6 Retail Big Data analytics use cases and examples

The explosion of data in the last decade has transformed every industry, but perhaps none more than retail. The rise of big data has been one of the most important trends in data analytics, revolutionizing the way retailers operate in today’s increasingly competitive markets. With massive amounts of information at their disposal, often in a cloud data platform, organizations are now able to use retail big data analytics tools to unlock new insights and drive improvement across every facet of their business. This has allowed them to gain a better understanding of their customers, better manage inventory, enhance marketing campaign efficiency, optimize pricing, and boost overall performance.

In this post, we will explore six different use cases for retail big data analytics and discuss some examples that companies have used to utilize these powerful methods.

1. Hyper customer segmentation

Consumers today demand personalization and bespoke experiences wherever they interact with retailers. By taking advantage of cross-channel data, retailers can easily group customers together according to their preferences and buying habits beyond simple aggregates and demographic breakdowns. By building these highly targeted, granular customer segmentations, retailers can better identify target audiences for promotions, streamline campaigns, and improve overall customer satisfaction. Companies like Neobank Northmill have used customer segmentation to create a more personalized banking experience, which has in turn led to a boost of conversions by 30%.

2. Inventory management

Retailers live or die by their ability to manage inventory, ensuring what customers want is available when and where they want it while minimizing products being over- or out-of-stock. With retail analytics, businesses can quickly understand customer purchasing patterns, predict future patterns, and optimize inventory. For example, Canadian Tire used self service BI from ThoughtSpot to quickly identify changing demands from customers and shift inventory in the early days of the pandemic. In doing so, they were able to grow sales by 20%, despite 40% of their brick and mortar locations being shut down during quarantine.

3. Price optimization

Pricing is a fine line for retail organizations. Charge too little, and you’re leaving money on the table. Too much, and consumers will look elsewhere. The most successful retailers aren’t optimizing pricing weekly or even daily; they’re doing so multiple times a day. By leveraging real-time pricing data from competitors, sentiment data from social media, their own sales data and other data sources, retailers can identify which prices are most likely to generate the greatest profits, and adjust these as quickly as they market moves. This helps them configure optimal pricing structures across channels in order to maximize revenue potential.

4. Supply chain optimization

Supplies chains have never been more complex, nor more important to optimize, for retailers than today. Everything from customer satisfaction to profit margins depend on a resilient, optimized supply chain. By collecting large amounts of supply chain data, such as shipping times, inventory levels, and supplier availability, and exposing this data to supply chain and logistics specialists through self-service analytics, retailers can optimize their processes to reduce costs and improve efficiency. For example, ThoughtSpot customer Nike leveraged retail analytics to optimize their supply chain in their distribution centers to cut down on delivery time and keep customers happy.

5. Fraud detection

Fraud can dramatically impact retailers, damaging relations with customers and eating into revenue. By leveraging machine learning algorithms and natural language processing technologies, retailers can predict and detect fraudulent transactions before they occur, protecting both the customer and the business from potential losses. These fraud detection systems are the most powerful when the detection models are added into a business intelligence platform that allows risk managers and fraud detection teams to explore data, visualize findings, and share information with other teams.

6. Automated marketing campaigns

By utilizing data-driven marketing tools such as email campaigns and social media ads, retailers can send personalized messages directly to their customers and promote their products in the most effective manner. Ideally, these are part of a modern data stack, where reverse ETL processes can be used to sync insights and trigger actions between marketing tools and a cloud data platform.

Start optimizing your retail strategy for growth

Retail big data analytics can be a powerful tool for businesses of any size to gain insights into customer behavior and make informed decisions about their operations. The most valuable retail analytics solutions extend the power of data beyond technical teams, enabling everyone in a retail organization, from merchandisers to marketers to supply chain managers, to ask and answer their own questions.

Retailers like Unilever, Avon Cosmetics, and Canadian Tire are doing so with safe, reliable self-service analytics from ThoughtSpot. By taking advantage of the free trial offer, retailers can take their first steps into the world of big data and gain instant access to valuable insights in no time at all. With the right approach, successful transformations will follow. Happy searching!

Data Trends

10 data-driven KPIs and metrics for retail decision makers

Read more


8 retail analytics benefits you should know about

Read more

Data Trends

6 effective inventory optimization techniques your team should be using

Read more