One of the best ways to find the latest fashion trends is to walk into a store at the start of a new season. Almost every brand follows a similar layout — your discount racks at the back, the staple essentials in their usual places, and front and center; you have the latest styles and colors of the season. The ones that make it much harder to walk by without adding them to your cart. It's easier to resist if you're closer to the end of the month. Rebecca Bloomwood would know. Store windows and stylishly dressed mannequins make it easy to showcase the latest products in retail. 

When you're online, though, it's a whole new ball game. Especially in AI and machine learning, where products are ranked based on performance, how easy(or difficult) is it for brands to encourage product discovery for fresh, new arrivals in a product catalog that runs into thousands of SKUs?

Relevance, as solved by AI

One of eCommerce's biggest challenges is getting shoppers to discover the products they are most likely to purchase. Being online has its apparent advantages, but it is wrought with many challenges too. You don't have a store associate gauging the shopper's every move, coaxing and guiding them towards a product they're already weak on. With ecommerce, you have to figure that bit out on your own with the data you have about the shopper. 

But how do we get to a place of knowledge about what each of those thousand shoppers is looking for without asking them? We know it because they tell us. The semantic search involves understanding the context of what is typed into a search box. It goes beyond a simple text match algorithm that picks up products that match the terms entered by the shopper but intelligently understands the context and intent behind the search. 

Here's an example of an intelligent search. While looking for a 'green wrap dress,' the system breaks down the search term to understand that green, wrap, and dress is three different attributes of the query: green is the color, the wrap is the style, and dress is the category. Then, the products that come up are ranked based on how close they are to what the shopper is looking for.

AI and Merchandising: the new Mac & Cheese

But what happens in the absence of sufficient data? If the shopper is visiting the site for the first time, or if there are new products that haven't had the chance to climb the ranks in terms of popularity. Say hello to AI-assisted merchandising. In a typical search scenario, new products are automatically added at the front or back of the results, making it easier for them to surface during a search or while browsing through categories if there's a dedicated page for new arrivals. There needs to be more search volume built for these products, and as a result, they are buried under existing products that are more popular. AI-assisted merchandising helps tackle such situations. At Unbxd, we assign a Freshness score to every product in the catalog.

What does a freshness score do?

It gives a fighting chance to every new, freshly added product in the catalog. First, more recent products are assigned a higher freshness score giving them a head start when they get added to the catalog. From there on, visitor feedback is aggressively directed to products with higher freshness scores to gauge their performance. Over some time, the newbies get significant exposure to pit them against old timers in the catalog. Once there is sufficient feedback on performance, the results are recalibrated based on set performance indicators such as conversion rates, overall popularity, and other similar metrics.

And so We Believe

The real impact of AI and machine learning in ecommerce comes when you use it to augment human insight, making merchandising more scientific and less subjective. Ultimately it is about using technology that smartly overcomes limitations with the right innovative features and is capable of facilitating effortless product discovery that marks the difference between an excellent online shopping experience and a great one. The future of online retail belongs to intelligent collaboration between man and machine. Our latest product, Browse, combines the intelligence derived from more than 50 shopper signals and extensive merchandising controls to personalize all category pages, at scale, for each shopper. Book a demo to find out how Browse can help you personalize the browsing experience and enhance the product discovery journey for shoppers on your site.