The Cascading Challenge of Merchant-Driven Product Attributes
The most common excuse made in the world of business and commerce — often said defensively, in hushed tones and with no small degree of implied embarrassment — is, “it’s the way we’ve always done it.” Well, we’re only human, after all. Stagnation and stasis and the comfort of the known often keeps us from innovating and evolving our processes.
Take, for instance, the time-honored retail industry practice of letting the merchant drive the attribution of products. This isn't, on its face, a terrible idea. Who but the merchant, and the designers and distributors they buy from, knows the product being sold the best? For many years this was certainly true, yet in a customer-centric, "niche-ified" world of preferences and choice, merchant-driven attributes are largely objective, can be inconsistent, and lack depth. Without a customer-centered product taxonomy that describes merchandise in the language that shoppers — as opposed to merchants — actually use, retailers start a cascading series of problems right at item set-up that continues on through site search, demand forecasting and merchandise planning.
Consider the men’s black T-shirt. Merchant-driven attributes will always tell you that it’s “men’s”; it’s “black”; it’s either a crew neck or a v-neck, and maybe they’ll even tell you whether there’s a pocket. Yet customers in the real world will also sometimes want a logo, or have a pre-determined sense of their desired fit, fabric content, fabric weight, T-shirt length, and whether that simple black T-shirt is intended for casual or active apparel.
If that T-shirt isn’t attributed correctly right at item set-up, it won’t be easily found in online searches — neither via SEO/SEM nor on-site search — and it won’t be recommended to a consumer who might otherwise love to add it to his online cart. It might not even be ordered for next season, despite its popularity, because merchandise planning and demand forecasting teams thought it was the “crew neck” that drove the sales of it, not the fact that it was activewear + medium length + crew neck that actually got customers buying it in stores and online.
Let it be said that there are an incredible number of alternate ways an attribute can be named. For example, lip gloss can also be known as lip glaze, lip shine, lip topper, liquid gloss, and tinted gloss. Categorizing it as just one of these, without a product attribute taxonomy that can provide synonyms, means that a beauty retailer will be dead in the proverbial water when a consumer tries to search for “lip shine” and ends up getting “powders to remove shine from a forehead” as their top results.
And, of course, different consumers shop uniquely. Under the category of “Loose dress” you’ll find nap dresses, house dresses, sun dresses, swing dresses, relax loose dresses, relax nap gowns, and many more. These loose dresses come with different embellishments and fabrics, and are worn for different occasions. These nuances really matter to shoppers.
Speaking in the language of customers, rather than that of merchants, has hugely beneficial ramifications across the entire retail value chain. A product with two to three attributes can, through visual, artificial intelligence-driven tagging and a carefully designed taxonomy, immediately unlock its sales potential to become a product with 10-15 attributes.
When product attribution is done correctly in this manner, it instantly drives better site search, filters and facets, product recommendations, and demand forecasting. On-site conversion goes from an anemic retail industry average of 2.5 percent to 4 percent, to 5 percent and higher. Just as importantly, the ability to sell at full margins is enhanced because demand is now forecast using that language of customer-driven attributes — not merchant-driven attributes. This increases the ability to sell to customers what they’re looking for right now, and decreases the need to mark that inventory down later. “It’s the way we’ve always done it” becomes a thing of the past, and consumers have a new set of forward-looking, customer-focused brands to pin their shopping allegiances to.
Jay Hinman is the vice president of marketing at Lily AI, the customer intent platform built to power the present and future of e-commerce.
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Jay Hinman is the vice president of marketing at Lily AI. He is a longtime B2B marketing leader with specialties in demand generation, corporate marketing and product marketing and extensive, cross-discipline team leadership experience both in small start-up and large technology companies. He’s led marketing at Opera Software, MobiTV and Neumob (acquired by Cloudflare), among others, and lives and works in San Francisco, CA.