The North Face brings cognitive computing to e-commerce

Fashion brands like The North Face are leveraging AI-driven tech like IBM Watson’s cognitive computing to grow bring the online and in-store experience closer together

Key Takeaway

The North Face has become a pioneer in leveraging artificial intelligence to make the shopping experience better and more cohesive for customers. Their AI tool takes on the role of a personal shopper for the e-commerce customer, attempting to close the gap between the in-store and online experience.

As the 360-degree customer experience becomes even more imperative in the competitive fashion space, apparel companies are looking for ways of leveraging the data at their fingertips to create a competitive advantage. Insights from external data can help them to  better understand the needs of their target consumers, and alleviate the overwhelming paradox of choice that often plagues the e-commerce space.

One company who has adapted particularly well is VF Corporation, parent of popular sport brand The North Face. The team has partnered with IBM Watson to leverage their AI analytics and curate a better, more personalized shopping experience for their outerwear apparel.

The impact of AI in online retail

Though e-commerce has continued to grow significantly over the last decade, many consumers still prefer the brick-and-mortar experience which enables them to try on different sizes, look in the mirror, feel material before a final purchase is made – something e-commerce equivalents have struggled to emulate. Buying from brick and mortar also allows immediate feedback and more personalized, human-driven customer service.

Meanwhile, the ability to engage customers online remains a challenge. According to a 2016 report from Business Insider, 70% of e-commerce customers abandon shopping carts before completing checkout. Despite efforts in retargeting and targeted marketing, the challenge of maintaining the buyer’s interest throughout the online shopping experience remains.

By analysing data on customer feedback and sentiment, search history, purchase history and add to cart behavior, companies can better understand their customer, segment on the basis of user behaviour and online data, and predict customer preferences and intent to purchase, leading to a much more targeted strategy.

The power of analytics: implementing IBM Watson

Watson Analytics has been used in the fashion space across a number of brands to enhance the user experience by allowing customers to interact with data conversationally, discover and refine product selections, and spot trends with almost immediate results.

Watson’s cognitive computing makes use of natural language processing and artificial intelligence, enabling a computer to behave and act like a human would – taking in sensory inputs, learning customer preferences and behaviors and creating interactive experiences for the user to recommend decisions that will enhance their experience. The system is constantly gaining value and knowledge via past interactions.

The North Face launched their interactive online shopping experience tool in partner with Watson back in 2015. The press release stated: “In keeping with The North Face brand’s mission of applying technology to transform the retail experience, customers can now use natural conversation as they shop online via an intuitive, dialogue-based recommendation engine powered by Fluid XPS and receive outerwear recommendations that are tailored to their needs.” In turn, retailers benefit from the psycholinguistic profiles the technology creates.

Developed in partnership with digital commerce technology agency and software solutions provider Fluid, and powered by IBM’s Watson cognitive computing technology, The North Face experience harnesses Fluid’s Expert Personal Shopper (XPS) software to create a more engaging, personalised and relevant shopping experience.

Utilising Watson’s natural language processing ability, XPS helps a customer fine tune their product selection. For example, after a customer enters details of a desired jacket or outdoor activity, the AI will ask questions about where, when and for what activities the jacket will be used for.

The software then takes into account a number of external data points, from previous user preferences to customer reviews, social media sentiment, the weather forecast for that specific location and the user’s gender, and narrows down the search to 6 products that would potentially meet the shopper’s requirements. Additionally, based on the activities it then rearranges the alternatives from “high match” to “low match”, saving the customers time while browsing through a plethora of options that might not be suitable for their needs.

The retail industry, like many others, is awash in structured and unstructured data -- from social media to text messages to customer reviews

By tapping into Watson, retailers now have the power to turn this data into meaningful insights that can make the shopping experience more intuitive, informed and enjoyable,” said Stephen Gold, IBM Watson VP Business Development & Partner Program. “Market leaders like The North Face and Fluid are demonstrating how cognitive technologies can redefine how brands connect and engage customers.”

XPS: how it works

XPS and AI programs rely on a Natural Language Interface (voice-based like Siri or text-based like XPS). This is vital as it allows a customer to speak to a computer in the same manner with which they would to a live sales assistant at a store. Furthermore, it eliminates the need for a customer to know ahead of time what they’re looking for and type it into the search, thus removing the friction points of data entry and overwhelming choice often caused by shopping online.

Cognitive retail assistants or bots like the one used by The North Face often lead to more effective and efficient end-to-end personalised experiences for the online shopper, facilitating unique buying moments and leveraging brand loyalty.

Simultaneously, these products continue to collect data on individuals users and segments, providing the product and management teams with better consumer insights, and continuing to feed the predictive algorithms with data that can enhance their ability to offer increasingly more personalized and accurate recommendations moving forward.

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