Expert Interview Series #2 Jiwon Hong, August 2020
Currently I am CEO & Co-founder of YesPlz, the next-gen visual search for eCommerce. Prior to YesPlz, I launched the search and recommendation engine for SmartTV at Samsung to the global market. I am committed to transforming online shopping search experience to be easy and delightful.
Generally speaking, a Fashion Recommendation Engine (FRE) extracts the key attributes from a product and finds items with matching attributes, such as silhouette, material, price and brand. Most of all, a recommendation engine should find clothing the customer likes.
The most well-known recommendation engine is collaborative filtering which you probably have seen at Amazon. At the end of the product detail page, Amazon shows a list of items under the title “Customers who viewed this item also viewed.” This recommendation engine assumes a shopper will like items that other shoppers viewed after checking out the original item.
A traditional fashion recommendation has been recommending clothing based on the same brand or similar product title. The richer the keywords, the better the recommendations work. Except creating product titles and descriptions is expensive.
With computer vision technology, a recommendation engine has evolved to detect the key attributes from an image and recommends styles accordingly. No text information is required. A photo of a long tank top will search another 100 similar images of long tank tops assuming a customer is looking for that particular style. Even better, sophisticated vision technology narrows a search to the fine details such as a slim fit waist and a flat neckline for the long tank top matching a customer’s specific preferences.
If a retailer’s website is based on eCommerce platforms such as Shopify or Magneto, a retailer can simply go to their app store and download a recommendation app. It only takes one or two clicks to install. The pricing is about $7 -$35 monthly which is much cheaper than building an in-house engineering team.
Enterprise clients can test a recommendation engine through API that can be integrated in as early as a few weeks. The cost for an enterprise varies by size, but A/B testing can be a great way to test a recommendation engine performance and set a price. Most solution providers offer either an introductory rate or a free trial for A/B testing.
It is easy to test different solutions and see which recommendation engine fits your business best. There is no reason to delay offering this virtual shopping assistant to your customers.
Well-curated recommendations transform the shopping experience, making a shopper happy and wanting to come back. Plus, it is impossible to manually curate the recommendations for each individual customer.
Over time, a good recommendation engine learns an individual’s shopping preferences, if a customer allows it. Imagine a virtual shopping buddy who deeply understands a customer’s preferred styles that can quickly find the right item! That capability can be on your website now.
Unpredictability is unfortunately the norm for 2020, with fashion retailers experiencing unexpected twists and turns over the year. In the context of a pandemic as well as shifting customer expectations, it may be difficult to determine which business decisions to apply to your brand this holiday season.
Taxonomy is essential in fashion because of the large number of products inherent in the industry, and it points us in the right direction to help us find the clothing we’re looking for.
With investors including initial investors PrimerSazze and igniteXL Ventures both based in Silicon Valley. The investment will be used to scale out the world’s first visual search filter that helps style online consumers through personalized AI technology