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Overcoming the ‘Retail Apocalypse’

As retailers wrestle with monumental changes in the industry, they are adopting artificial intelligence and predictive analytics to pinpoint what customers will want, when they will want it and where they will want it.

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Dell EMC

The retail industry is undergoing a sea change so massive that many industry insiders have termed it the “retail apocalypse.” In a sign of this industry upheaval, in 2018 major retailers closed 5,524 stores in the U.S. and 1,432 stores in the U.K., according to figures compiled by the Coresight Research, a firm that studies the retail industry.1

But that was 2018.  In some good news for the industry, Coresight predicts that 2019 “will not be the year of retail apocalypse or even decline. Instead, it will be a year of reinvention — for the retail sector as a whole and for physical stores in particular.”2

This predicted reinvention of the industry stems in part from the use of sophisticated technology, specifically artificial intelligence. In fact, Coresight predicts that in 2019, “Artificial intelligence will become retailers’ go-to technology.”

Leveraging data insights for competitive advantage is not new to the retail industry. Retailers have been pioneers in the use of analytics, both online and off. As a result, modern retailers leverage a growing range of data sources to understand everything from customer‑buying behavior and product trends to product‑price optimization and targeted advertising.

Even so, AI is poised to take the retail industry to new heights. As neural networks become more and more efficient, and as technology gets more and more powerful, retailers are seizing the day, and using AI to predict what customers will want, when they will want it and where they will want it.

While in some industries enterprises are still kicking the AI tires, retailers are adopting artificial intelligence in droves. More than 28 percent of retailers are deploying AI today, according to a new study from the Capgemini Research Institute. That’s a seven-fold increase from 2016. Even more striking, 99 percent of retailers using AI in customer-facing functions expect AI to increase sales by up to 15 percent, the firm reports.3

Common use cases

For retailers, use cases for artificial intelligence are everywhere, from the pages of online shopping sites to the isles of brick-and-mortar stores. Let’s walk through some of the more common use cases.

Shopping assistants — Retailers are using natural language processing, chatbots and virtual assistants to provide customers with live shopping assistance. Drawing on AI on the back end, these digital shopping assistants help tech-savvy customers instantly locate the products with the lowest price and the highest quality. The digital assistants can ask questions to clarify what customers are looking for and then help them search for products and place their orders. Along the way, customers can save both time and money — while driving higher sales for the retailer.

Recommendation engines — With AI and the horsepower of high-performance computing systems, retailers can use consumers’ transaction history, social media sentiments and other structured and unstructured data to anticipate their needs in real time. This predictive power allows retailers to offer customers personalized choices as they shop.

For an example of the power of AI-driven recommendation engines, consider Amazon, and how it can predict what book or movie you might like to spend the evening with and what your next “must‑have” item will be. Research indicates that more than half of Amazon’s sales are not planned by shoppers in advance but are triggered by Amazon’s recommendation engines, which present shoppers with customized suggestions based on their current and past purchases and search histories.4

Targeted marketing — With the power of AI and machine learning at their command, retailers can now easily combine known customer sentiments and preferences with external factors, such as the weather and the day of the week, to deliver highly relevant suggestions at just the right moment. AI engines can help make those suggestions even more meaningful by determining where customers are in the buying process and tailoring the targeted marketing accordingly.

Fraud detection and prevention — Every year, the retail industry loses billions of dollars to fraud and theft. AI-powered systems can help stem the losses by highlighting suspicious behavior that deviates from normal shopping activities and by implementing rules that prevent scams from being repeated, both in-store and online.

Payment processing companies are helping with these efforts. For example, to help identify and stop fraudulent transactions, Mastercard leverages machine-learning algorithms running on HPC systems to examine each transaction against a set of 1.9 million rules as the transaction takes place.5 This capability helps Mastercard stop fraud in its tracks without disrupting or delaying legitimate transactions.

Supply chain optimization — AI systems help retailers make the journey from the warehouse to the store as efficient as possible, removing unnecessary costs, waste and merchandise delays. In one such use case, sensors and transmitters along the supply chain feed data to back end systems to warn of problems that could impede the flow of merchandise. In another use case, retailers employ predictive analytics to help determine supply chain availability and demand based on weather.

The payoff for these efforts can be substantial. A study by McKinsey & Company found that U.S. retailer supply chain operations that have adopted data and analytics solutions have seen up to a 19 percent increase in operating margin over five years.6

Inventory forecasting and stock level optimization — In physical and online retail environments, empty shelves and out-of-stock products are costly, and so are overstocked shelves. AI-powered systems help retailers avoid both of these problems by predicting purchasing patterns to help ensure that merchandise is available while reducing days in inventory and stock‑outs. With better predictions, retailers can keep inventory at an optimal level because they have a better view of the demand for products ahead of time.

Fulfillment — For growing numbers of retailers, AI is now one of the keys to filling orders in an efficient and cost-effective manner. One way in which AI comes into play is the use of autonomous robots to automate and accelerate labor-intensive pick/pack/ship processes. For example, a Forbes writer offers this view of a robot in action in an Amazon fulfillment center: When a customer places an order for a book, a robotic unit automatically brings the closest pod containing that book to the person fulfilling the order, and then the robot shines a light on the location of the book within the pod.7

Leveraging AI for sales and marketing – a case study

Customer data is everything to the Dell EMC Marketing Analytics team. Each customer touch point — whether it’s a phone call, email or online communication — represents a piece of data that thousands of Dell EMC sales professionals rely on to get more insight into their customers.

To make the most of this data, the marketing analytics team uses an advanced analytics platform on top of a robust data platform, and delivers analytical data to a select group of Dell EMC sales and marketing professionals via dashboards. This solution enables the sales team to leverage predictive analytics to target the right customer at the right time with the right product.

Key takeaways

The retail industry is in the midst of a sweeping transformation driven by new technologies and new business models. Artificial intelligence is one of the key enablers of this transformation. As Coresight Research notes in a recent report, “AI is putting new tools into the hands of retailers, helping them eliminate friction in the shopping process, deepen their engagement with customers, uncover business insights and automate processes.”8

Ready to learn more?

For an inside look at the Dell EMC Marketing Analytics team’s use of predictive analytics, read the case study “An Efficient Way to Discover New Customer Insights.” To learn more about unlocking the value of data, explore Dell EMC AI Solutions.

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1 Coresight Research, “Reviewing 2018 U.S. and U.K. Store Closures,” January 10, 2019.

2 Coresight Research, “10 Retail Trends for 2019: Get Ready for Retail Reinvention,” January 24, 2019.

3 Capgemini Research Institute, “AI in Retail Report,” December 17, 2018.

4 Navidar, “Machine Learning is Driving an Innovation Wave in SaaS Software,” August 2017.

5 Dell EMC white paper, “Fighting fraud the smart way — with data analytics and artificial intelligence,” December 2018.

6 insideHPC, “Reinventing the Retail Industry Through Machine and Deep Learning,” 2018.

7 Forbes, “Amazon’s Robot-Filled New York Fulfillment Center Gives Rivals Another Reason To Worry,” December 10, 2018.

8 Coresight Research, “AI in Retail: Putting New Tools in the Hands of Retailers

Copyright © 2019 IDG Communications, Inc.