Hyper-personalisation is an imperative, not an option, in a digital economy. Indeed, as various industries adopt technology “as” their business, rather than “in” their business, the combination of smart devices, rapidly-evolving CX (Customer Experience) capabilities and real-time processing of big data, means that new opportunities arise for banks to meet customers’ needs on a highly-personalised and dynamic basis.
Customer experience is predicted to overtake price and product as a firm’s brand differentiator, with customers increasingly expecting interactions with their bank to be as sophisticated, immediate and personalised as their experiences with other industries. In addition, COVID-19 has accelerated digital transformation across sectors such as in the manufacturing sector where mass customisation is expected to become a new reality in the coming years. Consequently banks are increasingly expected to deliver hyper-personalisation.
Hyper-personalisation can be defined as using real-time data to generate insights by using behavioural science and data science to deliver services, products and pricing that are context-specific and relevant to customers’ manifest and latent needs – according to Margaret Doyle, Partner, Chief Insights Officer, Financial Services, Deloitte – in an article published by UK Finance. These insights are garnered using AI to analyse data.
AI has already enabled banks to personalise their products and services and thus enhance CX. Ubiquitous AI applications include packaged products and services, credit scoring with predictive analytics and chatbots for customer service. AI is potentially a golden opportunity for banks to differentiate their brand by delivering hyper-personalised products and services to customers.
While AI as a technology has been around for the past 60 years, what makes it especially powerful today is the access to more data than ever before. Yet this access also poses the biggest problem for banks in terms of data management.
Many banks have yet to set up an ecosystem approach as opposed to a siloed approach, in order to capture, manage and extract value from a combination of external and internal data. For those banks that successfully set up an ecosystem approach, two areas that underpin CX are particularly ripe for AI-based applications – building: (1) an accurate view of the customer and (2) an emotional connection with the customer.
Building an accurate view of the customer
The application of reinforcement learning, which is based on the continuous learning of the machine itself, can allow banks to hyper-personalise their products and services by using a deep understanding of their customers’ preferences. As such, banks will be able to understand, for example:
- The best time of day to contact a customer
- The most suitable format
- The most convenient channel
By leveraging AI to deliver hyper-personalisation, banks will be able to rationalise their product and service strategies, whilst guaranteeing a strong CX. This will boost returns, which are currently below banks’ cost of equity.
Building an emotional connection with the customer
Across different sectors companies often overlook the importance of emotional connection behind CX. Research shows that customers primarily rate brands based on personal feelings and experiences, rather than information.
Research across hundreds of brands in different sectors shows that the most effective way to maximise customer value is to move beyond mere customer satisfaction and to build an emotional connection. From a profitability perspective, on a lifetime-value basis, emotionally-connected customers are more than twice as valuable as even highly-satisfied customers.
Because artificial empathy – which lies at the heart of emotional connection – continues to be one of the biggest challenges in AI, using AI to build an emotional connection is still a long way off. Nonetheless, AI can help banks become better at recognising the emotions of their customers – and at interpreting them.
Indeed, machine learning systems can come to recognise patterns (e.g., speech patterns or unusual smart device usage) that are associated with a specific emotion, using a variety of data sources. Subsequently, banks can integrate these insights into the types of products and services they are offering and into how they are offering them to help build an emotional connection with their customers.
AI is set to become more deeply intertwined into the core of banks’ business models. Technological advances expressed through innovative CX will further heighten the importance of emotional connection. With advances in artificial empathy likely over the medium term, banks’ capacity to hyper-personalise their products and services is set to grow.
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