With the cost of motoring rising faster than inflation and vehicle prices—both new and used—continuing to climb, consumers are increasingly turning to finance when purchasing their next car. At the same time, ongoing financial pressures mean customers are more inclined to shop around for the best possible deal. Acquiring new customers is already a challenge,
Hyper-personalization is fast becoming a core component of the financial services industry,
It has risen rapidly in prominence thanks to the emergence of machine learning and AI technologies, allowing banks to tailor their offering specifically to customers and their exact requests.
Indeed, harnessed correctly, hyper-personalization can enable financial services providers to interact with their customers in an empathetic manner that resonates directly with them.
What is hyper personalization?
Hyper-personalization in digital banking refers to the use of advanced technologies, such as artificial intelligence (AI), machine learning (ML), big data analytics, and behavioural insights, to deliver highly customized and relevant financial products, services, and experiences to individual customers.
Hyper-personalization goes beyond traditional personalization by leveraging real-time data and predictive analytics to anticipate customer needs and preferences.
In short, it moves completely away from a one-size-fits-all approach, instead, treating customers as unique individuals with their own unique aims, ambitions and desires.
The importance of hyper-personalization in banking
Hyper-personalization is becoming not just important, but vital.
Consumers are increasingly expecting personalized banking. If a bank, building society, credit union or other provider does not provide at least some form of personalization to show that they value each customer as an individual, they are likely to lose them to those providers that do take this approach.
There are a host of statistics which underline the value of hyper-personalization, such as those from McKinsey that state over 70% of consumers expect personalization and will get annoyed when it doesn’t manifest.
There are endless other pieces of research, such as the one by Accenture which states that three quarters of consumers will not purchase from a supplier without personalization.
How to implement hyper-personalization
The key to enabling a provider to truly get into the mindset of their customers and deliver banking personalization is to find out about their goals, aspirations, their lifestyle and motivations.
However, in a financial services world underlined by the closure of branch networks and the rise of “anonymous” online banking, the personal, face-to-face method of truly getting under the skin of customers is disappearing. One way of countering this is by using the digital footprint left by today’s customers – meaning there is a raft of data that can be analysed to deliver hyper-personalized sales and marketing campaigns at scale.
By plugging in a sophisticated, GDPR-compliant AI tool, a bank, credit union or building society can turn the few and basic touchpoints it has on its clients’ lives, such as age, gender, geographical location, and nationality, into a rich source of intelligence. AI can analyse detailed behaviours such as the purchases their clients are making, what time they are accessing emails and even the tools they are using, such as a laptop or a smartphone.
The possibilities are endless. AI takes out the labour-intensive process of trawling through customers’ data and can analyse reams of information, covering thousands of customers, at the touch of a button.
The benefits of hyper-personalization
Hyper-personalization in banking offers numerous benefits for both financial institutions and their customers.
Chief among these is the provision of a much-enhanced customer experience, which is vital in today’s ever-competitive financial services landscape. Customers can receive recommendations and solutions that are relevant to their unique needs and preferences, while real-time insights and proactive engagement save customers time and effort. Personalized interactions also foster a sense of being understood and valued, improving overall satisfaction.
Such benefits, in turn, lead to increased customer loyalty and retention for providers. Personalized communication builds trust and strengthens customer relationships, while anticipating and addressing customer needs proactively keeps them engaged with the bank.
Another major benefit for financial providers is a boost to revenue and profitability. Banks, credit unions or building societies can suggest complementary products, such as credit cards, insurance or loans) based on customer behaviour and life stage, with these relevant, targeted offers being much more likely to be accepted by customers. Moreover, personalized financial solutions encourage customers to consolidate their banking activities with one institution.
The deployment of these approaches also leads to improved operational efficiency. Advanced analytics reduce guesswork, enabling more efficient allocation of resources, with AI-powered systems streamlining processes including customer support, marketing, and loan approvals. By focusing efforts on highly relevant interactions, banks save on broad, less effective marketing campaigns.
Challenges in implementing hyper-personalization
Implementing hyper-personalization in banking comes with several challenges due to the complexity of technology, data management, and customer expectations.
Financial providers often have fragmented data spread across multiple systems, making it difficult to create a unified customer profile, while the sheer volume of data means that managing and analysing it in real-time requires significant computational resources.
Concerns with privacy and security can also cause major issues. Financial providers must adhere to strict data privacy laws like GDPR, CCPA, and others, which can limit how customer data is used, while storing and processing large volumes of sensitive data increases the risk of breaches and cyberattacks.
Technological barriers also need to be overcome. Legacy systems may not easily integrate with modern AI and machine learning tools. There is also a scalability issue – ensuring hyper-personalization works efficiently across millions of customers can be technically demanding.
However, a strong AI-driven hyper-personalization platform takes all these issues into account. It can manage a high volume of data while ensuring no data breaches occur and can integrate alongside existing technology infrastructures that might be in place.
Conclusion
If a financial provider ignores the rise of hyper personalization, it does so at its own peril.
It is now a vital part of the financial services landscape and is set to become even more important in the coming years as the expectations of customers continue to become even higher.
The benefits of hyper-personalization are clear to see. They deliver mammoth benefits to consumer and provider alike, allowing both groups to enjoy a symbiotic relationship.
However, introducing hyper-personalization does not come without issues – but these can be overcome by working with a trusted specialist partner.
To learn how Fintilect can help with a tailored hyper-personalization programme, please contact us.