In the digital age, BtoC players are faced with a major challenge: delivering a consistent, high-quality customer experience in an ever-changing environment. Recent advances in artificial intelligence offer exciting new opportunities. In particular, the advent of Generative AI (GAI) solutions marks a turning point comparable to the digital revolution we experienced at the beginning of the 21st century.
Increased personalisation, immersive shopping experiences, optimised sales processes thanks to the use of data to anticipate customer needs - generative AI has the potential to radically transform a company's availability and customer relations thanks to personalisation and improved availability and responsiveness. In fact, the paradigm shift is such that we are no longer talking about customer relations but about customer interaction.
Enhancing the customer experience with virtual reality
Artificial intelligence enables businesses to collect and analyse massive amounts of data on customer preferences, buying behaviour and habits. Using advanced, pre-trained algorithms, digital businesses can create personalised, accurate recommendations, leading to increased sales and greater customer loyalty. This personalisation can be further enhanced by integrating augmented reality features that allow customers to visualise products in their real environment. In addition, the multiplicity of interactions with customers makes them progressive, constantly improving the quality of responses and the relevance of messages, even going as far as hyper-personalisation. Companies can also use AI-enhanced chatbots to offer instant, personalised assistance throughout the purchasing process, improving overall customer satisfaction.
Transforming an after-sales service with the cloud and generative AI
Companies are continually looking to improve the customer experience, particularly by optimising after-sales service. The combination of cloud technologies and generative AI offers considerable potential for creating seamless, augmented customer experiences.
Indeed, by building a knowledge base that will enable conversational AI to provide precise and relevant responses to customer queries, and by implementing a modern technological solution, it becomes possible to offer a fluid, personalised, and autonomous customer service; in other words, a responsive and efficient service, capable of relieving sales teams of repetitive and time-consuming requests.
Experimenting to develop your own convictions about EMI
In the same vein, the example of the automation of digital marketing campaigns has become a priority for many companies. The use of generative AI can help create hyper-personalised, responsive, and impactful campaigns, offering an improved customer experience and increased conversion rates.
All of this is only possible once the stakeholders have been identified so that responses can be trained by reinforcement, controlled, and validated by human beings. While we recognise the creative potential of IAGs, the transformative aspect can frighten employees, who fear that their job will be disrupted and would therefore be better off not taking the plunge. However, this fear is unjustified because generative AI does not intrinsically understand the data it processes.
Experimentation, with the interaction of all the stakeholders and the wealth of their best practices, is therefore essential in order to build up convictions on the subject and determine how AI will fit into the landscape, notably through tests with panels, for example, and support once AI has been integrated. The number of explorations and tests is decisive in order to reap the benefits of enhancing human creativity rather than replacing it.
Companies that adopt IAGs in an innovative, effective, and ethical way, in order to meet new customer expectations, will benefit from significant competitive advantages to improve their customer relations and de facto their performance. It is therefore imperative that they successfully integrate and adopt this dazzling technology.
But we are only at the beginning of the journey. Today, the question is not "should we adopt them" but rather "how should we adopt them". Over and above the transformative aspect, this will necessarily involve an "AI readiness" stage, to ensure that we have a sufficiently substantial data base to train AI on sound foundations.
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