Coastal cowgirl sundress. Ancient Greece-inspired wall art. Strawberry girl cheek stain. Or consider a flowy beach dress with interesting sleeves for a summer cocktail party.
People want these items. In fact, today’s consumers are looking for both popular, timely trends and classic, timeless pieces. While your store may have many each of these items—or possibly all of them—without the right approach to product attributes, shoppers won’t find any of them.
This is because retailers have often underinvested in a critical area: their product data and, therefore, their product attributes. Descriptive, thorough product attributes connect people to items that retailers are selling. They impact several other key points in the customer journey, as well. In fact, customer-centric product attribution is so critical to revenue that 20% of purchase failures are potentially a result of missing or unclear product information.
Here, we dive into the basics of product attributes and their importance. We’ll cover how they drive revenue for retailers and how AI is the most accurate and efficient way to get them right.
What Are Product Attributes?
Each item sold by a brand, retailer, or marketplace has a unique set of features. To the simplest degree, these are the product’s attributes.
When an item is sold online, its product attributes are manifested in its listing’s metadata and tags, which help consumers find it and learn about it. Because each item’s list of attributes is unique, each item can, therefore, be differentiated from one another.
Product attributes can describe tangible, physical aspects of a product, including embellishments, fits, fabrics, colors, materials, patterns, closures, and more. They can also describe intangible aspects of a product, such as perceived value, quality, an occasion it can be associated with, or a new trend it may align with.
Including all of this detailed information within a product listing makes that item more discoverable and understandable to shoppers. In order to achieve this, though, product attributes should account for varied language that aligns with the voice of the brand or retailer and their customers. Incorporating an extensive taxonomy that’s rich with synonyms, of-the-moment trends, and words and phrases people actually use while shopping is critical for product attribution to be successful. Product attributes absolutely must reflect customer language in order for people to find, connect with, and ultimately purchase a product.
How Do Product Attributes Fall Short?
Oftentimes, product attributes don’t live up to their full potential. Many retailers use their merchant-focused language to describe the products they’re selling to people with no experience in retail or merchandising. Of course, these consumers use different words and phrases to describe what they want to buy!
Product attributes typically originate from manufacturers or brands, and that information is sent on to retailers or marketplaces, where items are listed for sale. This process leaves much room for error, inconsistencies, misattributions, and blank fields. Since it is entirely merchant-focused though, many products are attributed with descriptive words and phrases that only merchants can make sense of.
The key to connecting people to items via product attribution is building consistent, thorough product descriptions and metadata that account for the various ways consumers speak.
Why Are Product Attributes Critical to Get Right?
As discussed, product attributes connect people to products. They drive sales. And when done right, they minimize dissatisfaction and, therefore, return. Here’s a look at how customer-centric product attributes enhance multiple steps of the buying journey with consumer experiences in mind.
Product Attributes Maximize Google Search
31.5% of product searches start on Google, with younger generations searching for products there even more. Additionally, 82% of all current desktop traffic to retailers’ websites is from organic and paid search. Clearly, retailers need to ensure that their paid and organic investments work extremely well and are optimized for performance.
Because of the breadth of retailers’ online offerings, AI delivers the most comprehensive Search Engine Optimization (SEO) and Search Engine Marketing (SEM) ecommerce product attribution capabilities to make the most of their search investments. With assistance from artificial intelligence, product descriptions feed SEO and SEM-optimized data to Google Merchant Center, driving better search engine result page rankings. This same data enrichment optimizes organic rankings with descriptive, SEO-rich language highlighting product benefits and aligning with how consumers search.
Product Attributes Provide Accurate Product Descriptions
Product titles and descriptions are yet another linguistic opportunity to connect people to products. In regards to titles, accurate and descriptive titles must reflect customer search queries, as Google uses those to determine the relevance of a listing to a search. (Remember those 31.5% of product searches that start on Google? Product titles provide great opportunities to connect with shoppers there!)
AI-generated product descriptions deliver detailed language that describes features to a great extent. Thorough descriptions of your product’s characteristics help consumers better understand your offering, especially when comparing it to that of a competitor. They may also minimize returns. The details listed within product descriptions, including their depth and accuracy, give an honest look at your offerings pre-sale, helping people understand exactly what they’re purchasing.
Product Attributes Drive Personalization
When someone adds an item to their cart, they are often recommended other products that may be of interest to them. Powerful product attribution makes this experience much more personalized, especially when AI is involved.
Personalized product recommendations are served up to consumers, and they’re based on similar attributes from products those people previously engaged with. For example, if a person added a coastal cowgirl sundress to their cart, they’d likely respond well to seeing other coastal cowgirl-inspired items, such as boots, hats, fringe jackets, or shell jewelry. Since those items contain the coastal cowgirl product attribute, they will be recommended.
Upselling via personalized recommendations is a lucrative opportunity, too. 40% of retail executives say personalization efforts have had a direct impact on maximizing sales, basket size, and profits.
How Does Lily AI Approach Product Attribution?
Lily AI’s retail AI suite harnesses generative AI, computer vision, natural language processing, and machine and deep learning to automatically generate the highest-performing and most customer-centric product attribution available.
Lily’s AI handles product attribution with depth and ease. Its extensive taxonomy covers a multitude of ways consumers speak, including over 25,000 customer-oriented product attributes, synonyms, and trends.
Lily AI adds high-performing product attributes during item setup and product onboarding. These attributes are rich and in-depth, including new trends, styles, and categories, such as the following examples:
- Words and phrases account for hot trends like Quiet Luxury.
- Descriptive qualities like bodycon fit, velvet, and zip-close.
- Synonyms like “thin straps” and “spaghetti straps.”
- Subjective attributes, like dressing styles and occasions, like those found in the concept of a “boho chic cocktail dress.”
In addition to our AI, our internal team of retail experts ensures accuracy and that new and hot trends are accounted for. This means that your offerings are always up-to-date, relevant, and descriptive, making them easier than ever for consumers to find, connect with, and purchase.
