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The rise of artificial intelligence is disrupting businesses in every industry. But according to Kamaraj Chinnasamy from Tata Consultancy Services, AI has still barely scratched the surface of the retail sector. He outlines 10 ways how AI can be used to enhance customer experience, supply chains, customer service, and more.
1) Personalisation
Today’s consumers demand more than just products; they seek experiences tailored to their unique preferences. AI, by analysing intricate patterns in vast datasets like purchase histories, browsing habits, and even social media activity, can offer personalised recommendations and advertisem*nts.
This level of granularity allows retailers to send tailored emails, suggest relevant products, and customise user interfaces.
In the future, we can envisage AI predicting customers’ needs even before they realise them. For instance, based on past purchase data and online activity, a retailer might suggest winter apparel for a customer who has a high probability to travel to a colder region soon. Moreover, with an understanding customer’s food choices of health-related aspirations, AI can personalise the basket to offer healthier substitutes of the products.
AI may also facilitate personalisation of virtual stores for customers based on the contextual understanding of the consumer’s inclinations and aspirations.
2) Autonomous Stores: AI-driven Retail Automation
The concept of an autonomous stores, while seemingly futuristic, is already taking shape. With a complex web of sensors, cameras, and machine learning algorithms, products are automatically detected and billed as customers walk out, optimising customer experience and operational efficiency.
Beyond just convenience, this can also revolutionise in-store customer experience. Stores equipped with AI cameras could gauge a shopper’s emotional reactions to products and displays and could offer real-time deals that might appeal to their current mood. As this technology evolves, stores might dynamically change layouts and automatically monitor and replenish shelfs, with movement of products within the store with the help of robots.
3) Blockchain in Retail Supply Chain
Blockchain, when complemented with AI, provides an unparalleled level of transparency and efficiency. From a farm's soil quality data to a shirt's manufacturing date, every step can be recorded on an immutable ledger.
For retailers, this means reduced instances of fraud and counterfeit goods. For consumers, it guarantees product authenticity. Imagine scanning a QR code on a product with a smartphone and tracing back its entire journey. Moreover, AI can predict supply chain disruptions or demand spikes, and blockchain can ensure that every entity in the chain fulfils its obligations, ensuring smooth operations.
Additionally, smart contracts on the blockchain could automatically release payments to the suppliers once AI-powered sensors at the retailer’s warehouse detect that the goods have been delivered and meet the required standards.
4) Augmented Reality Shopping
Augmented Reality is set to redefine online shopping. With AR glasses or even smartphone apps, consumers can virtually wear a dress or place a piece of furniture in their living room. AI enhances this by suggesting products based on personal preferences, past purchases, or trending styles and context.
Moreover, it can allow shoppers to customise products – example customising a sofa based on the wall colour and the space available in their living rooms. This not only enriches the online shopping experience but also reduces return rates, a challenge many retailers face.
Imagine a scenario where shoppers, using AR and AI, get a virtual shopping assistant that offers real-time product recommendations, answers queries, and provides in-store assistance. Imagine asking a chatbot about the best hiking shoes, and it responds by asking about your past hiking terrains, budget, and even scans your foot size using AR to provide you with the best suited options.
5) Chatbots
Modern chatbots, powered by AI, are a quantum leap from their rule-based predecessors. They can understand context, handle multifaceted queries, and learn from interactions. For retailers, this means reduced operational costs and 24/7 customer service. Chatbots also facilitate understanding of the customer’s context and capturing relevant information to help the retail colleagues to convert what might have been a missed opportunity.
In the future, these chatbots might evolve into virtual shopping assistants, guiding customers through entire shopping journeys, and offering personalised advice, mimicking in-store colleague-based assistance. Moreover, advanced AI chatbots could detect shopper emotions based on the text inputs, adjusting their responses to align with user’s sentiments, ensuring more empathetic customer service.
6) AI-driven Demand and Inventory Management
Out-of-stock or overstock scenarios can be costly for retailers. AI, with predictive analytics, can forecast demand with high accuracy.
