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How is Artificial Intelligence changing retail?

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Herald Perez
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The retail sector has been going through a digital shift for a while now. Every field of retail companies has seen a rise in speed, efficiency, and accuracy, in large part because of sophisticated data and predictive analytics technologies that support businesses in making data-driven business choices. Without the internet of things (IoT) and, most significantly, artificial intelligence, none of those insights would be feasible. Businesses now have access to high-level data and information that can be used to enhance retail operations and create new business prospects thanks to AI in Mobile Retail Execution. Retailers who want to maintain their competitiveness need look no further than AI. By 2020, 85 percent of businesses are expected to start utilising AI, and those that don't, run the danger of losing unstoppable market share to rivals.


Need of AI in retail industry

Artificial intelligence has a huge number of advantages for the retail industry. The digital revolution in retail is only separating successful firms from failing ones, apart from the business insight and sheer speed that these technologies may give. Below are some of the points which prove the need of AI in retail industry

  • Traditional retailers must connect customers in a personalised and relevant way that is distinctive and inspirational across all touchpoints given the abundance of creative rivals offering consumers immersive shopping experiences.
  • Retailers must provide customers engaging service and experiences while differentiating their items to maintain consumer attention. Retailers may take the initiative in innovation rather than only responding to change by incorporating predictive analytics to gain additional market knowledge.
  • Retailers must cut through the clutter to turn the different data sources from all facets of their business—from the supply chain to the shops and consumers—into a consumer-first strategy.
  • However, treating these channels as separate business units increases friction for customers wanting a seamless shopping experience and results in operational inefficiencies. Digital and physical retail channels often operate under different sets of strategies and methodologies.
  • Retailers must reconsider their old supply chain in favour of adaptable and agile ecosystems that can swiftly react to consumers' evolving behaviours in order to meet a larger range of customer needs that are transitioning from mainstream to specialised.


Top uses of AI in retail industry

  • Stores can become checkout-free

Reduced wait times, a reduction in the number of human employees, and considerable operational cost reductions are all benefits of retail robotization. Checkout-free stores have already been implemented by Amazon AI. When you take an item off the shelf or put it back, the “Just Walk Out” and “Amazon Go” system responds. The Amazon account will debit your credit card when you leave the store with your purchases. Amazon intends to create additional AI-powered stores like Amazon Go, where just six to twenty human employees are required and the stores are free from checkouts


  • Customers serviced with chatbots

AI chatbots offer even better customer service, enhance searches, notify users of new collections, and recommend related goods. A chatbot may propose a snapback to complement a customer's particular apparel, completing the appearance if they already own one. Eighty percent of companies globally either employ AI chatbots now or will do so soon. Some of the renowned brands have used chatbots to assist their clients in navigating their collections.


  • In-store assistance

Retailers also make investments in technology that benefit both store employees and customers throughout the buying experience. Paper price tags are no longer needed at some of the shops because of Edge technology; instead, smart shelf tags are now in use. On the screens, this technology also offers video advertisements, nutritional information, and promotions. Customers may use Lowebot, an autonomous in-store robot from Lowe's, to help them in various languages discover what they need in the shop. In addition, the real-time monitoring features assist with inventory management.


  • Prediction of customer behaviour

Utilising behavioural economics and developing a customised strategy for each client is made possible by artificial intelligence systems for company owners. To enhance sales, there is a platform called Intelligent Incentive that examines each customer's psychology and emotions. The algorithm analyses a client's emotional responses and behaviour from prior purchasing experiences in an effort to generate the best pricing offers for that specific consumer.


  • Analysing customer satisfaction

Artificial intelligence is able to determine your consumers' emotional state while they purchase. For this purpose, Walmart has already launched facial recognition technology. Each checkout line has cameras mounted, and if a client becomes irate, a store employee will approach him or her. Mood monitoring will undoubtedly contribute to improving customer connections.


  • Virtual Fitting Rooms

Another fantastic application is called Virtual Fitting Rooms, and it allows clients to select the ideal outfit in a matter of minutes with all the components precisely matched. A productive virtual fitting kiosk can measure 200,000 points on your body in 20 seconds by scanning you. These scanners were put in the stores of many large brands, and the increased sales were enormous.


  • Visual Search powered by AI

Customers may submit photographs and use Visual Search systems that use artificial intelligence to locate goods that are comparable in terms of colours, forms, and patterns. Cortexica's image recognition technology guarantees accuracy of over 95%. Ninety percent of customers rated The “Find Similar” function favourably. The visual search feature of American Eagle's IR technology proposes complementary items in addition to helping users find matching or similar clothing.


  • Predicting prices

Price forecasting is the process of estimating a product's price based on factors such as demand, seasonal trends, its properties, when new versions of the same thing will be released, etc. Although the travel sector is where it is most obviously employed, it might also be used in the retail sector. Just envision a tool or service that notifies your consumers in advance when a given product's pricing will change. This is feasible and simple to achieve with artificial intelligence. You could increase client loyalty by using a price prediction function. But in the retail sector, predictive analytics and machine learning might accomplish far more than merely predicting prices.

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