AI Case Study

The North Face offers customers a personalised, engaging virtual shopping experience using chatbot and machine learning

Outdoor apparel retailer, The North Face is using Fluid’s Expert Personal Shopper (XPS) software and IBM Watson to enhance its app by offering personalised recommendations and assisting with the shopping. IBM Watson enables the customers to have a natural conversation with the app while Fluid XPS system powers the recommendation engine.

Industry

Consumer Goods And Services

Retail General

Project Overview

"Using Watson's natural language processing ability, XPS helps consumers discover and refine product selections based on their responses to a series of questions. For example, after a shopper enters details on a desired jacket or outdoor activity, XPS will ask questions about factors like location, temperature or gender to provide a recommendation that seeks to meet the shopper's specific usage and climate needs. Unlike other product recommendation engines, this conversation with the shopper is what enables XPS to refine its recommendations and deliver a more accurate result."

Reported Results

The company claims:

* Average customer engagement of two minutes in length
* 60 percent click-through rate to try product recommendation

Technology

Function

Sales

Sales Operations

Background

It is estimated that about 70 percent of shopping carts are abandoned online before a purchase is complete (Sanz, n.d.). A little interaction with the customer could go a long way in completing the transaction.

Benefits

Data

factors like location, temperature or gender to provide a recommendation that seeks to meet the shopper’s specific usage and climate needs