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Hyper-personalisation and data-driven decision-making can boost the global banking sector
Home > Banking > Hyper-personalisation and data-driven decision-making can boost the global banking sector
Global Banking and Finance Review
Company
By Frode Berg, Managing Director – EMEA, Provenir
With the likes of Just Eat, Netflix, Amazon and Spotify shifting the dial, personalisation is no longer enough for consumers – they expect hyper-personalisation from their banking providers.
Hyper-personalisation sounds complex but simply refers to a detailed and nuanced understanding of each individual consumer’s needs, preferences, and behaviour – which are now changing more frequently as people battle volatile economic conditions and a fierce cost of living crisis.
Thanks to the evolving Open Banking environment, financial institutions have access to a plethora of customer data – and thanks to fintech advancements, they can also access the tools required to accurately analyse and categorise that data in real-time using machine learning and artificial intelligence (AI).
Challenges to overcome
However, there are major challenges when it comes to the implementation and use of this data and technology. A recent industry report highlighted that whilst 71% of banks are running personalised campaigns and targeted communications, less than half are leveraging customer data management, providing proactive advice and recommendations, and incorporating AI and machine learning recommendations. In fact, according to McKinsey: “The cumulative benefits are so great that the annual potential value of AI and analytics for global banking might be as high as $1 trillion.”
Provenir’s 2024 Global Risk Decisioning Survey, meanwhile, revealed 38% of respondents point to data quality and integration issues when it comes to delivering personalised offers to customers, while 19% struggle with real-time decisioning.
Such challenges hinder an organisation’s ability to offer customised products and services, resulting in reduced customer engagement and missed revenue opportunities – as well as increased churn and ultimately higher acquisition costs.
As consumer behaviour changes and customers demand greater personalisation and real-time offers, banks and other financial service providers must embrace digital thinking and advanced technologies, such as AI and data analytics, to successfully pivot towards hyper-personalisation, delivering tailor-made services in real-time.
This shift in mindset not only increases customer engagement – it also serves as a key differentiator in a crowded market. With the rapid adoption of customer analytics by fintechs and neo-banks, traditional incumbents must develop smarter data-driven strategies to remain competitive and retain customers.
The path to success
To achieve hyper-personalisation, banks will need to push past tired legacy infrastructure and deploy an agile tech stack with seamless integrations so they can monitor every aspect of their business in real-time.
Secondly, they need to offer innovative products that have a broader reach. For example, Buy Now Pay Later (BNPL) is popular because it effectively reaches an underserved population. With BNPL, hyper-personalisation is about financial services aligning themselves with the best merchants to drive up their customer base and reach.
Modern tech stacks should include access to lifestyle and contextual data, such as social media, to provide banks with a more complete picture of prospects so that offers can be better tailored to their specific needs.
Drawing on such data enables banks to use newer marketing models driven by AI. For example, Amazon doesn’t know a customer personally, but it does know what that person bought – and so suggests complementary products.
This translates to the financial services sphere where a consumer may get a mortgage online. At some point in the future, the provider could offer a loan for home improvements.
In order to achieve hyper-personalisation the use of data both internal and external is critical. This could be through use of open banking to understand how a customer likes to spend their money or via internal data where you can decipher a pattern on how the consumer spends e.g. they always pay for their holiday in January on their credit card so banks could be proactive in offering limit increases or promotional purchase offers in December to match the consumers spending habits. Either way, data is imperative in ensure hyper-personalisation is effective and tailored to each individual customer.
And when it comes to communication, customers now expect to be able to use whatever channel they want to interact with their bank, and whenever they want. Phone, AI-powered chatbots, websites, and mobile apps – customers in today’s digital ‘on-demand’ world will look to communicate with their bank as needed, and in multiple ways.
This is where banks can really start to differentiate themselves – by ensuring they can be reached 24/7 regarding any product, and for any reason.
This ubiquity of communication channels, combined with predictive and pre-emptive problem-solving, helps banks stand out from the crowd and show they are taking hyper-personalisation seriously.
Banks may choose to partner with fintechs to achieve hyper-personalisation – or they may decide to compete against them instead. Whatever approach they take, there isn’t any time to waste if they want to remain relevant and ensure that the industry thrives in the digital age.
Hyper-personalisation refers to a tailored approach in banking where services and products are customized based on individual customer data, preferences, and behaviors.
Data-driven decision-making is the process of making choices based on data analysis and interpretation, rather than intuition or personal experience.
Machine learning in finance involves using algorithms and statistical models to analyze financial data, allowing institutions to predict trends and automate decision-making.
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