Hyper Personalisation: 5 use case examples in digital banking | Subaio

Hyper Personalisation: 5 use case examples in digital banking

hyper personalisation in digital banking

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Esther Iyoha

Esther Iyoha

is a freelance writer for Subaio. She specializes in crafting digestible, action-worthy content for the fintech sector. Follow her on LinkedIn.

Hyper personalisation has become an essential part of the finance sector, including the banking industry. More consumer-centric systems continue to be the focus to meet increasing customer demands.

With hyper personalisation in banking, the primary objective is to leverage more relatable services and offers to improve the customer experience. Banking users are constantly on the lookout for tailored services that match others being provided in other aspects of their lives. With this in mind, banks have to continuously elevate their systems and offers in order to satisfy evolving consumer needs.

When it comes to banking personalisation, it all starts with the right data. Financial institutions have access to an abundance of information from consumer transactions everyday. This presents them with opportunities to implement data and analytics and create effective frameworks for a more personalised banking experience. These frameworks could then be utilised across all channels like mobile banking apps, for example, to increase customer loyalty.

What really is hyper personalisation?

By definition, hyper personalisation in banking is the consumer-centered approach to digital banking that utilises behavioural data science and the use of Artificial Intelligence (AI) to offer relatable products and services to consumers.

According to a McKinsey study,  banking personalisation can “reduce acquisition costs by as much as 50%, lift revenues by 5 to 15%, and increase the efficiency of marketing spend by 10 to 30%”.

Hyper personalisation generates insights into consumer needs by harnessing real-time data,  giving primary bank account users the comprehensive digital experience they want.

Driving factors affecting the the rise of hyper personalisation

In addition to the increase in revenue, other factors that drive the need for banking personalisation also include:

  • The need for clear brand distinctiveness: Hyper personalisation provides a competitive advantage to establish brand visibility and a clear, unique value proposition.
  • Open banking: Creating easy access to product data along with customer transaction data. This offers digital banks the right tools to customise their products, pricing, and services to nurture their customer base.
  • Rapid technological innovation: More financial institutions are working to keep up with industry expectations for increased customer connectedness through their digital channels.
  • Financial inclusion: With hyper personalisation, more people can have access to certain benefits and service offers that they wouldn’t have had otherwise. As in the case of households who may be experiencing unexpected life changes. Their banks can leverage customer data to customise certain financial offers that can support them.

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5 best examples of hyper-personalisation in digital banking

Here are some hyper personalisation examples that have gained momentum in the digital banking space so far:

1. Custom-made product recommendations.

The type of customisation that users enjoy when platforms like Netflix present them with movies and shows that they ‘might like’. This personalised style is beginning to shape the future of digital banking as well.

Financial institutions like the Bank of Ireland are turning to this banking style. The BOI announced their intention to become the ‘Netflix of Banking’. Bank of Ireland is implementing data science, artificial intelligence, machine learning and analytics, to achieve this end. The target goal is to to accurately recommend the right products and offerings,  in order to meet the needs of their customers depending on what’s happening in their lives.

2. Predict customer needs.

By creating industry ROI-oriented ability to deeply analyse and micro-target customers. Banks can now use enhanced transaction data for effective cross-selling and exceptional banking experience for their customers.

This was the case when the fintech software company Personetics partnered with KBC, a multi-channel bank insurance group based in Belgium. This partnership led to the roll-out of Engage–the white-label solution created by Personetics to be integrated into KBC’s mobile channels. Engage offers personalised data & insights, financial advice, and automated money management to KBC’s customers.

3. Providing financial management.

Many financial institutions are looking to build long-term customer loyalty by providing personalised finance management solutions.

A specific example would be the partnership between Meniga, a software company based in Iceland, and Länsförsäkringar, one of the largest financial institutions in Sweden, to launch a new Personal Finance Management (PFM) solution. Their software solution harnesses Meniga’s platform to provide customers with real-time data on their spending habits. While also acting as a personal finance advisor to help them understand and better manage their finances.

4. Make promotional offers.

These are offers made in a timely manner that resonates with the customer’s individual purchasing patterns. With sufficient data readily available to accurately drive personalisation, financial institutions can now make customer segments for promotional offers based on the Customer Buying Cycle.

This is the case with Santander Bank, the third largest bank in Poland. Utilising Artificial Intelligence (AI) to generate offers that are targeted to respond to the changing needs of both existing and potential customers. Santander Bank aspires to be the “best open financial services platform” that adapts to customers’ individual tastes and preferences.

5. Offering personalised loans.

By partnering up with fintechs, financial institutions can effectively utilise customer data on loans that their customers might have. And then propose a better, more personalised offer to them. Especially if the loan happens to be from a competitor.

An excellent example of this is the Dutch bank ABN Amro, in partnership with the fintech company, Subaio. Based on current insights into their customers’ payment data,  ABN Amro offers a wide range of personalised loan offers that easily convert. This is a result of their knowledge of their customers’ recurring payments, including their loans and other financial products.

These hyper personalisation use cases are among several other examples of how this trend is gaining popularity in the banking world. And especially now as digital banking has become a part of our new normal.

Conclusion

Hyper personalisation has become a dominant trend in the recent decade. In the past, banks were able to thrive under more traditional systems not necessarily geared toward digital trends. Demands for a more personalised banking experience continues to grow. Leading banks into making investments towards technological solutions,  leveraging customer data to connect with their customers. Delivering the right products and service offerings to them at the right times.

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