Big data volume continues to grow, but Walmart is using it to the company’s — and its customers’ — advantage. By analyzing the robust information flowing throughout its operations, the discounter has gained a real-time view of workflow across its pharmacy, distribution centers, stores and e-commerce, according to a company blog.
Here are five ways that Walmart is using big data to enhance, optimize and customize the shopping experience:
1. To make Walmart pharmacies more efficient. By analyzing simulations, the discount giant can understand how many prescriptions are filled in a day, and determine the busiest times during each day or month. Big data also helps Walmart schedule associates more efficiently, and reduce the time and labor needed to fill perceptions.
2. To improve store checkout. While it is still only testing the process, Walmart is using predictive analytics to anticipate store demand and determine how many associates are needed to man registers. The data also reveals the best form of checkout at each store: traditional stations or self-checkout.
3. To manage the supply chain. The company uses simulations to track the number of steps from the dock to the store. The result: more optimized routes to the shipping dock. The strategy also pinpoints the number of times a product gets touched along the way to the customer. Big data also reveals transportation lanes and routes for the company’s fleet of trucks. This insight helps Walmart keep transportation costs down and more accurately schedule driver times, according to the blog.
4. To optimize product assortment. By analyzing customer preferences and shopping patterns, Walmart can optimize how to stock shelves and display merchandise. Big data also provides insight into new items, discontinued products and which brands to carry, the blog said.
5. To personalize the shopping experience. By analyzing shopper’s preferences, Walmart can develop a more consistent, tailored shopping experience. If a customer is shopping for baby products for example, Walmart can use data analytics to anticipate their needs then create personalized mobile rollback deals for these shoppers.