Artificial Intelligence: How Algorithms will Optimise Retail

Optimising AI for Retail
The world is getting faster, finer and superior with artificial intelligence and the retail industry is not left behind. As we move forward in the future, brands are witnessing a confluence of various technologies, including machine learning and artificial intelligence which are assisting them in providing an enhanced customer experience and increased efficiency. Whether it be by forecasting demand or understanding the current sale trends, artificial intelligence is one of the growing trends in today’s e-commerce.

The pandemic has forced many brands to invest in digital tools, although the gains are not immediate as the world of e-commerce is filled with contingencies. Artificial intelligence is used by many brands to forecast supply, demand, sales, provide personalised customer experience using historical data and predictive analysis. Without further ado, let us have a look at how artificial intelligence is optimising retail.

Personalisation: Customers expect brands to cover their needs irrespective of the channel that they use and in order to satisfy customers expectations, brands are enabling personalised recommendations. According to McKinsey, “ Personalization can deliver five to eight times the ROI on marketing spend, and can lift sales by 10% or more”. Artificial intelligence can simplify the personalisation process by learning customer preferences based on their previous actions. It then makes the customer’s buying experience more smooth by making proper suggestions

In 2019, a survey was conducted on “Business Impact of personalization in Retail Study” on 3144 customers, the customers were asked three questions,
  1. Do you ultimately buy something different from what you had originally planned?
  2. Did you ultimately spend different amounts of money you had originally planned?
  3. Would you recommend the store to others?
The response of customers are below:
Business Impact of personalization in Retail Study

Automation in-store: As brands get larger in size, tasks will become complex in nature, the level of complexity and repetition limits human intervention. To tackle these scenarios brands are using automation, as AI is well versed with the required technology to handle unexpected changes or complexities. According to a report on, “Smart Stores – Rebooting the retail store through in-store automation,” which surveyed more than 500 retail executives and 5,000 consumers and 500 retail executives across Europe, North America and Asia, found that around 59% of consumers who have previously visited stores with automation said they would be willing to shift their in-store purchases from a retailer without automation technologies to one that offers them, rising to 67% for 22-36 year-olds.

In the same survey, most shoppers believe automation can help to solve pain points they experience in-store including long checkout queues (66%), difficulty in locating products (60%), and products being out of stock (56%).

Augmented Reality and Virtual Reality : Augmented reality is enhancing the customer experience like never before, stores are enabling augmented reality and virtual reality to solve the problem of standing in queues for the changing rooms. AR and VR lets a customer have a virtual try-on with many different variants available. This technological advancement increases the sales massively, and it also decreases the return rate of unwanted products. It is thereby ensuring an efficient, personalised and excellent customer experience.

Smart Mirror: A smart mirror is a wonderful new technology that many stores are using, which is present in fitting rooms. Customers can log in their personal information in the smart mirror using the QR code which is provided in the app or in email. By using a smart mirror, shoppers can request products of different colour, size or even a completely different product to be brought to their fitting room. Shoppers can even share purchases with their friends , they just need to take a snap and can easily share it with a single click. 

Catalogue Creation: Artificial intelligence is being used to create catalogues, catalogue creation is essential but around 98% of the brands find it difficult to create quality catalogues. The complexity in creating unique catalogues for each marketplace is high. By using Artificial Intelligence, the process of catalogue creation becomes a breeze.

Data Collection and analysis: From a retail perspective, with an increase in the number of sales, the retail operation becomes complex. Additionally, managing and analysing data becomes difficult for brands of all sizes and across all industries. Artificial Intelligence can help the brands in analysing data and finding out more about competitors, marketplaces, and products.

Chatbots: In earlier days, shoppers had to wait for their turn to interact with store keepers, if they have any query regarding products. Now, brands use natural language processing to understand the query of shoppers, it acts as their shopping advisor and more, chatbots can even assist shoppers in making smart decisions about their shopping, for instance, the chatbot can answer customer queries related to new collections, new brands that are available in the store and much more. As we move into the future, chatbots are getting more intuitive and are enabling a better customer experience. Apart from providing customer support, chatbots are enhancing the impact of AI in E-commerce through voice-based interactions with shoppers and addressing their needs more efficiently. Some other uses of chatbots in retail are:

  • Image Powered Search: By using AI and Machine learning algorithms shoppers can search for products using images, this process eliminates the need for keyword searches. Shoppers can upload product images of their choice, and then chatbot will find the relevant products using AI. This process simplifies the shopping experience for shoppers and will help them find products with speed and efficiency.
  • Voice Powered Search: In addition to searching products through images, shoppers can now easily search products through their voice, as voice recognition accuracy has improved than before. Brands are enabling voice search features for their customers, as they understand that people expect quick results for their searches and voice searches are the way to go.

Inventory Management: Inventory management is one of the essential areas of any business. By using Machine learning and AI-based techniques, brands are getting insights from previous years sales trends, which are helping them in anticipating changes in product demands and solve the problems of under or overstocking of inventory.

Order Fulfillment: In today’s competitive world, businesses have to deal with a high volume of sales orders every day and brands need to hire or train staff every time they add a new channel of sales. Many organisations are leveraging Artificial Intelligence and Automation to automate the process of order fulfilment, listing, inventory update, order fulfilment, cataloguing, etc.

Predicting peak hours using AI in Logistics Centers : By utilizing AI and ML technology, brands will be able to monitor and predict traffic and other factors which can help them in proper planning of their shipping time.

Demand Forecasting: Accurate forecasting of demand is one of the crucial things that a business should focus on, gone were the days when businesses used to forecast demand using conventional methods. Times have changed now, by using historical data and predictive analysis in AI, supply chain operators can better estimate all the factors which can work towards precision in demand forecasting. So that the organisation can focus on producing more products in demand and thereby leading up to a reduction in lost sales due to inventory stock-out situations.

Supply Prediction: Brands are leveraging Machine learning and AI-based techniques that use complex algorithms, structured and unstructured data which can be used to interpret the arrival time of supplies, which will allow the brands in taking preventative measures to decrease the impact of shortages, reduce the cost, provide proper warehouse and inventory based management.

Sales Prediction: Studies suggest that knowing customer needs before they want leads to business success, having a well thought out sales framework is vital as it will enhance productivity and accuracy. Machine learning and AI-based techniques use complex algorithms that can make sales prediction through AI-based forecasting which will help the organization in efficient planning.


To succeed in this fast paced ecommerce world, brands need to constantly update their technological approach to meet the never ending demands of their shoppers. As we move into the future, having the right machine learning and AI technologies at their service, brands will be prepared for the Global market like never before.