In today’s digital age, customers have high expectations when it comes to their shopping experiences. Whether shopping online or in a brick-and-mortar store, they want to be able to find the products they’re looking for quickly and easily. Catalogs, both physical and digital, are essential when it comes to shopping from any retail business. With the rise in e-commerce from 15% of retail sales in 2019 to 21% in 2021, it’s become imperative that businesses have product catalogs to support their sales goals.
Product cataloging is the process of organizing and maintaining a comprehensive list of products that a retailer offers for sale. It helps customers easily find and purchase the products they’re looking for, and businesses can accurately track inventory and sales.
AI is increasingly used in product cataloging to improve the efficiency and accuracy of the process through:
- Image Recognition and Editing
- Search Engine Optimization (SEO)
- Inventory Management
- Virtual Try On
Image Recognition and Editing
Retailers often need to edit product images to make certain they’re clear and accurately represent the product. This can involve cropping, resizing, and adjusting the color balance of images, and writing descriptions to make them more appealing to customers. AI can be used to automate and speed up this process, allowing retailers to edit large numbers of product images quickly and accurately. This ensures when a customer types in a keyword of an item they’re searching, the results displayed contain realistic items they would consider buying. In addition, websites are able to recommend items “we think you’ll like,” based on keywords. This ties in directly with SEO.
Search Engine Optimization
Through proper organization and categorization of products, retailers can make it easier for customers to find what they’re looking for. The AI program can analyze the data from the catalog to optimize product listings for SEO purposes. This helps improve visibility of the retailer’s products in search results and drive more traffic to the website. It can also lead to revenue increases from repeat customers as they know the retailer carries items they tend to buy.
By accurately tracking what products are in stock, retailers are able to meet customer demand and avoid running out of popular products. AI can analyze data from the product catalog and predict future demand for products, allowing retailers to make informed decisions about inventory levels and avoid overstocking or running out of products.
Virtual Try On
For those that prefer to avoid trying on clothes or beauty products in store before buying, their hopes have been answered with AI. Customers can now virtually try on items to see if they like how they look before buying them. Walmart has set this up allowing users to either upload a personal photo or choose from one of 100+ pre-loaded models to try on clothes for them. Computer vision is used to depict how a certain article of clothing or accessory would look on the individual user, as if they were physically in the store trying it on.
Being able to try on clothes virtually before buying will also lead to a decrease in returns. Around 30-40% of online clothing purchases are returned and a significant portion of those never make it back on the store floor to be resold. It costs companies more money to ship items back to a store than to a landfill. By virtually trying before buying, customers are also helping the environment.
How Appen Helps the Product Cataloging Process
We are a leading provider of AI training data and have a wealth of experience serving customers in the retail and ecommerce space. With over 100 million product categorization labels in more than 20 different markets and the ability to quickly recruit contributors globally, we ensure the right labels are applied for each specific market. Among the wide range of tools and services we offer, our new taxonomy tool allows contributors to classify data according to a custom hierarchical taxonomy, and our ADAP Audit tab allows customers to quickly audit the quality of their annotated data, guaranteeing products will be represented correctly in catalogs.
In addition to these tools, we offer custom project services and can handle projects of all sizes, from “standard” use cases like search result relevance to more specialized variations like evaluating the consistency of an entire page of search results. We also have a network of local experts with language and cultural knowledge who can best interpret queries from users in their region, and constantly update their tools and processes to perform the highest quality of service.
Overall, AI training data can play a vital role in improving the customer experience and driving growth in the retail and ecommerce industry. By leveraging the expertise and tools of a provider like Appen, companies can effectively use AI training data to enhance their product catalogs and deliver a seamless shopping experience to their customers.
This article was written with the help of ChatGPT by OpenAI.