Cognitive Technologies in the Retail Space

Stephen DeAngelis

June 1, 2021

Although every economic sector undergoes constant change, the retail sector has probably seen more changes in a shorter period of time than most other sectors. Vaibhavi Tamizhkumaran, a Digital Marketing Executive at Indium Software, writes, “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.”[1] The momentum for leveraging cognitive technology has been growing for the past few years. Some of the momentum was created by the rising importance of social media. Back in 2017, Andrew Busby (@andrewbusby), Founder and CEO of Retail Reflections, declared, “Cognitive computing is forever changing how we shop. How? Consider your social media activity. What did you post on Facebook this week? Twitter? Snapchat? You are sharing your one-of-a-kind digital footprint with the world through images, videos and chats that detail likes, dislikes, emotions and opinions.”[2] Since then, the influence of social media has only grown with Instagram and TikTok. Busby notes, “[In the past, it was] impossible to really understand the customer and create an actual one-one-one relationship with them. Cognitive computing technology taps into social media to make this a reality.” As explained below, cognitive computing can do much more for retailers than simply monitor social media.

 

The Value of Cognitive Computing in Retail

 

Cognitive computing is a subset of artificial intelligence (AI), which, like most AI systems, leverages machine learning as well as other technologies. 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.” He adds, “No person could evaluate [those] vast quantities of data and come to the same conclusions. As [cognitive computing algorithms] 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.” If you are wondering of what use these capabilities are, Muktabh Mayank Srivastava, Co-Founder and Chief Data Scientist at ParallelDots, provides a few examples for all the stakeholders in the retail supply chain from manufacturers on the supply side to consumers on the customer side.[3]

 

Retailers
• Reducing Out of Stock (OOS) Situations: You can’t sell what you don’t have. Srivastava notes that OOS can result in significant lost revenue. He explains, “A typical supermarket has 15,000+ SKUs while a convenience store has 5,000+. Ensuring the constant availability of all these products is a daunting challenge. … AI-based shelf monitoring solutions … can be used to ensure that shelves are replenished on time and the most basic OOS losses don’t take place.” Melanie Nuce (@auntmel), Senior Vice President of corporate development at GS1 US, adds, “In an effort to gain velocity in fulfillment, AI has been playing an increasing role in inventory management. However, some retail operations lack specificity in their product data management. AI needs as much information as possible to be successful, but if all product details are not set up, consumers may be presented with incomplete or inaccurate information.”[4]

 

• Forecasting: Paul Winsor (@PaulWinsor1969), general manager of retail at DataRobot, observes, “The really impactful part [of cognitive analytics] is around forecasting. We are now seeing retailers using AI and automated machine learning to operate their demand forecasting to understand the actual quantity needed today based on the demand from the customers.”[5] Srivastava adds, “New, more accurate AI-based forecasting methods can predict demand for various products depending on weather, holidays, government policies, locality, and other variables that influence sales.”

 

• Personalization of Shopping Experience: Most people understand that cognitive technologies can be used to enhance online customer experiences. Chithrai Mani (@chithraiMani), Vice President of Digital Transformation Solutions for InfoVision, explains AI can also be used in-store. He writes, “Artificial intelligence can automate in-store operations and reduce operational expenses in retail stores. It can replace sales personnel to assist customers in the store, reduce queues through cashier-less payment, replenish stock by real-time stock monitoring, and digitize store display and trial rooms.”[6] Rather than replacing sales personnel, I would recommend augmenting sales personnel. Many retailers have learned that reducing sales personnel decreases the personal touch many shoppers are seeking. Winsor insists, “If retailers want to stay open in the existing stores that they are operating in, my recommendation to them is to ask: Are they understanding the changing habits of those customers, and how they’re shopping with them, in those locations?” Data and cognitive analytics can help answer that question.

 

• Ensuring Compliance: Retailers and suppliers have a symbiotic, but often tense, relationship. One source of tension is retailer chargebacks (or fines) for failing to comply with their compliance requirements. The situation is made even more difficult and complex because every major retailer publishes its own set of requirements. Srivastava observes, “There are many compliances that the retailers have to follow — some products need to be kept at a required temperature range, hygiene compliances, COVID security protocols, and more. AI can help retailers make sure these policies are not being breached at ground level. Apart from that, for retail execution purposes, AI can help in shelf monitoring to ensure KPIs like planogram compliance, eye-level placement of high salience products, right promos and price displays etc., are all met.”

