In the Spirit of Computer Vision & AI
Product Catalog Data Enrichment Improves Workflow Efficiencies
The retail landscape is very competitive, so retailers are turning to technology to improve operational efficiencies while at the same time improving their customer’s shopping experience.
Retailers are investing in new technologies such as virtual reality, augmented reality, computer vision, and voice assistants to enhance the shopping experience and make it easier for customers to find what they want. Furthermore, to address streamlining internal processes to reduce time and effort, retailers are using artificial intelligence and machine learning to automate the creation of product catalog data. These investments aim to create an immersive, digital-first shopping experience that will keep customers returning and revolutionize how their teams work. This article focuses on the value of contextual computer vision.
According to IDG, 58% of retailers have definite plans to implement computer vision. Additionally, an overwhelming 96% of respondents to the IDG study believe that computer vision has the potential to grow revenue, while 97% say these technologies will save their organization time and money.
Computer vision is a multifaceted technology. This technology allows retailers to transform how they create and manage their product catalog data. Contextual computer vision quickly and accurately analyzes product images and automatically extracts relevant information for product identification, classification, and categorization. By leveraging Contextual Computer Vision, retailers can scale generating product metadata for every product detail page. AI programs have evolved to digest product image elements and furnish website ready copy for product names and descriptions, SEO titles, generate additional product photos to produce unique product angles of any item, analyze and invent brand-specific product color names, and lastly, determine items that fit a specific body type. With AI, retailers can increase the accuracy and efficiency of their product catalogs, enabling them to offer customers more accurate search results and better product recommendations.
AI-generated product data is much faster than humans manually writing descriptions one product at a time. Merchandisers no longer have to spend countless hours creating descriptive and flowery product attributes and romance product copy and SEO descriptions SKU by SKU because of the advancements in contextual computer vision. For the first time, Retailers can eliminate these time-consuming manual tasks from their merchandising team, which allows them to focus on more vital tasks. Additionally, early adopters gain an early advantage.
If you have yet to think about the value computer vision offers your brand, here are dozens of direct and indirect benefits it provides and why you should seriously consider adding it as a strategic initiative.
Guess which Image is Computer-Generated?
Many retailers receive a single stock photo from the manufacturers, and photoshoots are expensive. However, to improve the sell–thru ratios, products should have a minimum of 5+ images for consumers to view. Here‘s where computer–generated images can expand the number of photos per product at a reasonable cost to increase the chances an item sells.
I uploaded a single product image into DALL-E free trial and received three high-quality photos to use alongside the original.
Contextual Computer Vision Enrichment Example
Digitile’s computer vision attribution solution analyzes the product image below and provides its client’s an enriched product catalog with attributes so customers can quickly search and find products on their site.
Digitile auto-generated the following Product Name, Description, and SEO title using the attributes from the image on the left.
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Contextual Computer Vision Benefits for Shoppers
Enhanced Search and Discovery
Improving the search experience makes it easier for customers to find what they are looking for, which can significantly improve their overall shopping experience. In addition, this can lead to increased customer loyalty and higher levels of engagement.
When products are tagged, it’s easy to produce alternative items for out-of-stock items to avoid shopper abandonment.
Contextual Computer Vision Benefits for Merchandising Teams
Increases Merchandise Visibility & Improves Sell-Through Ratios
A direct benefit of contextual computer vision is that it can also help with the customer’s experience by providing more accurate search results and improving the user experience. For example, a customer may search for a specific product, and the attributes defined by the image recognition system can provide them with the exact items they want. Customers who receive accurate search results convert at a higher rate, improving a brand’s sell-thru ratios. In addition, image recognition technology can help buyers understand what customers are searching for, allowing them to align their inventory with customer demands better. This can help buyers identify the right products at the right time to ensure they have the most relevant products to offer their customers.
Onsite search can make it easier for customers to find the products they are looking for quickly and easily. This can increase the visibility of specific products and improve their chances of being sold at full price. In addition, by making it easier for customers to find the products they need, retailers can reduce the need for markdowns and the associated losses in revenue.
Increased Conversion Rates
With a better onsite search experience, retailers can significantly improve their conversion rates. This is because customers can quickly and easily find the products they are looking for, reducing the likelihood of them leaving the site without making a purchase.
Increased Accuracy in Product Catalogs
Automatically enriched product catalog data can help reduce errors and improve the accuracy of product information, including descriptions, pricing, and availability.
Automatically enriching product catalog data can reduce the cost of manual data entry and ensure that product data is always accurate and up-to-date.
Easily show alternative items currently in stock if the item requested is out of stock.
Merchandising and Marketing teams should review customers’ onsite search words and phrases to learn which terms are most frequently used to find products. With this insight, merchandising can create new product category pages, and marketing can tailor ads that use their customers’ natural language to improve conversion.
Contextual Computer Vision Benefits for Marketing Teams
Improve Customer Targeting & Drive Efficient Return on Ad Spend (ROAS)
Appending product attribute data to customer records creates detailed insights into customer affinities to better segment customers and target them with more personalized marketing messages. Additionally, Marketing can create a better lookalike audience to increase return on ad spend.
Accurate product catalog data helps retailers rank higher in search engine results for related keywords, driving more traffic and potential customers to their website.
Improve & Increase Customer Loyalty
Deliver an optimized and delightful shopping experience designed to convert and develop greater trust and credibility in your brand to build a loyal base of customers.
The Challenges of Using Computer Vision AI
Using computer vision AI for retailers can come with some challenges. First, it can be difficult to set up and maintain the technology, as it requires specialized hardware and software. Additionally, it requires a robust multi-layered taxonomy to properly attribute product images. This is where Digitile comes in. Digitile is an affordable solution for retailers of any size; see first-hand how quickly and easily it generates product catalog data.
Digitile specializes in building rich taxonomies for apparel, home & decor, beauty, CPG, sporting goods, jewelry, and more.
How Retailers Harness Computer Vision to Enrich Product Catalog Data
Computer vision AI is a powerful tool for retailers to create detailed product catalogs and better meet customer needs. It can quickly and accurately identify and categorize products, allowing retailers to create comprehensive product descriptions. Additionally, this technology can help retailers identify customer preferences and build product catalogs that are tailored to meet their customers‘ needs. With this tool, retailers are able to provide customers with a more personalized shopping experience.