How Amazon uses Big Data in practice
Amazon has thrived by adopting an “everything under one roof” model. However, when faced with such a huge range of options, customers can often feel overwhelmed. They effectively become data-rich, with tons of options, but insight-poor, with little idea about what would be the best purchasing decision for them.
To combat this, Amazon uses Big Data gathered from customers while they browse to build and fine-tune its recommendation engine. The more Amazon knows about you, the better it can predict what you want to buy. And, once the retailer knows what you might want, it can streamline the process of persuading you to buy it – for example, by recommending various products instead of making you search through the whole catalogue.
Amazon’s recommendation technology is based on collaborative filtering, which means it decides what it thinks you want by building up a picture of who you are, then offering you products that people with similar profiles have purchased.
Amazon gathers data on every one of its customers while they use the site. As well as what you buy, the company monitors what you look at, your shipping address (Amazon can take a surprisingly good guess at your income level based on where you live), and whether you leave reviews/feedback.
This mountain of data is used to build up a “360-degree view” of you as an individual customer. Amazon can then find other people who fit into the same precise customer niche (employed males between 18 and 45, living in a rented house with an income of over $30,000 who enjoy foreign films, for example) and make recommendations based on what those other customers like.
The technical details
Amazon collects data from users as they navigate the site, such as the time spent browsing each page. The retailer also makes use of external datasets, such as census data for gathering demographic details.
Amazon’s core business is handled in its central data warehouse, which consists of Hewlett-Packard servers running Oracle on Linux.
Ideas and insights you can steal
Too much choice and too little guidance can overwhelm customers and put them off making purchasing decisions. Recommendation engines simplify the task of predicting what a customer wants, by profiling them and looking what people who fit into similar niches buy. In this way, developing a 360-degree view of your customers as individuals is the foundation of Big Data-driven marketing and customer service.
You can read more about how Amazon is using Big Data to drive success in Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results.
Bernard Marr is an internationally bestselling author, futurist, keynote speaker, and strategic advisor to companies and governments. He advises and coaches many of the world’s best-known organisations on strategy, digital transformation and business performance. LinkedIn has recently ranked Bernard as one of the top 5 business influencers in the world and the No 1 influencer in the UK. He has authored 16 best-selling books, is a frequent contributor to the World Economic Forum and writes a regular column for Forbes. Every day Bernard actively engages his almost 2 million social media followers and shares content that reaches millions of readers.