Factory 4.0 concept: Collaboration of industrial robots in smart warehouse. (Representational image) Image: Shutterstock
While technology adoption was trickling in at a snail-like pace over the years, the post-COVID 19 retail world has witnessed a massive AI disruption as more and more businesses (both offline and online) are ramping up AI automation to tap consumers’ changing shopping patterns. Customers are increasingly looking for personalized, hassle-free shopping experiences; global retailers are hence on the edge of perpetual AI integration to align their products/services with customer preferences or expectations. Having surpassed 2 billion US Dollars in 2020, the AI in Retail Market size is anticipated to rise at “more than 30% CAGR between 2021 and 2027,” reports Global Market Insights.
Retailers can use AI tools and solutions to collect and analyze data on sales, demographics, consumer behavior, etc., to automate product prices, manage pricing strategies, and offer personalized recommendations. Recent research suggests the undeniable importance of AI in increasing online, in-store, mobile, and omnichannel sales, managing and enhancing in-store and digital experiences, improving customer service, payments, logistics services, and optimizing supply-chain and operational efficiency. Though AI paves the way for a promising future, many traditional and online retailers have failed to keep pace with the ever-upgrading emerging tech adoption. In this article, the authors suggest a few operational areas that call for early AI adoption, along with example use-cases, to help the incumbents and new entrants thrive in the perpetually competitive retail context.
1. AI in improving customer engagement
In this digital age, customer experience management to provide personalized recommendations and tailor-made products/services across all retail channels is key to all businesses. Continuous customer interaction is an inherent part of retailing, and quite naturally, it allows firms to capture large amounts of customer data, which includes customers’ purchase transactions, post-consumption social media information, and other C2C (customer to customer) data in various digital media platforms. In addition, firms can also gather buyer data from external sources and social media. This rich repository of buyer preferences/knowledge can be analyzed using AI tools to offer personalized recommendations and perk up their shopping experiences.
For example, Japanese clothing brand Uniqlo launched its UMood campaign in Australia that used neuroscience to offer customized clothing recommendations to its customers. First, the company used technology-enabled headsets to capture the buyer’s neurological responses. Then, AI is used to analyze these responses and match a buyer’s mood to perfect clothing.
As buyers walked through Kroger’s grocery outlets, the store’s cloud-based EDGE technology displayed prices, nutritional information, price promotions, etc., from the stores’ IoT-enabled displays; customers could compare products of different brands before making a purchase decision.
2. AI in improving store operations, supply chain, and pricing strategies
Intelligent automation of store processes, both brick-and-mortar and online, can extravagantly improve customers’ shopping experience. Each store has its customers with unique preferences based on location, demographics, gender, income, etc. AI can use the unique data particular to a store to automate products and services assortment to tailor-meet a demand.
Stores install AI-enabled solutions to offer customers a virtual tour. For example, Zara remodeled its flagship store in Stratford, London, with smart mirrors, allowing customers to browse through its massive online product line with swipe and displayed hologram images, significantly reducing the time of inventory checks from days to a matter of a few hours. Also, the store’s robot-powered pick-up points and automated check-out booths managed more than 2,000 orders concurrently.
In an online context, vee|24 built its Nuge-BOTs that allow retailers to interact with their online customers – from initiating a conversation to generating sales. Such assistance bots helped customer service executives provide augmented support by analyzing customer’s tone and mood and suggesting response scripts based on the analysis.
The supply chain involves several processes – from material procurement to logistics to distribution. Proper coordination among all functions is essential for the smooth functioning of the entire supply chain. AI algorithms can collect and analyze the data and eventually generate calculations to help retailers make decisions in real-time. AI algorithms can also be used to automate repeat processes, track and evaluate data to forecast real-time consumer demand based on sales at a particular time, past purchases, weather, mood, socio-economic changes, etc. Using predictive analytics, retailers forecast and model recent and upcoming trends across various products and locations. Also, AI automation of transportation modes, robots and drones for delivery, route optimization using GPS, etc., can ease logistics and transportation processes. Algorithms can also be used to automate warehouse and inventory management.
For example, clothing brand H&M used AI tools to forecast supply and demand and plan their promotions and stock management based on the predictions. Retailers also started using AI-driven automation to develop real-time pricing strategies and decide on promotional coupons. For example, FaceX’s smart tool enabled retailers to identify customers’ emotional temper to determine customized pricing.
3. AI in improving inventory and operational management
Retailers use AI to optimize demand predictions and plan supply chain operations to manage inventory. For example, Amazon’s patented “anticipatory shipping” system was designed to predict a future demand and ship a product to the nearest hub of the customer location even before an order was placed. This could make overstocking or understocking issues a passe and significantly cut down on delivery time and cost.
Also, AI/ML automation can be used to detect any bottleneck in the supply chain, e.g., a sudden road blockage or challenges due to weather changes. Algorithms can automate processes such as rerouting shipments or adjusting inventory, etc. Retailers using such solutions can manage sudden changes in planned events cost-effectively and meet customer demand with agility.
In a nutshell, with AI automation, the future of retailing could be all plain sailing, with reduced operational costs and increased revenue growth. Thus, the early adoption of AI has undeniable importance for retailers to leapfrog the competition and thrive in the market.”
S. Arunachalam is an Assistant Professor in Marketing, and Academic Director at ISB’s Centre for Innovation and Entrepreneurship and Lopamudra Roy is a Research Associate-Content Development at ISB’s Centre for Innovation and Entrepreneurship.