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Solving Ecommerce Shoppers' Needle-In-A-Haystack Problem Posted on : Mar 14 - 2019

I love shopping at a Nordstrom store, but hate shopping on the Nordstrom website. Unless I know exactly what I want, like my favorite shade of lipstick, I have a time trying to find what I might want.

Take a search for a blue dress. Nordstrom served up nearly 3,000 options and after narrowing it down to casual styles, I had a mere 1,000 to go through. Included in that search were jumpsuits (sorry, not a dress), plus sizes and maternity dresses.

I have been a longtime member of Nordstrom’s loyalty program, so it knows my size, my style/brand preferences and also knows I am of an age where I am not going to have a baby.

Nordstrom should take all that it knows about me as a dress shopper and serve up a first page selection of blue dresses that are most likely to catch my eye. Instead I have to wade through page-after-page of irrelevant selections. After five or so, I am out of there.

The blue dress I might want is relatively easy to find in the store, but nearly impossible on It shouldn’t be so hard, which is why a new machine-learning commerce platform from SAP drew my attention.

Called SAP Upscale Commerce, it was launched late last year and demonstrated at NRF’s Big Show. Afterwards I caught up with Lori Mitchell-Keller, co-president of SAP Industries, to talk through how it can solve the ecommerce shoppers’ needle-in-a-haystack problem when browsing online, but she also tells me it works to support sales in-store too.

“Our application is surprisingly simple, but what it does is powerful,” Mitchell-Keller explains. “It uses machine learning to understand the customer and help build wardrobes around what the customer likes. It is like the ‘complete the look’ suggestions many websites offer [e.g. Nordstrom] but rather than having suggestions programmed by the buyers, it is based on the individual customer’s preferences.”

This application takes personalization to the next level to help the customer find what he or she is most likely to want when and where they are looking for it. Sales associates can tap the intelligence of the program too in order to deliver to the customer items that he or she might like.

“It is a deep learning system which automatically figures out which products to show to each visitor, while at the same time maximizing profit for the retailer,” Mitchell-Keller remarks. “This means that each visitor has a completely unique shopping experience tailored to them.”

Burberry and Ulta are early adopters of the new platform. Mitchell-Keller shares an example of how it works for Burberry.

“It knows that the customer doesn’t like peplum or double-breasted jackets, because she has tried on three of them and never bought, or only chooses pants to go with a jacket, never skirts,” Mitchell-Keller explains. “That kind of understanding of the customer is powerful and saves her time, especially considering the desire of customers to spend the least amount of time finding the right outfit.” View More