How Auto Tagging Boosts Revenue for Retailers

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: 

• Search boxes that are difficult to locate on the website
• Insufficient support for synonyms and even typos and mistakes
• Unexpected result pages with non-logical sorting
• Badly implemented filters with irrelevant attributes and poor functionality

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. 

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The Problem With Manual Tagging

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: 

• It costs a lot. Stores need to recruit additional individuals to complete the manual tagging process their offer grows. This increases the costs and makes stores less profitable. 
• It takes up a long time. Imagine you have thousands of products. How long will you need to tag them all? Manual tagging will take up valuable time that you could use to actually grow your business. 
• The result is insecure. Matching all product categories, discovering all relevant attributes, and making all information consistent are complex tasks for a human to do. Humans are prone to errors, which results in incorrectly tagged products and missed conversions. 

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Automatic Tagging as the Solution 

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Artificial Intelligence (AI) in fashion directly tackles the challenge of relevant product search, improving product discovery through advanced tools like a product tagging solution for retailers. 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 fashion product 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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