Unfortunately, most online stores perform a manual tagging process that takes up a lot of valuable resources.
To maximize search results and keep up with the modern buyer, product tags should be reliable, organized, and flexible. The method of manually tagging items is, sadly, a struggle. Here are some of the problems with it:
Artificial Intelligence (AI) in fashion directly tackles the challenge of relevant product search, improving product discovery. AI has the ability to recognize items on an image that features fashion products, providing appropriate context.
AI provides various solutions to the fashion industry, including visual search, product recommendations, customer support chatbots, and many others. Among them is also automatic tagging, a solution that leverages intelligent machine learning algorithms to enhance the product catalog with correct and informative product tags. The technology helps provide the most relevant search results for shoppers as the most cost-effective product tagging approach.
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For us humans, it just takes a second to look at an image and recognize the items it contains. For machines, this isn’t so easy, and that’s why we’ve seen most product tagging performed by humans.
However, things have changed. Scientists developed Neural Networks that replicate the human brain using the advances in Computer Vision and Deep Learning. They can be taught to identify what is in a picture just as we humans do. They can take an image, analyze it, and send us semantic knowledge in the form of text.
Deep Learning accelerated and automated the entire product tagging process, eliminating the need for human intervention. The result is automatic tagging, a process that generates rich metadata for product catalogs. It operates in a way that scans the image and identifies characteristics linked to specific keywords.
AI systems have to learn from a large number of images about how clothing items look like, to recognize them in an image. Therefore, automatic tagging represents a trained AI system that has gone through thousands of tagged photos to learn the attributes of the fashion items. Thanks to this “training,” it can now do the same: tag images with the correct categories and attributes.
If the team usually takes days or weeks to complete the product tagging task, the auto tagging engine does it instantly and more accurately. Regardless of whether you’re selling items from various vendors or just want to improve the search results of your exclusive online shop, auto tagging can help you convert more.
These are some of the main effects of using an AI-based auto tagging engine:
Customers will also benefit from auto tagging. Firstly, they will get more relevant search results, which means they will get to the products they want immediately. Then, they will get a personalized shopping experience based on their own needs and preferences. Finally, they will be able to easily navigate the catalog, contributing to an outstanding customer experience.
The auto tagging engine makes tagging automated, specific, and scalable to giving retailers the knowledge they need and shoppers the smooth experience they anticipate. Moreover, collecting and attaching appropriate tags and attributes to catalog images with exceptional granularity and accuracy provides detailed and textual search results that keep up with trends.
Want to learn more about auto tagging? Check out our detailed guide here.
The automated product tagging engine by Pixyle.ai can help you get a clean catalog, improve your website's search results and boost your sales volumes by scanning the whole product catalog in just a few seconds. To see how it works, try our demo.