If you’re a small bank or credit union, you might think advanced analytics is a waste of time and money. You know your customers and get your best insights from talking to them, so why pore over reams of data?
But such interactions are one of the greatest advantages smaller institutions have over big rivals. The truth is that every bank and credit union needs the kind of detailed understanding of its customers that only data can provide.
There’s worrying evidence that smaller institutions are falling behind large counterparts when it comes to analytics. The Financial Brand reported in December that only 9 percent of institutions with assets less than $1 billion had invested in advanced analytics, compared with about half of banks with more than $50 billion.
That’s a problem for community banks and credit unions, argues Frank Koechlin, president of Empower Your Analytics, a data analytics and marketing services firm for the financial services industry. Writing in The Financial Brand, Koechlin says:
In the old days, small, local institutions offered personalized, hands-on service, but generally waited for consumers to approach them with specific needs. But times have changed, and banking providers of all sizes must now proactively approach accountholders. Banks and credit unions must arm themselves with data to create cross-selling conversations and stimulate needs-based opportunities.
Data Improves Banks’ Customer Retention, Profits
How are banks doing this in the real world? Consider a few examples reported recently by the consulting firm McKinsey & Co.:
- A European bank used algorithms to increase its customer retention rate by identifying the customers most likely to reduce their business in the future. Such algorithms work by analyzing what is known about customers who have already cut their business or left entirely, then applying that knowledge to existing customers. The bank used that intelligence to craft a campaign targeting those at-risk customers, which “reduced churn by 15 percent,” McKinsey reports.
- A U.S. bank’s private banking division was handing out discounts to select clients, arguing that the discounts built relationships and generated more business with high-value customers. After studying the data, the bank was able to identify which discounts did not deliver the value the bankers thought. After making changes, “revenue rose by 8 percent within a few months,” according to McKinsey.
Insights such as these are possible only with good data, not good instincts. In fact, as the U.S. bank example shows, the instincts of even experienced bankers can sometimes lead institutions down the wrong path.
How Banks Are Using Analytics Today
Modern banks of all sizes are finding the most value in analytics by deploying it in several ways:
Managing risk to the bank. McKinsey notes that banks can manage risk by deploying “analytics-aided techniques, such as digital credit assessment, advanced early-warning systems, next-generation stress testing, and credit-collection analytics.” Moreover, analytics can help banks monitor their own performance when it comes to regulatory compliance, argues Deloitte.
Managing risk to customers, including the risk of fraud. More banks are deploying machine learning to better understand customers’ spending habits so they can flag suspicious activity in near-real time. Here’s one example I learned about recently. A customer received a call from her financial institution at 10 a.m. on a weekday, asking if she’d just spent more than $100 at a store located more than 200 miles from where she was standing. She hadn’t, of course, and the institution took immediate corrective action.
The bank was using machine learning to build a model of the kind of places the customer typically spent money so it could quickly identify suspicious transactions. It knew she had never used her card near the town in question and didn’t often use it on weekday mornings. And it probably knew that she didn’t frequent the type of store where the purchase was made.
Driving growth and profitability. A deep understanding of customers and what they need can help with retention. If a bank’s customer mix skews over 50 years of age and affluent, they’ll want different financial products than, say, millennials who are just starting to save. At the same time, analytics also presents opportunities for customer acquisition by allowing banks to develop targeted marketing appeals based on real information about the needs of particular customer groups. Finally, as McKinsey notes, good analytics “can help banks wring small improvements out of almost all their everyday activities.” For example, one bank “used new algorithms to predict the cash required at each of its ATMs across the country, combining this with route-optimization techniques to save money.”
Improving the customer experience. Data not only helps banks understand what their customers (and potential customers) need, it can also help them deliver it. As we noted in an earlier blog post, younger customers are more likely than older generations to regard “authentic” experiences with brands as important, yet they also demand the ability to do everything, including banking, through mobile devices. That makes them perfect target customers for a digitally savvy community bank — one that demonstrates its authenticity by proving it understands its customers while providing convenient, intuitive digital banking options.
If the reasons above don’t persuade community banks and credit unions to take data analytics seriously, then consider this: Larger banks are increasingly using data to build their businesses, and those that don’t risk being left behind. As Karan Bhalla, managing director at IQR Consulting, tells American Banker, “The reason [small banks] need to budget for this is because bigger banks are absolutely budgeting for this.”