According to Forbes rankings for 2020, Walmart holds the top spot for the largest publicly traded retailer. Driving sales at such a massive volume and commanding market share — in 27 countries, no less — requires a data-driven approach to doing business. Never has this been more evident in the wake of the pandemic as companies have had to figure out how to adjust their business models and adapt on the fly to changing customer demand.
There are many ways companies like Walmart use sales analytics in decisions making — here are a few.
Five Walmart Use Cases for Data Analytics
Boosting efficiency in pharmacies: Analyzing data pertaining to transactions by the time of day and month helps the company anticipate customer demand. Analytics is also integral in staffing the pharmacy efficiently.
Make the checkout process better: This retail giant is gearing up to apply prescriptive analytics to registers to help anticipate demand and smooth out staffing. This application of data also helps retailers strike the right balance between traditional cashiers and self-checkout stations.
Streamline the supply chain: With the goal of making the path from manufacturing to arrival on the shipping dock as simple as possible, data analytics can help retailers understand the journey from start to finish. The company reports they are able to use data analytics to reduce transportation costs and schedule drivers efficiently using transportation-related data.
Optimizing product lineup: Armed with data pertaining to customer behavior and display information, the company is able to use a data-driven approach to stocking shelves and carrying products/brands.
Offering a more personalized shopping experience: Customer analytics helps retailers like Walmart anticipate shopper’s wants and needs and provide personalized offers.
The State of Sales Analytics Today
One specific way in which Walmart harnesses sales analytics on the ThoughtSpot platform aims to give a wide variety of decision-makers — merchandisers, finance specialists, eCommerce experts, and line executives — the ability to directly query tens of billions of data rows. With the insights they access, employees are able to impact dynamic pricing, markdowns, inventory levels, and more.
This use case underscores the importance of accessibility and immediacy when it comes to data insights. Any lag in connecting decision-makers with insights — or any difficulties they encounter along the way — can result in missed opportunities. As one analyst for Walmart’s Data Café noted, having to wait a week or month for a sales analysis means “you’ve lost sales within that time.”
Today’s analytics connect stakeholders to near real-time insights a few different ways. The first is through search-based tools, in which users can ask specific questions and receive data visualizations illustrating the answers. While this helps users pull specific insights on an as-needed basis, it doesn’t address all the questions left unanswered — either because they haven’t come up yet or because users haven’t had the time.
Artificial intelligence-driven tools, on the other hand, automatically mine data to uncover insights that may have otherwise been missed before alerting human decision-makers about these potentially useful patterns. With such large data stores — growing every day — retailers are harnessing AI analytics to automate the data mining process and get the resulting unearthed insights in front of people who can act upon them.
Companies like Walmart use sales analytics to cultivate a better, more immediate understanding of customers and product performance for the employees making daily decisions — among many other key functions. Search analytics helps stakeholders answer specific questions without having to wait for static reports, while AI analytics uncovers insights from deep within data and brings them to the surface.