By analysing historical sales data, local events, weather forecasts, and even global news, AI can provide actionable insights. This means efficient warehousing, optimised supply chains, reduced wastage due to perishables expiring, and maximised profitability. With the integration of AI and IoT (Internet of Things), real-time inventory tracking becomes possible, leading to dynamic pricing strategies and promotion adjustments.
AI-powered robots and drones can manage warehouse operations from restocking shelves to picking and packing orders. AI systems can dynamically adjust supply chain operations based on real-time events. For instance, if a sudden weather event disrupts a supply route, the AI can instantly reroute the shipments or adjust inventory distribution across locations.
Moreover, AI can assist business users to design supply chain based on different strategies such as cost efficiency or minimum lead times etc.
7) Integration of IoT and Tech
The Internet of Things, comprising smart shelves, interactive displays, and other connected devices, provides a treasure of real-time data. AI algorithms can analyse this data to offer insights on customer preferences, inventory levels, and even in-store navigation patterns.
From personalised recommendations inside smart fitting rooms to automated checkout and predictive maintenance of instore equipment, convergence of AI with IoT has already enabled higher operating efficiency and enhanced customer experience.
Moreover, in a connected store, if a customer is spending significant time in an aisle, a nearby screen might instantly display promotions or comparisons, aiding the purchase decision. Or based on smart shelf weight sensors, the store could automatically reorder stock or adjust pricing on the ESLs (electronic shelf labels) to move the inventory.
Furthermore, AI can enable insights such as quality of perishable products in the supply chain based on the data captured from the logistics provider such as temperature, humidity, route etc.
8) Facial Recognition: Retail Security and Privacy
AI-backed facial recognition is rapidly being adopted in the retail world. It offers a myriad of applications such as enhanced security by tracking potential shoplifters and controlling access to restricted areas within stores or warehouses, facial-based payments, and age verification for restricted products etc.
The interactive displays in the stores, for instance, could provide personalised content by identifying the customers and their choices. Similarly, the billboards or digital signages outside the stores could display tailored advertisem*nts based on the demographic profile of the viewer, thereby enhancing promotion effectiveness.
Facial recognition can also facilitate enhanced customer support, recognising returning shoppers, store associates could provide more personalised assistance and offers to them.
9) Smart Carbon Tracking
Sustainability is becoming a priority for consumers and businesses alike. As eco-consciousness rises, retailers are under pressure to showcase their sustainability efforts. By integrating AI, retailers can go beyond just tracking to predictive and proactive carbon management.
AI can assist retailers in tracking their carbon footprint across the supply chain. From sourcing raw materials to product delivery, AI can analyse and provide insights into carbon-intensive areas and suggest strategies for reduction.
Apart from the standard parameters, AI can analyse data such as road and weather conditions and traffic situations to calculate the carbon footprint of a product or a service with even higher accuracy. Additionally, generative AI can significantly reduce the effort required to consolidate data from multiple siloed systems for reporting purposes.
Moreover, AI can analyse historical data to predict future carbon emissions for the planned activities and help retailers in pre-emptively implementing reduction strategies. Furthermore, AI can be used to optimise energy consumption in stores, warehouses, and during transportation. By predicting demand, AI can also reduce waste, further contributing to sustainability.
10) AI in Fashion: Data-driven Insights
Fashion is inherently dynamic, with trends changing rapidly. AI can help fashion retailers stay ahead of the curve. By analysing vast amounts of data from social media, online searches, and sales patterns, AI can predict upcoming fashion trends. This not only influences design and production but also reduces wastage as retailers produce what is likely to be in demand. This means designers and retailers can be proactive, reducing the time to market.
AI can offer suggestions on fit, sizes, or even recommend complementary products. Moreover, smart mirrors could utilise this capability to enhance shopper’s experience within the retail stores. Additionally, by integrating data from wearables to an AI system, the retailers can suggest fashion items to match a customer’s lifestyle and activity levels.
Furthermore, by integrating AI-based smart try-ons in the mobile app, retailers can enable the customers to virtually try on fashion and beauty products and allow retailers to offer personalised recommendations based on the facial feature, skin tones, and so on.
