Welcome back! In my previous post, I set out to explore the seven biggest mistakes you can make in banking personalisation, from thinking that all data is good data to dismissing generative AI. Here comes round two.
4. Letting customers’ financial health deteriorate
Amidst rising household costs, ringing recession alarm bells and record levels of debt, 59% of bank customers in J.D. Power’s 2022 US Retail Banking Advice Satisfaction Study said they expected their banks to help them boost their financial health. This view was echoed across different age groups and personal financial health categories. However, overall satisfaction with the guidance respondents received fell down a significant 30 points (on a 1,000-point scale) from 2021. The downward movement is the most pronounced in customer perceptions of the frequency and quality of the advice provided.
Long story short: people actively want banks’ help to get financially fit, but even if they get it, it fails to deliver on expectations. In the age of big data analytics, there’s exactly zero excuse for that.
Algorithms can spot if a customer’s income has been negatively affected by the economic fallout, allowing banks to build use cases for responding to such scenarios. For example, people with no salary coming into their accounts can be flagged to receive offers for emergency loans. Peer comparisons and cash flow patterns can reveal if someone who’s currently financially stable might be hit with financial trouble in the future. Then the bank can alert them to scheduled transactions that might send them into the red or help them stick to the 50/30/20 budgeting rule to right their financial ship.
5. Not talking about the green elephant in the room
Eight in ten banking customers want to know the carbon impact of their spending. That’s according to research conducted by carbon footprint management expertCogo and Behavioural Insights Team, who surveyed 2,007 UK mobile banking users to find out how they feel about their banks encouraging them to cut their environmental impact. This suggests a 52-million-strong customer pool interested in green banking features and products in the United Kingdom alone.
Some banks are taking note. As they should, not only because of customer demand but also because they’re better positioned than anyone to help people make greener spending choices and make a real difference in the fight against climate change.
Raiffeisen Bank Romania, for example, has recently launched its carbon footprint monitoring functionality, which calculates users’ monthly CO2 footprint based on the transactions they make using their Raiffeisen current accounts. To make carbon emissions data digestible for app users, the widget can also display peer and national average rates and greenhouse gas equivalencies and highlight which spending categories are the main culprits.
6. (Still) trying to sell harder, not smarter
This might be an unpopular opinion but I don’t believe people hate ads. I think people hate ads that are irrelevant to them. Unfortunately, many promotional offers they receive from retail banks fall into this category.
This is because, as we explained in our eBook on personalised digital sales in banking, there is a popular misconception among bankers that putting customers before sales targets equals less sales. This thinking is deeply flawed. Banks who stick to this legacy approach often do so because they fear that otherwise they will risk achieving their sales target of, let’s say, 10,000 credit cards per year. Relying on behavioural and data science in creating promotional campaigns, however, they’d have the same chance of selling them, except to different customers. The ones who would actually need and use them.
For example, the bank could start by analysing customers’ point-of-sale transactions made with a debit card over a certain period of time. Next, explore the benefits they can offer customers for credit card purchases, including loyalty programme points, cashback, no-interest repayment plans or travel rewards. Then comes calculating the amount of rewards each customer would have earned if they had swiped their credit card instead. This could be followed by a personalised message to let customers know what they’ve missed out on and to prompt them to use or apply for a credit card.
7. Putting off generative AI adoption
“In 2021, among executives of the world’s 2,000 largest companies (by market capitalization), those who discussed AI on their earnings calls were 40% more likely to see their firms’ share prices increase – up from 23% in 2018,” according to analysis by Accenture. But when it comes to making the most of AI’s full potential, most businesses are barely scratching the surface – financial service providers included.
There’s not one but two mistakes banks can make here.
The first has to do with timing. As in, looking at the AI opportunity as something they might need to compete on further down the line. It isn’t.
In March, Bloomberg made headlines by announcing BloombergGPT, the first large-scale generative artificial intelligence model specifically designed for the financial sector and trained on domain-data sets. The same month, news broke of Morgan Stanley’s rollout of a new, OpenAI-powered chatbot to help its 1,600-strong staff tap the bank’s vast repository of research and data. “This is like having our chief strategy officer sitting next to you when you’re on the phone with a client,” commented Jeff McMillan, Morgan Stanley’s head of analytics, data and innovation.
Imagine the benefits these applications are delivering to their users – and their customers – right now. But there are tons of other ways banks could leverage AI to drive customer satisfaction, organisational resilience and their bottomline at the same time. Think algorithms that can spot fraudulent patterns in financial data to prevent and minimise fraud losses and boost customer trust, perform credit risk assessment so banks can make better lending decisions faster, tailor outreach campaign design and messaging down to individual preferences, and so on.
Are you guilty of making any of these personalisation mistakes? Get in touch so we can help you make sure they won’t cost you (more) money and market share.
