With growing digitalisation, the way we bank has undergone a huge transformation. Banks today can no longer expect to operate as they did earlier. Growing competition from fintechs and new-age digital banks emphasise how banks currently fall short of providing superior customer experiences. Customers expect personalised engagement from all businesses they deal with, including banking partners. They have little patience for one-size-fits-all approaches and view generic messages as spam. Customers also place value on efforts that focus on building deeper insights and go beyond transaction facilitation.
A report by Deloitte found that customers now demand the same level of sophistication, immediacy and personalisation in their interactions with banks as they do in other industries. They are even willing to share their data if assured of receiving personalised services. Despite personalisation having been a buzzword in the banking industry for some time, 94% of banks are unable to provide customers with the kind of hyper-personalisation they currently prefer.
What is hyper-personalisation?
Hyper-personalisation refers to a thorough and nuanced understanding of each customer's needs, preferences and behaviour; banks leverage data and analytics to gain this understanding about their customers. This allows banks to offer targeted services and products that are tailored to the individual customer, resulting in a more engaging and satisfactory customer experience. In other words, hyper-personalisation is ultra-precise messaging in near real time using machine learning (ML) and artificial intelligence (AI) to process recent customer activity, events and real-time signals.
However, despite the increasing demand from customers for hyper-personalised services, implementation isn’t always simple. A report titled ‘Maximizing Digital Engagement’, co-produced by Infosys Finacle and Qorus, revealed that although 71% of banks are running personalised campaigns and communications, less than 50% are leveraging enterprise customer data management, providing proactive advice and recommendations, data-driven micro-segmentation, and incorporating human-augmented sales, leveraging AI and ML recommendations.
Moving towards hyper-personalisation – the three stages
For banks to enable hyper-personalised conversations at the population scale, there are three key aspects that they must focus on:
Truly know your customer (KYC)
A deep understanding of customers, that goes beyond the standard demographic data, is crucial. This includes accessing external sources such as social media, past interactions and building customer data platforms to gather information from multiple institutions.
Alternatively, banks can adopt a ‘customer genome’ approach based on demographic factors such as age, gender, ethnicity and life stage. By creating models from this data, banks can understand customers better and anticipate their needs. This can enable banks to shrink their go-to-market timelines and create a boundary-less data platform that delivers personalised experiences. One example is the increasingly popular buy now pay later model where credit is offered based on the customer’s creditworthiness. Today, some progressive banks go beyond traditional parameters, considering behavioural, demographic and social data to arrive at a decision in a matter of minutes.
This requires a strong architectural foundation with a standardised data model that can help banks drive efficiency in product design, avoid inconsistent and overlapping product definitions, and enable personalised pricing and bundling of banking products and services. This is essential for banks to personalise products to their customers more effectively and provide superior customer experiences.
Understanding and analysing customer data
Data can offer three key types of insights:
- Descriptive insights – This explains and visualises the nuances of the customer’s transactions, spending patterns, assets held, portfolio performance, etc.
- Diagnostic insights – This answers the ‘hows’ and ‘whys’, helping banks to better understand financial behaviour.
- Predictive insights – This helps banks foresee the status of a customer’s financial health. It can be used to alert customers about a potential cashflow crunch, penalties, an unplanned large payment, or an opportunity for early closure on a loan. Identifying patterns in customer data can help banks anticipate their customers' needs and tailor their offerings accordingly. Predictive analytics can also help banks identify and prevent fraudulent activities before they happen.
This requires banks to establish a solid groundwork and take advantage of modern technologies such as AI, ML and cloud computing. One crucial component is the implementation of an asset library that consists of a data layer, analytics layer, decisioning layer, and execution-and-measurement layer. This asset library can help consolidate, standardise, and modularise data and analytics while integrating decision-making and execution processes. Scaling is critical, which will mean leveraging the cloud to support AI and ML. According to BCG, about 86% of banks are considering cloud solutions (SaaS and PaaS) for their next-gen CRM platform.
Actioning based on insights
Deep insights coupled with clarity on desirable outcomes can empower banks to nudge customers to make informed decisions. These nudges can be both personalised and contextualised at scale. Recommended actions could be around choosing to complete a task through the next best action, allowing for integration with lifestyle journeys, automated savings, etc.
Achieving hyper-personalisation at the population scale requires banks to leverage conversational AI to deliver personalised interactions in real time. By using chatbots and voice assistants, banks can create a seamless and personalised experience that is available round-the-clock. Conversational AI can also help banks identify when a customer needs to speak to a human representative and route them to the appropriate team member. This reduces wait times and improves overall customer satisfaction.
One great example is Erica, a virtual financial assistant from the Bank of America that drives personalised, proactive, and predictive conversations with customers based on insights derived from sources such as account balances, past transactions, spend patterns, payment alerts, duplicate charges, etc. Similarly, Monzo, a UK challenger bank, analyses user behaviour and identifies specific pain points, thus empowering customer service representatives to handle 85% of daily business queries directly, without the need for consultation with the Monzo data team.
There is no doubt that hyper-personalisation is the future of banking. By adopting a comprehensive approach that involves true KYC, marrying customer centricity and product centricity, and analysing customer data to derive actionable insights, banks can make their journey towards hyper-personalisation successful. Moreover, it requires a cultural shift within the organisation to prioritise customer centricity and embrace new technologies.
However, the benefits of hyper-personalisation are well worth the effort. By delivering personalised services, banks can differentiate themselves in a crowded market and become true advisors of financial well-being, and most importantly, stay ahead of the competition.
