Hyper personalized Shopping Experience with AI

Artificial Intelligence although in its early stages when it comes to its potential, has progressed immensely over the last month and is evolving at an exponential pace. Adoption of AI technologies have been moving at a fast pace in the ecommerce industry with all major players actively pursuing in providing hyper personalized shopping experiences with Artificial Intelligence.

Hyper personalisation is nothing but the strategy of analysing data and through machine learning algorithms, provide a completely customised service to individual customers. In eCommerce, the personalization can be anything from providing customized product recommendations based on purchase and browsing data to sending out personalized birthday wishes, coupons etc. based on customers profile information.

Hyper personalization has large number of benefits when it comes to hyper-personalized shopping experiences. Through hyper personalization, a customer will discover new products that has arrived in the shop which would have been missed otherwise. By doing these personalized interventions or nudges, the entire journey of the customer’s shopping experience is made super-efficient and builds a personal connect.

On the other hand, hyper personalization requires large amount of customer data to be stored and processed in a secure way. This would raise a privacy concern from the customers on the kind of data collected, whether it is stored in a secure manner and processed as much as possible without human interventions.

The benefits outweigh the potential risks associated with hyper personalization in the eCommerce industry and it is up-to each of the eCommerce companies to carefully evaluate and decide whether to pursue the usage of hyper personalization. Here are some of the ways in which Hyper personalized shopping experience can be provided to customers via AI:

Product Recommendations

Using various parameters that are collected from customer like profile details, order history, browsing history and pattern, AI can be used to recommend products that they are likely to be interested to purchase. These personalized recommendations can be either a section on the website / app or pushed to customer as a nudge via push notification in app or popup recommendations on the website.

For e.g. an online retailer can recommend a new dress to a customer who has recently purchased similar item.

Understand Customer Preference

AI can be used to analyse a large amount of data about customer which includes their purchase history, location, browsing behaviour and even sentiment analysis from the reviews they have posted. With this data, a complete personalized shopping experience can be provided to the customer.

Targeted promotions

With the amount of customer data available for analysis, ecommerce systems can provide personalized and targeted promotions that will be relatable to the customers. These targeted promotions have more chances of getting customer interactions than a generic promotional message.

For e.g. an online retailer can offer a discount on a product that a customer has viewed, but have not purchased yet.

Virtual Assistants

Virtual assistants are one of the most popular use cases for an AI model. AI powered virtual assistants can be used to provide customers with a personalized assistance throughout their shopping journey.

For e.g. an online retailer can setup a virtual assistant who can be available 24×7 to answer any question about products, payments, shipping etc. Another use case can be the use of virtual assistant to automatically engage with the customers about notifying them about availability of a product which was previously out of stock.


Following are the benefits of hyper personalized shopping experiences via AI:

  • Increased customer satisfaction and loyalty
  • Improved conversion rates
  • Increased average order value
  • Reduced marketing costs


Following are the challenges involved in hyper personalized shopping experiences via AI:

  • Data privacy concerns
  • Data security risk
  • Need for a large amount of data for training and validation of AI Model
  • Trust in the AI Model

Overall, hyper-personalized shopping experiences with AI have the potential to be a powerful tool for Ecommerce vendors. However, it is important to carefully consider the challenges and take appropriate mitigation measures when implementing this.

Author Details

Roy Maria John

Roy M J is a Technology Architect with Infosys. He is a Digital Transformation Specialist associated with Digital Experience IP Platforms & Frameworks in Infosys. He helps in delivering digital transformation for large enterprises across the globe via Live Enterprise Interactions Suite and Digital Marketplace Platforms. He has rich experience in Web technologies primarily on the JavaScript stack. He is part of Cloud and Emerging Technologies track in the Digital Technology Council.

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