The North Face & IBM Watson: A Winning E-Commerce Combination?

The North Face mission statement is to “Provide the best gear for our athletes and the modern day explorer, support the preservation of the outdoors, and inspire a global movement of exploration.”


The North Face is an American apparel brand well known for its winter jackets and outdoor equipment, striving to “provide the best gear for athletes and the modern day explorer”1. It is lesser known for its technological innovation and machine learning exploration. In fact, it is one of the first companies to use IBM’s Watson artificial intelligence technology in a retail environment2.


In 2016, The North Face launched a mobile app that allowed customers to engage with Watson to help them find what they need and narrow down their apparel choices. The goal was to help customers find the perfect jacket for their next adventure3. Instead of scrolling through the 350 jackets that The North Face has in stock and potentially getting confused with the plethora of options, customers could now tell Watson what they will use the jacket for, what kind of general style they like, and Watson would spit out a narrow set of jacket choices customized to the customer’s preferences2.


This technology is very important for an organization like the North Face, which is trying to build out its e-commerce platform4. Companies that use e-commerce face significant hurdles to get customers through the full purchase funnel without losing interest; around 70% of online carts are abandoned before check-out5. Brands that sell products online do not have the luxury of a knowledgeable sales associate to help a customer sort through their many options, leaving customers free to opt out of purchase. Watson is bridging this gap for The North Face. It provides the customer with a customized list of items, both removing friction for the customer as well as improving efficiency and conversion rates for The North Face5.


In the short term, the company uses this technology as a data source6. Based on high take-rate to purchase, high cart values, and other Watson metrics, The North Face can use the data to inform future merchandising strategies and glean important insights and patterns on suggestions that work for the majority of customers6. Longer term, as they further build out this technology and market it to the wider public, the company can acquire more customers, gain higher conversions over time, and reduce costs necessary to service each customer6. If they get this right, machine learning through IBM Watson can set The North Face up to potentially dominate the outdoor apparel space.


While the idea and product sound great in theory, the actual current experience of using Watson on The North Face website is fairly buggy2. According to Cal Bouchard, the Senior Director of Ecommerce at The North Face, Watson’s artificial intelligence is currently at a second or third grade level and will take a couple of years to get the technology just right2. For example, when I went through the platform3 myself the first time, Watson matched me with a jacket that was not in stock. The second time I used Watson, it matched me with a jacket that did not come in my size. This was a frustrating experience and I would recommend that the team at The North Face work with IBM to fix issues like these that erode the customer experience. In the short term, it is extremely important to get the technology right to build up credibility around this platform. Additionally, in the medium term, I would dedicate resources to marketing the partnership with IBM and this technology to the public to build up brand awareness and engagement.


Having experienced the platform myself and the frustration and mistrust that came with Watson spitting out a jacket that was not available in my size, I wonder how The North Face can educate customers on Watson’s success rate and set customer expectations that the technology will not always be perfect. A sales associate in-store can easily make the same mistake as Watson did, suggest an item of clothing that is not in the right size, and be forgiven by the customer. Do you think customers will also forgive Watson and The North Face for making this type of mistake or will they expect an unattainably high success rate for this technology and give up on it if it does not meet that?


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1 The North Face, “About Us,”, accessed November 2018.


2 Matt Marshall, “The North Face to Launch Insanely Smart Watson-powered Mobile Shopping App Next Month,” VentureBeat, March 4, 2006,, accessed November 2018.


3 The North Face, “Expert Personal Shopper,”, accessed November 2018.


4 Rebecca Harris, “The North Face Brings AI to Ecommerce,” Marketing Mag, January 12, 2016,, accessed November 2018.


5 Luis Sanz, “The North Face & Watson: Bringing the In-Store Experience Online,” Olapic, 2016,, accessed November 2018.


6 Daniel Faggella, “Artificial Intelligence in Retail,” Tech Emergence, October 29, 2018,, accessed November 2018.


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Student comments on The North Face & IBM Watson: A Winning E-Commerce Combination?

  1. This is a very intriguing post that points out many of the potential pitfalls with machine learning and how humans may have higher standards for machine vs. man. I think that humans will continue to have a higher standard for machines, but I am not sure that is a bad thing. If we are going to lose the je ne sais quoi of human interactions (and the potential jobs that come with a move from humans to machine), I think it is a reasonable tradeoff that machines perform better than humans in this context. That said, this line of thinking becomes tricky when error is much more catastrophic. We see this play out with self-driving cars. They are essentially held to an error rate of zero. Any accident results in a ton of bad PR and usually a scaling back of the pilot program. This may be unfair as the car may have a significantly lower error rate than its human counterpart, but we are less willing to tolerate those minimal errors because they come from a machine. Overall, I think this shift to automation will require a better understanding of the psychology behind how humans view machines and AI/machine learning likely demands a shift in that perspective if it is to be adopted widely.

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