Associate Dean Accreditations & AOL II Mohanbir Sawhney Chair Professor, Woxsen University
As customers, we have been exposed to personalised marketing messages and product recommendation engines for many years now. But with the rapid developments in the machine learning and artificial intelligence (AI) space, we are on to the next level of personalized shopping.
The e-commerce companies have an unprecedented amount of data and technology know-how today to conduct deep-level insights to cluster and segment consumers into nano groups. This helps them to push product and price recommendations right at the point of customer log in till the point of customer purchase or leaving the portal. Additionally, it remembers the items that were searched during the previous session and continues to provide recommendations for products and items related to those items.
This is where agentic AI bots enter the picture. It is the most advanced form of generative AI and machine learning that can work autonomously to achieve specific goals. Imagine having a virtual chatbot assist you daily. In a few minutes, the system will attempt to understand you and will continue to push recommendations and provide immediate personalization.
Using natural language processing (NLP) and machine learning (ML), conversational AI solutions provide relevant answers based on the meaning behind users’ questions. The use of these apps is crucial for businesses that offer customer service at scale through chatbots like Zendesk and Intercom, as well as apps like WhatsApp. New to the mix, generative AI tools are used to create new content from simple text prompts provided by marketers. Generative campaigns are used by most teams today to expedite the creation of campaigns and to increase efficiency.
According to Bloomreach, agentic AI today can provide product recommendations, styling advice, and handhold checkouts. According to Centric Software, companies like H&M, Zara, and Swarovski have integrated AI into their online shopping to the extent that the customers would not even feel that there is a personalization engine at work. It recreates the joy of “stumbling” across a product that feels just right. Victoria’s Secret has launched a beta version of an application AM by You, wherein customers can design their wear and then buy these personalised items.
Not to be left behind, Google Shopping in the USA notes individual preferences, and locations. Weather and other factors to recommend specific products with rationale for such. According to Capterra, two-thirds of customers plan to complete the checkout in 4 minutes or less. In such cases, AI is helping by auto-filling the details and pushing in relevant payment information along with promotional discounts. All of this is to provide an enhanced customer experience.
What is the rationale for companies to do this? It aims to increase customer loyalty, to increase the efficiency of the sales process, and to improve inventory management. It has been estimated that personalisation can increase revenue by 15% according to McKinsey. Salesforce reports that 65% of customers remain loyal to companies that offer personalization.
However, there are some concerns. There is a primary concern regarding the privacy of personal information. To achieve this, companies must be transparent about how they use customer data. Additionally, overt personalization may result in the customer feeling quite insecure about the sharing of information. The misuse of customer data is being curtailed in many countries through the implementation of privacy laws.
The future of hyper-personalization. A highly immersive experience can be achieved by combining AR/VR with advanced AI engines. Another development could be to enhance omnichannel experiences by integrating AI into offline and online experiences. We are only at the beginning of the journey.
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