Overstock is one of the granddaddies of Internet commerce with 20 years worth of customer data to support its marketing analytics. Here’s how it increased the speed of models and analysis by 5x.
Getting the attention of consumers is no small feat these days. They are bombarded with advertisements on television, through their mobile phones, and on the Internet. You may see ads from your favorite online retailers follow you from Google to news sites to Facebook.
Engaging with the right consumer at the right time can make a big difference for online retailers, and these ads are an important means of doing it. But there’s plenty of competition out there.
If you want to use these kinds of ads to gain consumer attention today, you probably have to act fast. It’s not like the Internet commerce of 20 years ago when online retailer Overstock came onto the scene, buying and then sellingthe inventory of failed online retailers.
Overstock.com is really one of the original online ecommerce businesses. This online retailer was founded the same year as Netflix (the company that started by sending out DVDs via US mail) and 3 years after Amazon.com made its debut selling print books on the Internet and shipping them to your doorstep. It was a time when the World Wide Web — we called it that back then — was just getting started as a mainstream network used by consumers for many things, including consumer purchases. After 20 years in business, Overstock has amassed huge volumes of data.
Overstock’s business model has evolved over the years beyond discount and liquidation to include sales of new merchandise and hand-crafted merchandise from developing countries. The site sells everything from furniture to apparel to electronics.
Overstock has always been a bit of a trailblazer. For instance, back in 2014 it was among the first big retailers to accept bitcoin for payment. So it shouldn’t be a surprise that Overstock would work with newer technologies to get an advantage when it comes to advertising and marketing itself to consumers.
Like many other businesses, Overstock uses SEM, also known as search engine marketing, or paid search, to place advertisements on the familiar sites that consumers use — from Google Ads to Facebook. If you search for “sectional couch,” for instance, an ad for that type of furniture at Overstock.com may very well appear at the top of your search results on Google. And then later, Facebook will show you an ad for sectional couches at Overstock.
Chris Robison, Overstock’s lead data scientist for marketing, has overseen the pieces of technology that contribute to the company’s effort to bid for ads across various advertising platforms. Among the technologies in place to perform the work were Teradata, Python, Jupyter Notebooks, Apache Spark, Scala, and a large Hadoop cluster. There were many of today’s leading-edge technologies in place, Robison told InformationWeek in an interview. But those technologies were siloed.
The problem was gigantic. How do you know when a customer is most likely to purchase? Robison’s team wanted to assign scores to customers based on their likelihood of purchasing, and was working to better understand customer browsing and purchasing behavior.