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AI and retail transformation

By Retail Technology | Wednesday March 13 2024 | UPDATED 13.03.24

Five ways AI and emerging technologies are set to transform retail this year by Kamaraj Chinnasamy, TCS retail domain head UK & Europe

It may seem like technological innovation is at fever pitch – but the truth is, the impact of AI and other emerging technologies has barely begun to be felt, not least in the retail industry. New technologies, including AI, with its myriad applications, promise to revolutionise retail, expanding its capabilities and opportunities far beyond current implementations, and propelling leaders in the space into a new era.


Here are five top ways that AI and digital technologies are already shaping the future of retail:


1. Digital Twins


Digital Twins refer to the virtual replicas of physical entities, processes, or systems. In the context of retail, this means creating a digital representation of a store, warehouse, or even the entire supply chain. AI plays a pivotal role in making these digital twins smarter and more responsive.


For instance, a digital twin of a retail store can simulate customer footfall, predict which aisles receive the most traffic, and even determine the optimal placement of products. By analysing real-time data, AI can adjust the digital twin to reflect changes in customer behaviour, seasonal trends, or inventory levels. This allows retailers to test what-if scenarios in a virtual environment before implementing them in the real world.


By simulating the entire supply chain processes, from manufacturer to end consumer, they can help in identifying bottlenecks, optimising routes, and ensuring timely delivery of products. Additionally, they can be used to train new employees, simulate emergency scenarios, or optimise worker routes within the warehouse, leading to improved efficiency and safety. Moreover, integrating AI with digital twins can lead to predictive maintenance for equipment and energy optimisation for store operations.

2. Returns Prevention


Returns are a significant pain point for retailers, often leading to increased operational costs and reduced profit margins. AI can transform this area by predicting and preventing unnecessary returns. By analysing customer data, purchase histories, and feedback, AI can provide insights into why certain products are returned more frequently.


AI can assist in improving product descriptions, sizing recommendations, and virtual try-ons, ensuring that customers have a clearer understanding of what they're purchasing.


Additionally, in sectors like food and cosmetics, returns can often be linked to allergens. AI is poised to play a pivotal role in allergy detection and prevention. By analysing product ingredients and cross-referencing them with customer profiles, AI can alert customers about potential allergens before purchase. This proactive approach not only reduces returns but also enhances customer trust and safety.


Chatbots and virtual assistants can also use AI to answer customer queries in real-time, reducing the likelihood of post-purchase dissatisfaction.


By reducing returns, retailers not only save on operational costs but also contribute to sustainability by minimising waste and reducing the carbon footprint associated with reverse logistics.


3. Intelligent Promotions


Traditional promotional strategies often rely on broad demographics and past sales data. AI, however, can personalise promotions to an individual level. By analysing a customer's purchase history, online and in-store behaviour, and data related to their aspirations, lifestyle, life stage, recency and frequency, AI can curate real-time promotions that are tailored to each customer.


Moreover, it can help to model a shopper’s purchase journey and provide offers that would inspire conversions at different stages. This means that instead of blanket discounts or generic promotional offers, customers receive deals on products they are genuinely interested in. Such intelligent promotions increase the likelihood of sales, enhance customer loyalty, and ensure that promotional budgets are spent more efficiently.


4. Demand Detection and Logistics


Predicting demand has always been a challenge in retail majorly due to dynamic market conditions and supply chain disruptions. Sometimes this leads to over or understocking which furthermore has a negative impact on the cost and the customer experience.


AI, with its ability to analyse vast amounts of data, can forecast demand with unprecedented accuracy. By examining past sales data, current market trends, social media sentiment, and even factors like weather patterns, AI can provide retailers with detailed insights into what products will be in demand.


This predictive capability extends to logistics as well. AI can optimise inventory levels, ensuring that stores are neither overstocked nor understocked. It can also streamline the supply chain, determining the most efficient routes for product delivery, and predicting potential disruptions.


In essence, AI will enable retailers to have the right products, in the right quantities, at the right places, at the right times, leading to increased sales and reduced costs and wastage.


5. 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.


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