Expert Interview Series #2 Jiwon Hong
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.
We explore data analytics for eCommerce in the third part of the YesPlz eCommerce digital transformation series. Learn more with Digital Commerce Consultant Christina Stensvaag (Former VP of Product Management at ShopStyle & Sephora, former Head of Digital Product Management at American Eagle).
Black Friday/Cyber Monday (BFCM) 2022 is just around the corner. The clock is ticking–and the success of the BFCM eCommerce 2022 season depends on making strong strategic choices to ensure both long-term and short-term longevity. We prepared a quick 5-question checklist for your fashion eCommerce to answer as you prepare for the upcoming busy season.
With BFCM 2022 around the corner, we’re going to put ourselves in the mind of a typical eCommerce shopper. What are their fears? Anxieties? Desires? In our second series on eCommerce digital transformation, we sit down with two experts (Christina Stensvaag and Jiwon Hong), to get their thoughts on how the evolving customer journey--and how that affects eCommerce.