5 Ways Walmart Utilizes Data Science to Achieve Profit

Ever wonder how Walmart stays on top as one of the biggest retailers in the world? Turns out, data science plays a huge role in keeping them ahead of the competition and driving profits. In this blog, we’ll dive into five ways Walmart uses data science to optimize everything from inventory to customer experience!

How Walmart Uses Data Science to Drive Profits

Walmart is using data science to stay ahead in the retail game. Millions of transactions happen every day at thousands of stores, and Walmart collects massive amounts of data. It's not just about gathering data, it's all about how they use it and data science techniques to make faster and smarter decisions to enhance efficiency and improve customer experience.

Walmart Optimizes Inventory Management with Predictive Analytic

Walmart is applying data science techniques in almost all of its operations. From analyzing customer behavior to predicting what products will be in demand, they use data science to make smarter decisions. One of its key strategies is to use data to streamline its supply chain. Efficient inventory management with predictive analysis helps them keep shelves stocked with the rights products while cutting down expenses and maximizing profits.

They can predict demand and make sure products are in stock when customers need them. This helps them reduce costs and maximize sales and profits. Data science helps improve Walmart's operational efficiency, from managing staff levels to checking areas where energy usage can be minimized.

Walmart Enhances Customer Experience with Personalized Recommendations

Ever noticed how Walmart always seems to know what you need before you even think of it? That's because they are using data science to give you personalized recommendations. Let's say you often buy skincare products, but also end up buying home decor or wellness-related products that match with your interests because they keep on showing in suggestions. These personalized recommendations don't stop at what you are actively looking for. Based on your purchase and search history, they anticipate what you might need next, all happening because of data science.

For example, if you have been buying Buldak Ramen frequently, you might also receive recommendations for tteokbokki, kimchi, chopsticks, and more. Walmart picks up on your food preferences and suggests related products. To learn how customer insights are transformed into effective marketing strategies through data science, check out this blog on data science in marketing.

Walmart Streamlines Supply Chain Efficiency with Real-Time Data

Since Walmart has a vast network of distribution centers and suppliers, they use advanced analytics to monitor inventory levels and predict demand. Using real-time data helps them streamline their supply chain efficiency, making sure the right products are delivered to the right location at the right time. They use RFID technology and sensors to track the supply of products from suppliers to stores. With the help of real-time data, they can identify bottlenecks or delays as they happen.

If a shipment is running late, the system notifies the manager who can then adjust and reroute delivery or reallocate stock from another nearby location to meet the needs of the customer. Real-time data also helps them predict spikes in demand by analyzing customer purchasing patterns. To explore more examples of how data science is used in retail, read about various retail data science use cases in this blog.

Walmart Optimizes Pricing Strategies with Machine Learning

Every week, Walmart serves over 265 million customers worldwide, hence effective pricing strategies are important to its success. To serve a vast customer base and ensure competitive pricing, Walmart uses advanced machine learning techniques. To stay competitive as one of the biggest retail company, one of their key pricing strategy is dynamic pricing. Dynamic pricing allows them to adjust prices depending on seasonal trends and local market conditions.

If demand for a certain product surges or if a competitor has lowered the price, Walmart can swiftly adapt by adjusting its own prices. Another strategy is everyday low prices (EDLP), it's a model that allows customers to feel confident they are getting things in low prices without waiting for sales of promotions. All of this is being done by advanced machine learning algorithms.

Walmart Improves Store Layouts with Data-Driven Insights

Ever walked into a Walmart and thought, “Why is this aisle so long?” Well, it turns out that there’s a method to the madness! Walmart is all about using data-driven approaches to improve store layouts and make your shopping experience smoother.

So here's the deal: Walmart collects massive data on customer behavior. They track where people go, what they buy, and how long they stay in certain areas. Using this data they can analyze which aisles are popular and which ones are totally ignored. This helps them to figure out how to rearrange the store to make sure customers find what they need, and also putting snacks near the checkout to tempt you when you’re waiting in line.

Walmart Data Science Case Studies

Want to test your Data Science skills on real-life case studies used by Walmart? Check out these interactive Walmart SQL questions:

• Histogram of Users and Purchases
• Frequently Purchased Pairs If these questions feel a bit too challenging, you can always check out the Free SQL Tutorial from DataLemur for some guidance.

And just a little nudge from me: I highly recommend the book Ace the Data Science Interview because it covers multiple technical interview questions from Meta, complete with solutions to help you prepare!