Retail After Covid, Part 3: AI in the Retail Sector

Stephen DeAngelis

July 21, 2021

“The ripple effects of the pandemic will be felt for some time and serve as a powerful illustration of the need for consumer-facing companies to be agile, resilient, and responsive to change,” asserts Oliver Wright (@owright001), senior managing director and head of Accenture’s global consumer goods industry group.[1] As I discussed in Part 1 of this article, retail is recovering but must adapt to the realities of the post-pandemic business landscape if the recovery is going to succeed. In Part 2, I discussed some of the steps experts believe retail and consumer packaged goods (CPG) manufacturers must take in order to adapt, including leveraging advanced cognitive technologies (aka artificial intelligence (AI)). In this final installment of the article, I want to discuss why experts believe leveraging AI is essential to retail’s future. Vaibhavi Tamizhkumaran, a Digital Marketing Executive at Indium Software, asserts, “The global transition to online shopping has wrought unprecedented shifts in the retail industry. The retail industry is constantly changing and will continue to evolve, from concentrating efforts on website growth and online retail to needing faster shipping speeds. With all of the changes in the retail environment and the continued shift away from conventional technologies, cognitive computing in retail is becoming increasingly important.”[2]

 

AI in the Retail Sector

 

Bain analysts, Michael C. Mankins and Lori Sherer (), assert, “The best way to understand any company’s operations is to view them as a series of decisions.”[3] That’s as true for CPG manufacturers and retailers as it is for any other business. Christina Bieniek, Chief Commercial Officer at Deloitte, explains, “If you work in retail, decision making might not be the first thing you think of when asked what you do all day. But think about how many decisions you make — what to sell, where to display it, how much to charge, when to mark it down and countless other decision points in your role. To succeed in retail, you must reflect on those decisions and whether they are driving the outcomes you want.”[4] Cognitive technologies, embedded with advanced analytics can help improve those decisions. Mankins and Sherer note, “We know from extensive research that decisions matter — a lot. Companies that make better decisions, make them faster and execute them more effectively than rivals nearly always turn in better financial performance. Not surprisingly, companies that employ advanced analytics to improve decision making and execution have the results to show for it.”

 

Bieniek agrees. She bluntly states, “The future of retail belongs to those who make the best decisions.” Going with your gut is not the best way to make decisions. The best decisions are based on data. Bieniek explains, “Making decisions based on data allows you to be more predictive. By applying today’s advanced technologies like artificial intelligence and machine learning, you can spot patterns our human logic might miss. This can be done in a very targeted way or across the entire value chain.” Not all data is of equal value. Anil Kaul (@anil_kaul), co-founder and CEO of Absolutdata, insists analyzing the right data is essential for retail success. He writes, “With advanced technology that allows [retailers] to integrate data sources and create digital twin frameworks to simulate and predict consumer behavior, retail leaders can scale profitably across brands, categories, countries and business units.”[5] He goes on to suggest, “Those that fully embrace advanced technology can improve margins and manage growth with AI-generated recommendations to drive five vital retail functions.” Those functions are:

 

Pricing: “One significant pain point for retailers,” Kaul writes, “is the inability to visualize pricing trends across categories and channels. Pricing for some items has been based on seasonal factors, but with advanced analytics, retailers can get a high-level view of trends, hyper-target customers using real-time data, and localize pricing to maximize profits.”

 

Promotions: According to Kaul, “AI-generated recommendations let retailers drive category expansion, improve forecasting, and optimize market share.”

 

Assortments: Kaul writes, “Retailers need a better way to make assortment decisions at regional and store levels, and AI can provide SKU recommendations that free retailers from old-school calendar-centric resets. With advanced analytics, retailers can spot opportunities and execute demand-based assortment strategies, responding quickly to customer-driven trends.”

 

Spending: “Intelligent spending optimization models,” Kaul explains, “can help retailers improve bottom-line results while reducing waste and increasing investment in profitable activities. AI can provide recommendations that allow retailers to align spending with shopper behavior while identifying ways to cut costs, including automation of required activities for greater efficiency.”

 

Trade promotion: We know from our work in this area that AI solutions, like the Enterra® Trade Promotion Optimization Solution™, can provide significant return on investment. Kaul writes, “Retailers can adopt an AI-driven trade promotion intelligence (TPI) strategy that is faster, smarter and more comprehensive than the standard trade promotion optimization (TPO) approach. With advanced analytics, retailers can receive recommendations that consider a broader range of real-time consumer data, and that lets them develop more accurate forecasts and create effective optimization plans.”

 

The kind of solutions Kaul discusses above take advantage of cognitive computing capabilities, like those found in the Enterra Cognitive Core™, a system that can Sense, Think, Act, and Learn®. Tamizhkumaran explains, “Cognitive analytics solutions entail self-learning systems and algorithms that mimic the human brain’s thought process in order to analyze large amounts of data quickly and accurately that no person could evaluate vast quantities of data and come to the same conclusions. As they are exposed to more data, these algorithms, like humans, become more intelligent. Cognitive computing is capable of understanding natural language, comprehending images, recognizing patterns, and much more.”

 

Concluding Thoughts

 

Bieniek writes, “We saw how important data-driven decision making is over the past year and a half. When the pandemic hit, consumer behavior from shopping habits to commutes changed drastically almost overnight. Companies making decisions based on past sales or gut instinct were suddenly flying blind. The businesses that kept or even gained customers were those that blended internal and external consumer data streams to proactively assess consumer needs and wants in real time.” She concludes, “The bottom line: competing in the future of retail is all about improving your decision making to better drive the outcomes that you want. Embracing today’s data and analytics tools, including AI and machine learning, ultimately allows you to make the decisions that keep you on course.” Kaul adds, “With AI-driven recommendations and machine learning features that ensure analytics get smarter over time, retailers can understand new buyer journeys across digital assets and brick-and-mortar operations. These new capabilities will enable the retailers that embrace them to not only navigate uncertainty during and after the pandemic, but to gain a long-term competitive advantage.”

 

Competitive advantage often results when opportunities are recognized and seized. Wright asserts, “Born out of disaster and necessity comes opportunity; the pandemic has sparked a new wave of innovation.” Much of that innovation is thanks to cognitive computing capabilities. Tamizhkumaran explains, “Retail cognitive computing and cognitive technology have ushered in a slew of new innovations. Cognitive computing has given retailers the tools to become more agile in business through demand forecasting, price optimization, and website design.” That’s why she insists cognitive analytics are reinventing the post-pandemic landscape for the retail sector.

 

Footnotes
[1] Daphne Howland, “5 signs that retail is going to be OK,” Retail Dive, 12 May 2021.
[2] Vaibhavi Tamizhkumaran, “Is Cognitive Analytics Reinventing A New Landscape For Retail Sector?” Indium Software Blog, 7 April 2021.
[3] Michael C. Mankins and Lori Sherer, “Creating value through advanced analytics,” Bain Brief, 11 February 2015.
[4] Christina Bieniek, “The future of retail belongs to those who make the best decisions,”” Retail Dive, 3 June 2021.
[5] Anil Kaul, “5 Ways AI Helps Retailers Get Ready for the Post-Pandemic Economy,” Total Retail, 12 April 2021.