 

CPG Companies

• Perfect Retail Execution: CPG companies require many of the same analytic results as retailers. Srivastava notes, “CPG creates guidelines to measure key performance indicators like planogram compliance, price display compliance, on-shelf availability measurement. Through image recognition solutions, field reps capture images of retail store shelves. These are fed to the AI which calculates the KPIs in real-time and instant action is taken to address the gaps.”

 

Supply Chain Players
• Improving Supply Operations. Mani notes, “All aspects of the retail supply chain, including inventory, staffing, distribution and delivery, can be managed in real time by implementing artificial intelligence.” Last mile delivery is no exception. Srivastava adds, “AI in combination with GIS systems can parse address lines and match packages to a point on the map. It can then allocate the package according to the service area of the delivery person.”

 

• Automatic Warehouses. The rise in ecommerce has made order fulfillment a major focus of supply chain operations. To speed up delivery times, warehouses have become a hot property and robots are being leveraged to do product picking. Srivastava notes, “Image Recognition and Robotics can automate and speed up the working of warehouses. They can perform automatic sifting according to requirements of individual stores and creating delivery parcels ready to be picked up.”

 

Customers
• Better Products. Better products often mean more personalized products. Busby explains, “This is the new world of the cognitive consumer. … According to research by Deloitte, nearly half of all consumers are ‘willing to wait longer for a personalized product or service.’ The customer journey has never been so difficult to map. But as we’ve learned, consumers are leaving behind digital footprints. Savvy retailers must follow them.” Srivastava adds, “AI can be used to analyze product usage to create more ergonomic and useful versions of products.”

 

• Better Prices. According to Srivastava, “AI can process product purchase data and optimize production/retail inventories to make sure customers get the best prices. The pricing, design processes are still chiefly intuition-based and there are experts that design product/marketing campaigns. The more purchase/customer usage/customer behavior data is available and processed by AI, the more effective these campaigns are going to become.” Solutions, like the Enterra Trade Promotion Optimization System™ can help both CPG manufacturers and retailers find the right pricing point for successful campaigns.

 

Concluding Thoughts

 

Andrew Blatherwick, Chairman at RELEX Solutions, asserts, “In today’s customer-centric supply chain, retail is detail. Store-level planogramming backed up by store-level inventory and supply chain planning is the new level of granularity required for retail success and long-term customer loyalty.”[7] Only cognitive analytics can help achieve those goals. Mani explains, “AI can transform every aspect of retail businesses. It replaces intuition with intelligence and gives retailers a vision for the future. Business leaders need to be pragmatic in their approach while implementing AI. They need to understand that it is a capital-intensive technology and that it shows results in the long run. A company needs to set a long-term AI objective to ensure success.” In addition to improving operational processes, cognitive computing solutions, like the Enterra Supply Chain Optimization System™, can provide retail and supply chain decision makers with other valuable, actionable insights. Cognitive technologies are no longer a “nice to have” they are “need to have” for retailers.

 

Footnotes
[1] Vaibhavi Tamizhkumaran, “Is Cognitive Analytics Reinventing A New Landscape For Retail Sector?” Indium Blog, 7 April 2021.
[2] Andrew Busby, “How Cognitive Computing Is Reshaping Retail,” Longitudes, 10 September 2017.
[3] Muktabh Mayank Srivastava, “How Artificial Intelligence is the Future of Retail?” IndianRetailer.com, 31 March 2021.
[4] Melanie Nuce, “When Artificial Intelligence (AI) meets 3 retail industry pain points,” The Enterprisers Project, 10 November 2020.
[5] Macy Bayern, “How AI can save the retail industry,” TechRepublic, 13 September 2019.
[6] Chithrai Mani, “Seven Ways Artificial Intelligence Is Disrupting The Retail Industry,” Forbes, 21 August 2020.
[7] Andrew Blatherwick, “Technology’s Role in Managing the Evolution of the Customer Centric Supply Chain,” Supply Chain Digital, 20 January 2017.