Today’s online shopper wants to be met with exactly what they want, exactly when they want it, and failing to do so can quickly lead to website abandonment and a loss in associated revenue. To help combat this effectively, this is where e-commerce product content enrichment comes in.
What is Product Content Enrichment?
Product Content Enrichment is the process of creating fully-enriched products with an extensive, customer-centric set of attributes. By definition, to enrich means to improve or add value. When applied to products sold on e-commerce websites, enrichment works to make product attribution better and more exact, improving the shopping experience for the consumer at the moment they’re showing high intent.
Enriched product content in e-commerce is made up of text, specifications, images, and any other key data gathered about a product.
Overall, the goal of product content enrichment is to transform a retailer’s product that might have thin and inconsistent product attribution data into a highly-searchable product with rich attribute data.
3 Things to Know about Product Content Enrichment
Here are the top three things we think are the most important to know:
1. It Should Speak the Language of the Customer
Speaking in this language of customers, rather than that of the manufacturer or distributor, has hugely beneficial ramifications across the entire retail value chain.
When a brand and/or retailer focuses on product content enrichment data provided solely by manufacturers and distributors, but stops there and doesn’t try to expand, they’re unable to grow and nourish the language that customers actually use. A retailer’s product content enrichment taxonomy should be robust and take into account how real people search and shop for products in the real world.
If a retailer doesn’t take this into account, carts are abandoned and conversion rates are lower; the need to mark down inventory later is exacerbated, and it’s increasingly less likely a consumer will come back to a site if they can’t find what they’re looking for in their own unique words. Slow product discovery, improper categorization, irrelevant product recommendations, and especially insufficient and non-enriched product attribution data all play a role.
For instance, consider a woman’s white skirt. Generic attributes will always tell you that it’s “women’s,” it’s “white,” and it’s either a maxi, midi, mini etc. Yet, customers in the real world will often have a predetermined sense of their desired fit, fabric content, whether it’s pleated, a high-low style, or is intended for hitting the tennis courts as a piece of an active apparel ensemble.
If said skirt 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 their 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 “mini length” and neutral color that drove the sales of it – not the fact that it was versatile activewear + came with built-in shorts that actually got customers buying it in stores and online.
2. It Impacts Site Search and Demand Forecasting
Through proper AI-driven tagging and a carefully-designed product content enrichment taxonomy, a product with two or three attributes can, through visual, immediately unlock its sales potential to become a product with 10 to 15 attributes. This works to drive on-site conversion and reshapes the meager fashion/apparel industry average of 1.4% to a growing 4% to 5% – or even more!
But just as importantly, the ability to sell at full margins is enhanced because demand is now forecast using customer-driven attributes – not legacy, out-of-the-box attributes.
Maintaining the appropriate inventory mix at the right time and delivering a quality customer experience are among some of the top challenges that all retailers are facing today. This means that demand prediction accuracy has never been more critical, and it starts with having finely-tuned product data that measures across thousands of attributes to determine exactly why things sold in the past.
Having a strong sense of what inventory will actually move within high-margin windows, or which proxy products to order based on product and consumer intelligence, is something that goes a long way toward ensuring that the products that are ordered are the products that will sell.
3. It Helps to Regulate the Supply Chain
By leveraging product content enrichment to build a taxonomy of granular product attributes, retailers can make significant progress in supply chain optimization. With the data visibility and insight they need, retailers can accomplish urgent objectives such as:
- Replace wholesale pre-orders with a leaner, demand-led, made-to-order model that empowers product development, fueled by AI to launch lines that are guaranteed to sell out fast.
- Reduce assortment complexity, clean up the product catalog, and use data analytics to identify the most profitable SKUs.
- Lean more towards price optimization and dynamic discounting rather than big end-of-season markdowns by extracting more value from the products that remain with better analytics.
Product Content Enrichment with Lily AI
In retail, granular product content enrichment and better product attribution data can help to connect shoppers with a more effective overall experience and reduce mountains of unsold inventory in the warehouse. The more insight the retailer has into their customer’s personal preferences, the better positioned they’ll be to anticipate demand and eliminate the uncertainty that causes overstocking. The future of retail is not off-the-shelf, but rather, on-demand.