Guest Column | September 3, 2021

5 Powerful Examples Of Retail Analytics

By Shannon Flynn

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Information is the world’s most valuable resource. Humans generate 2.5 quintillion bytes of data every day, and analytics can help retailers capitalize on this wealth of information. While data analytics in retail is a relatively new concept, it’s already produced impressive results.

In today’s increasingly competitive market, retail analytics can be the difference between becoming an industry leader and falling behind the rest. Here are five examples of how analytics is helping retailers today.

1. Personalized Marketing

Targeted marketing is the most recognizable use case for retail analytics today. Consumers’ online behavior reveals a lot about them, and you can use this data to target more specific niches, improving marketing efficacy. One of the most famous examples of how powerful this can be is Target’s pregnancy predictions.

Target statisticians were able to determine if a customer was pregnant by analyzing their purchase behavior. The store could then send them coupons for baby-related products, encouraging them to shop there. Modern analytics algorithms can perform similar work on a far larger scale and do so faster.

2. Supply Chain Management

Other applications of data analytics in retail can reduce companies’ operational costs. Studies show that some retailers have seen 60% reductions in operating margins by applying analytics to supply chain management.

Analytics engines can look at supplier and transport costs, travel distances, and warehouse operations to determine where inefficiencies lie. Data scientists can then take this information and see how different changes might affect expenses. You can then find the most efficient and affordable supply chain workflows possible.

3. Demand Forecasting

The same analytics that enable personalized marketing initiatives also allow demand forecasting. As you gather data over time, seasonal trends will emerge. Analyzing these historical stats can help predict when similar shifts will happen in the future, letting you optimize inventory in response.

Nestlé managed to make multimillion-dollar reductions in inventory by using analytics for demand forecasting instead of human judgment. The system went beyond seasonal trends to run “what-if” scenarios about various demand indicators. They proved to be reliable, letting Nestlé reduce its inventory safety stock by 20%.

4. Scouting New Store Locations

A less publicized but highly valuable use case of analytics in retail is finding optimal locations for a new store. Manually studying land costs, local demand, neighborhood demographics, and other factors can be challenging and time-consuming. Data analytics engines can automate it, finding the best place to open a new store in minimal time.

Both Wendy’s and Starbucks use geospatial data analytics to find profitable new locations. Their algorithms consider everything from traffic patterns to average income to nearby businesses to determine which areas hold the most potential.

5. Fraud Prevention

Many banks today use data analytics for fraud prevention, and retailers can, too. Rising e-commerce introduces fraud risks in checkout or return processes, but data analytics systems can highlight which cases are genuine and which are likely fraudulent.

One fashion retailer saved $10,000 worth of merchandise from fraud after its analytics solution noticed 93 separate orders were going to the same address. Humans may be able to spot these red flags, too, but analytics software can do it far faster, preventing issues before they create losses.

Retail Analytics Separate High Performers From The Rest

Data analytics are an increasingly crucial part of modern retail. While retail analytics may not yet be critical to a company’s success, it separates the highest performers from those that fail to stand out. With so much information available today, overlooking analytics’ potential translates to considerable lost revenue.

About The Author

Shannon Flynn is a technology blogger who writes about AI and IT trends. She's also the Managing Editor of and freelances for sites like IoT for All, ChatbotNewsDaily, and more. Follow her on Medium or MuckRack to read more tech news.