Big Data Analytics at Walmart, the World’s Largest Retailer

Walmart is a multinational corporation of chain hypermarkets, discount department stores, and grocery stores founded by Sam Walton in 1962. Walmart began as a small retail store in rural areas in 1962 and has since grown to become one of the largest retailers in the world. Walmart has more than 220 million customers and members visit 10,500 businesses and clubs in 24 countries each week. Walmart uses analytics in a variety of methods to increase the number of people that visit their platform, which is available both online and offline. As a result, Walmart is constructing private clouds capable of processing 2.5 petabytes of unstructured data each hour from 1 million consumers.

Walmart Data Café

Walmart has developed its Data Café, a state-of-the-art analytics centre based within its Bentonville, Arkansas headquarters, to make sense of all of this data and put it to work addressing issues. The Data Café allows huge volumes of internal and external data, including 40 petabytes of recent transactional data, to be modelled, changed, and shown in real-time. Furthermore, Data Café provides automated warnings when important metrics in any department fall below a benchmark, alerting the team. Sales analysts were able to detect real-time information to track product sales. Candy canes, for example, were popular throughout the Christmas season, yet they were not selling at all. The notice prompted a quick investigation, which discovered that the candy had been left on the shelf due to a simple stocking error. Data Café collects information from 200 sources, including meteorological data, economic data, Nielsen data, telecom data, social media data, gas pricing, and local events databases. Anything in these massive and diverse databases could be the key to solving a specific problem, and Walmart’s algorithms are designed to analyse data in microseconds and give real-time solutions.

How does Walmart Use the data?

Walmart is using big data analytics to improve and personalise overall consumer experiences, which is a critical strategy for many companies’ success. Let’s take a look at how Walmart makes use of big data analytics. In the pharmaceutical industry, big data is being used to boost efficiency. Walmart has about 5,000 pharmacies and uses big data analytics to determine how many prescriptions are filled each day and when the busiest times are. This information helps Walmart in scheduling, lowering costs and shortening the time it takes to fill prescriptions.

Walmart is also working with big data to optimise the checkout process. Walmart needed to use predictive analytics to forecast demand at specific times and determine how many employees were needed at the counter. Walmart can use data to determine the best types of checkout for each store, such as self-checkout. Furthermore, Walmart Pay allows shoppers to pay using their phones instead of cash or credit cards at the counter. The scan and go features allow you to skip the checkout line.

If you’ve ever been to Walmart, you’ll notice a banner that says, “Everyday cheap price.” Why is Walmart able to sell at a lower price? Supply Chain Management. Supply chain management collaborates with several systems, including payments, inbound and outbound inventory, customers, and third-party sellers, to ensure that data from each system is delivered as input to various customised machine learning algorithms that forecast inventory across Walmart network. Walmart fed a variety of key metrics into its forecasting algorithms, such as demographic data, product data including frequency, quantity seasonality and replenishes its inventory with the lowest cost of manufacturing, ordering it in advance from several vendors with cheaper shipping costs. Resulting in everyday low prices. Moreover, Walmart keeps track of the number of steps taken from the shipping dock to the final client and the number of times a product is handled. Because there is a risk of damage every time a product is touched. As a result, Walmart’s employees are only permitted to touch freight once it has arrived at the store. It’s best if you take as few steps as possible.  

As previously stated, 200 sources of data were gathered to analyse buyer preferences to build a consistent and enjoyable shopping experience. For example, when you’re looking for a new phone, Walmart can use data analytics to tailor special offers to you. By analysing customer preferences and purchasing trends, Walmart can make faster decisions about how to stock store shelves and display things. Big data may assist you in determining what’s new, what’s out of style, and which private brands to stock. Additionally, WalmartLabs also developed Social Genome, a big data analytics technology that analyses billions of social media conversations, tweets, videos, blog posts, and more. Walmart uses the Social Genome analytics tool to reach out to consumers or friends who tweet or mention Walmart products to tell them about the product and give them a special discount. 

Walmart has access to a large amount of data as well as the resources to manage it. Walmart’s ability to react quickly to data is something that any company can learn from. Data-driven retailing is a game changer in that transforming the retail industry by personalised shopping is taking over tailored information about new items and services.