‘Hyper-personalisation’ growing sophisticated
Since 2019, UOB has developed close to 70 machine-learning models to anticipate customers’ needs
THE next time you visit a UOB branch, don’t be surprised if you are routed to the wealth banking queue without explicitly seeking the service. It might be the effect of the bank’s “network net-worth wealth model”.
Over the past three years, UOB has developed close to 70 machine-learning models to help it pick out those with the potential to be converted into wealth customers.
The network net-worth wealth model, for instance, takes into account each customer’s transactional footprint. Think of the fund transfers people make when they go Dutch with their contacts, UOB’s head of group personal financial services Jacquelyn Tan told The Business Times.
Tan oversees the design of “hyper-personalised” experiences for those banking with UOB. Most of these experiences come in the form of “insight cards” delivered on the home screen of the bank’s all-in-one UOB TMRW app.
When asked how personalisation is different from traditional marketing, Tan said that normal segmented target marketing is still quite broad-based, focusing on quantity over relevance. With personalisation that is powered by artificial intelligence (AI), the machine is able to learn and adjust to customers’ preferences as they change across life stages over time, she noted.
“The benefit compounds,” she added, stating that it is then down to the bank to think about how to strategically tap the data and push it to all its available channels, including relationship managers directly dealing with customers at the branches.
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“That’s when you achieve seamless online to offline cross-channel engagement,” Tan said.
What she described – personalisation, or the tailoring of messages, at scale – has been identified as marketing’s holy grail: Achieving greater customer satisfaction in an era of instant gratification where generic messages – often seen as off-target – won’t cut it any more.
Banks have been on a mission to get this right.
UOB first launched AI-driven personalised insights over UOB Mighty – TMRW’s predecessor – in 2019. In the early days of this AI push, the bank had leveraged the expertise of its partners, Personetics and Meniga, to build a digital engagement engine that can personalise the banking experience for each customer, such as individualised Chinese zodiac outlooks during Chinese New Year.
Today, the personalisation comes from models built by the bank’s own data scientists to meet heightened customer expectations amid the rapid adoption of digital banking in the last two years.
One such expectation is a desire for the bank to make things simple and transparent. Customers are telling the banks to “track for me” so they can make smarter money management choices, Tan said.
Responding to this, UOB’s customers insight cards might show, for instance, how close a customer is to earning a higher interest through the mobile banking app, she said.
This defies the “old thinking that banks would rather you fall below the interest balance, so we save on interest”. Tan said it is more important for the bank to build trust: “You win hearts when you give customers what they want when they do not expect it.”
UOB also informs frequent travellers when it makes sense for them to open a KrisFlyer account for the bonus miles, or serves up restaurant recommendations according to customers’ past transactions.
With these efforts, more than 1 million monthly active users now interact with the insight cards delivered through the TMRW app, Tan said, adding that users viewed some 30 million insight cards in the first half of this year.
When asked what is next for the app, Tan said that personalisation could take an educational slant.
“In Singapore, we are very well banked... Payments are not a pain point. One thing that I feel we can do more as an industry is really about financial planning and wealth planning,” she said.
“There is a role we can play in driving educational awareness, especially for the young who have time on their side. Things like time in the market, dollar cost averaging, these are things that really need to start from a younger age.”
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