Big Data and Retailers
Retailers collect and maintain sales records for large number of customers. The challenge has always been to put this data to good use. Ideally, a retailer would like to understand the demographic characteristics of its customers and what types of goods and services they are interested in buying.
The continued improvement in computing capacity has made it possible to sift through huge volumes of data in order to find patterns that can be used to forecast demand for different products, based on customer characteristics.
Another issue that big data can help with is pricing strategies, specifically understanding how sensitive different customers are to prices. Choosing the right price for a product has sometimes been based on guesswork. By contrast, big data can increase the retailer’s ability to use customer habits to identify the profit-maximizing price for their goods. Another benefit to using big data is that retail stores can better plan the placement of merchandise throughout the store, based on customer shopping habits.
Big data can also help retailers with inventory management. Many retailers sell a wide variety of different products, and keeping track of this information is a huge challenge. With big data, retailers can have instantly updated information about the size and location of their inventories.
One of the most important uses of big data for a retailer is the ability to target individual consumers with promotions based on his or her preferences. Such targeting not only increases the efficiency of advertising, it gives customers a more personal relationship with the retailer, thereby encouraging repeat business. In addition, knowledge of customer preferences enables the retailer to provide recommendations for future purchases, which further increases repeat business.
As an example, Nordstrom has heavily embraced the use of big data. It was one of the first retail stores to offer customers the option of shopping online. The company has developed a smartphone app that lets customers shop directly from their iPads, iPhones, and other mobile devices. Nordstrom also shows customers which of its stores carries specific merchandise; for merchandise that must be ordered from other stores, Nordstrom can provide a highly accurate estimate of delivery time.
Nordstrom uses its big data capabilities to target customers with personalized ads based on their shopping experiences. This information can come from Nordstrom’s store sales, its website, and from social media sites such as Facebook and Twitter.
Nordstrom conducts research into improving the customer shopping experience through its Innovation Labs division. It created this division in 2011 in order to ensure that the company remains on the cutting edge of big data technology.
Walmart is another major retailer that has embraced big data. Based on sales volume, Walmart is the largest retailer in the United States. It’s also the largest private employer in the country.
In the past few years, Walmart has made a major push into e-commerce, enabling it to compete directly with Amazon.com and other online retailers. In 2011, Walmart acquired a company called Kosmix to take advantage of that company’s proprietary search engine capabilities (Kosmix was renamed Walmart Labs).
Since then, Walmart Labs has developed several new products based on big data technology. One of these is called Social Genome, which enables Walmart to target individual customers with discounts based on preferences the customers have expressed through various sites on the Internet. Another product developed by Walmart Labs is Shoppycat, an app that provides gift recommendations based on information found on Facebook.
Although e-commerce still accounts for a relatively small percentage of Walmart’s annual revenues, the investments the company has made in big data technology show that it expects online sales to become a progressively more important source of revenues in the future.
The best example of using big data in retail is Amazon.com, which couldn’t even exist without big data technology. Amazon started out selling books and has expanded into just about every area of retail imaginable, including furniture, appliances, clothes, and electronics. As a result, Amazon raked in $89 billion in revenues in 2014, making it one of the top ten retailers in the U.S., and the largest online retailer.
Like online retailers, Amazon uses big data for several applications:
Managing its massive inventories
Accurately keeping track of orders
Making recommendations for future purchases
Amazon provides its recommendations through a process known as item-to-item collaborative filtering. This filtering is based on algorithms designed to identify the key details that can lead a customer to purchase a product, such as past purchases, items viewed, purchases made by customers with similar characteristics, and so forth. Amazon also provides recommendations by email, chosen based on the highest potential sales.
Amazon has been able to put its investment in big data capabilities to good use in another way: It now earns revenues by allowing businesses to use its infrastructure for a fee. This is done through products such as Amazon Elastic MapReduce (EMR) and Amazon Web Services (AWS).
Amazon EMR enables businesses to analyze enormous quantities of data by using Amazon’s computer hardware. This hardware is accessible through the Amazon Cloud Drive, where businesses can pay to store their data. For many businesses, using these facilities is cheaper than building the computer infrastructure that would be required to handle the demands of big data. AWS provides a large variety of computer services through Amazon Cloud Drive, including storage facilities, database management systems, networking, and so on.
One interesting extension of Amazon’s use of big data is its plan to ship merchandise to customers before they order it! The company received a patent in 2014 for its “anticipatory shipping” methodology. In order for this plan to succeed, Amazon.com must be able to anticipate customer demand with an incredibly high degree of accuracy to avoid the risk of returned merchandise.