We often hear that online retail is among the business sectors furthest along on its digital transformation and AI journeys. Overstock.com is a case in point. The giant (2017 revenue: $1.75 billion) internet retailer, known mostly for furniture and home décor, has built out a digitized ecommerce environment that captures and analyzes the behavior of millions of monthly visitors to its website – behaviors such as adds and removals from shopping carts, product queries, product comparisons, time of days and so forth – amounting to billions of individual site events.
For Chris Robinson, a self-described “data enthusiast” and head of marketing data science at the Midvale, UT, company, Overstock.com’s enormous data stores is “where I knew I wanted to take my career…, e-commerce web log data is the playground we all dream of.” Formed in 1999 during the dot-com boom, the company has captured two decades of customer data that Robinson and the company’s teams of data scientists, engineers and analysts mine for “actionable insights,” as they say in the analytics world. The team faced not only a formidable data science challenge, it’s also data science at scale.
Author: Doug Black