Artificial intelligence and machine learning in an online digital marketplace can minimize human touchpoints and allow platform technology to produce the efficiencies expected.
The use cases that benefit include product deduping, identifying cross-seller recommendations, customer segmentation and personalization, intelligent search results based on customer behavior, and locating products using images as a reference, among others.
However, it can be a challenge to translate the power of AI and machine learning into moving the three levers of eCommerce: customer visits, average order value, and cart size.
In order for these technologies to deliver a positive ROI, they need the right datasets to create realistic models for their actions. Most eCommerce marketplace implementations lack the proper data aggregation systems for that to happen.
An effective marketplace implementation, though, can deliver on the crucial data-driven task of catalog harmonization.
How can AI within marketplaces help catalog harmonization?
Sellers that want to see the full value of AI reflected within their marketplace must be able to enable their products to be classified within the web taxonomy appropriately. This can be done either via natural language processing (NLP), in which items are classified based on text within product names and descriptions, or through a system in which classifications are based on images uploaded by the seller, known as image detection and classification.
AI can also be used to scale and improve the image quality of photos provided by sellers. This enhancement can help to create a more visually appealing user experience for the marketplace buyers, which in turn can drive sales.
Another function of AI can is to dedupe products uploaded by multiple sellers. These products may have different names, descriptions, and identification numbers, but in reality, they reflect the same Stock Keeping Unit (SKU) being sold on the website. This is also critical to the buyer experience, as duplicated products on a marketplace can weaken the perception of a site’s authority in the eyes of a consumer, thus leading to a damaged online reputation. Confusion over items also may lead buyers to drop out of the sales process between cart and checkout, a clear indication of lost sales opportunity.
What are some predictions for AI and ML within a marketplace environment?
The retail sector has just begun to scratch the surface when it comes to the utilization of AI and ML, and that is just in general eCommerce sites, let alone full-fledged marketplaces. With the introduction of VR-ready devices and Metaverses, AI and machine learning will have a big impact on moving more of the retail shopping experience to virtual stores. These stores will provide customers with the ability to browse a virtual eCommerce marketplace from the comfort of their physical homes. The technologies also will play a major role in improving customer interactions on online, virtual, and physical channels in the near future.
The author, Kiran Raghunathan is the CTO of McFadyen Digital.
