Amazon, Netflix, Airbnb, Uber, and other disruptors have raised the bar on what customers expect from a business. These online giants have figured out how to use their customer data to make personalized recommendations and predict when customers are going to buy — and present offers at just the right time.
Brands that use personalization report an average growth of 20% in sales (Monetate research), and customers feel less spammed and more like they’re in control of the experience.
It’s no surprise that consumers are looking for that same personalized, frictionless experience when interacting with their financial institutions, whether through mobile banking, your website, at a brick-and-mortar branch, or at one of your ATM locations.
And it pays off for banks who can engage their customers. According to a 2013 Gallup study, fully engaged customers bring in an additional $402 in revenue per year to their primary bank, as compared with those who are actively disengaged. Even better, the research said 71% of fully engaged customers believe that they will be customers of their primary bank for the rest of their life.
That could be your bank, but only if you can reach your customers in ways that feel natural and valuable to them.
Customers want to be engaged with the right messages at the right time
Imagine if you could understand your customers so deeply and predict their buying patterns so clearly that you could deliver targeted marketing only to those ready to invest in more products with your bank. Not only that, what if you could know what to say to them and on which channels to reach them? How would that impact your business?
The trend is clear: financial institutions must adopt a customer-centric business model now to ensure success in the future. This puts banks like yours at a crossroads, and the problem is where and how to embark on that journey.
Tackle your greatest challenges
The formula seems simple. Increase your engagement and you’ll increase your revenue. But meanwhile, you’re under pressure to acquire new customers, maintain your base, forecast/reduce risk, manage capital, navigate security compliance and financial regulations, and optimize the business.
You may also grapple with siloed data, legacy systems, and outdated processes, all seemingly monumental challenges that may adversely affect your customer experience.
For example, your customers and employees may not have access to the right data at the right time to provide an optimal experience. Or, from a marketing standpoint, different departments within your company may be targeting the same customers, resulting in too many emails.
Or your customers may get untimely messages about promotions that have passed or receive communications that don’t apply to their current situation. This creates frustration and a poor user experience that may be enough to make your loyal customers turn away.
Other banks have been in your shoes, facing the same challenges and fears, but they’ve made major strides in putting the focus on the customer. They’ve found success through the “magic” of machine learning (ML). ML enables your staff to prioritize your over-capacity bankers’ focus and marketing spend on opportunities that are real.
ML is a modern technique that uses algorithms to analyze enormous amounts of data. Machine learning models learn on their own and identify insights and patterns to predict future behavior. Machine learning algorithms connect the dots far faster and deeper than people can, exposing patterns in your customers’ behavior that empower your team to take actions that will impact your business’ top and bottom lines.
Unlike traditional analytics tools, ML can evaluate account holders, securities, and transactions in real-time. If you want immediate decisions integrated in the moment, machine learning is the answer.
And, good news, even though you may feel you are behind the curve right now, you have something that the younger fintechs you compete against don’t − a wealth of historic data that can be “mined” by ML to answer your specific business questions. Some organizations need help in improving the quality of their data for effective use in the machine learning model, and that’s not an uncommon challenge. But good data will be your key to success.
Machine learning applications in finance
Banks have found many successful ways to leverage machine learning. For example, they use it to answer specific business questions across all departments, including:
- How do I increase my customer wallet share, including:
- What are my best opportunities for cross-sell/remarketing my existing customers?
- Can I identify customers that we can convert from other banking institutions?
- Can I identify loan-default risk early enough to take an action?
- Can I dynamically price securities based on investor demand and market saturation?
- Can I predict my cash and reserve activity to optimize liquidity levels?
- Can I identify account holders’ attrition activity before they disengage?
- What percentage rate and product messaging would make my ideal prospect buy?
The first step towards engaging customers with the right messages at the right time is to capture what questions your bank wants to solve. With these questions in hand, you can move to the next step, seeing how much predictive value these “use cases” for machine learning will give your financial organization.
Case in point, these very questions are how it started for a large, institutional bank sitting on decades of financial transaction data we worked with. They wanted to more accurately predict member activity and drive better returns on cash reserves – and leveraged machine learning to do it.
Our machine learning model identified patterns in their transactions, which spanned hundreds of credit unions and billions in cash to predict the deposit activity of millions of credit union members on a daily basis.
The result? We freed $40 million in excess cash reserves. The insights gleaned also empowered the organization to pass on greater returns to members by selling short and long-term securities, arbitrage, and reducing borrowing fees.
Another institution, Primary Financial Corporation (PFC), found great success using machine learning to improve their sales targeting.
PFC wanted to predict CD issuers’ funding needs and institutions’ desires to invest. They developed machine learning models that synthesized PFC’s financial and competitive data to price securities, identify buyers, and project trade profitability.
By the time the first phase of the project was complete, PFC could predict with over 80% accuracy and 70% precision the likelihood of a particular investor to buy a given investment.
The common thread in these stories is that both organizations had an abundance of historic data at their fingertips, but they hadn’t explored how ML could help them retain more deposits, sell more products, or reduce their financial risks.
The rapid predictive insights that machine learning continues to provide to both companies has been game-changing. And both are now exploring other ML applications.
Machine learning is widening the gap between banks who embrace it and their competitors who haven’t. If you don’t improve your banking experience, your customers will turn to another bank or even be serviced by a fintech.
As you navigate how to become that customer-centric organization you want to be, explore machine learning as a way to get you closer to your customer and see rapid results. Start by coming up with specific questions that your business needs to answer, and take time to learn more about what machine learning can do in your organization.
Contact Fusion Alliance to discuss if ML is right for your project.