5 Ways How Amazon Uses Big Data

June 10, 2020 • Shannon Flynn

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Wondering how Amazon uses big data? What began as a modest bookselling business is now a global digital empire. Amazon still delivers books and everything else under the sun, but a substantial portion of the company’s earnings now come from web hosting and digital services.

Big data is at the heart of Amazon’s retail and digital services empire. Access to information on millions of customers across the world gives the brand the insight it needs to match customers with product recommendations, maintain competitive prices and keep distribution centers in sync. Here’s a look at how Amazon uses big data.

1. Delivering Personalized Product Recommendations

First and foremost, Amazon capitalizes on big data for one of the most essential reasons in retail today — providing personalized product recommendations. Amazon’s systems improve the customer buying experience by analyzing:

  • Site traffic in real-time
  • Past viewed products 
  • Recent purchases or returns
  • Product ratings and reviews

This setup is great for customers, but it’s also great for Amazon. Research attributes around 35%of the brand’s total sales to recommended purchases. From upselling during a purchase to connecting customers with items they didn’t even know they needed or wanted, this level of personalization benefits both parties.

2. Safeguarding Inventory Levels

Personalizing the shopping experience with big data also helps unlock insights into product “dependencies.” Without big data, it’s much harder for companies to spot relationships between items frequently purchased together.

Learning about these relationships helps Amazon and other retailers fine-tune inventory levels to keep up with demand. Big data systems can find community-, region- and season-based patterns in product dependencies, too, which helps with the geographical distribution of inventory across fulfillment centers.

3. Offering Targeted Book Suggestions

Amazon purchased Goodreads.com in 2013. This move was partly to expand the reach of the Kindle bookstore and partly to boost Amazon’s access to reader data. This information includes reading habits like the time spent reading each book and which words and phrases users have highlighted or looked up within their purchased Kindle books.

Using big data analytics, Amazon reviews and learns from user highlighting and the time each customer spends reading their Kindle purchases. These details provide insights into the types of works the reader enjoys, what they don’t like and sheds light on the topics they might want to know more about. The result is more relevant eBook recommendations in real-time and a “stickier” ecosystem for readers.

4. Perfecting the Anticipatory Shipping Model

Amazon is a trailblazing business model in many ways. Nothing exemplifies this concept quite like the company’s patent filing for what it calls “anticipatory shipping.” This setup is a longstanding dream among internet retailers, but access to abundant customer data means Amazon will be the first to capitalize on it in a major way.

Anticipatory shipping leverages the following customer data:

  • Purchase history
  • Wish list contents
  • Website searches
  • Items in shopping carts

With this data in hand, Amazon’s prediction algorithms can come to confident conclusions about which items the customer will purchase in the near future.

From there, Amazon’s supply chain springs into motion to get the products in question to a fulfillment center as close to the customer’s location as possible. As soon as the customer commits to the purchase, the system prints a shipping label and moves the product into the last mile of delivery.

5. Dialing-In the Right Price for Everything

Prices on Amazon.com change like clockwork. The retailer performs price adjustments a stunning 2.5 million times each day — a price change every 34.56 milliseconds. It’s also 50 times as often as chief competitors Walmart and Best Buy change their own prices.

This dynamic pricing strategy has helped Amazon boost its profits by 25%. It doesn’t mean the retailer has the lowest prices on every item, but achieving attractive pricing on the most visible and demanded products creates an impression among customers.

Yet again, big data makes this level of profitability and competitiveness possible. Amazon dials-in perfect pricing by training their algorithms on variables like time spent on the site, browsing history, Amazon’s — or a partner’s — available inventory, competitors’ prices, past purchases and the company’s desired margins.

A Retailer Becomes a Big Data Giant

Amazon became a retail powerhouse first and a digital services company second. Today, these two identities complement each other perfectly.

Using all of the customer data at its disposal, the brand has zeroed-in on a business model that’s convenient and comfortable for users while boosting efficiency and profitability for the business itself. In short, this is how Amazon uses big data.