Delivering a great member experience in a tech-centric economy is a never-ending process.
Consumer demand for convenience continually raises expectations. If you’re not constantly improving, customers can easily take their business elsewhere through their tablet or smartphone.
Even business models born from the concept of convenience are in a constant state of improvement. Take fast food, for example. An industry dedicated to getting us fed fast and fret-free is never resting on its laurels.
In April, McDonald’s—the epitome of convenience—launched a new mobile ordering and payment platform. This move follows an increased focus on technology for the company, including kiosk ordering and a test of delivery service in Florida via mobile ordering with ridesharing firm UberEats.
While their customers’ experience will improve, think of all the data the fast food giant will get in return. It’s not hard to imagine McDonald’s analyzing that information to uncover patterns and preferences, which in turn would lead to better business decisions.
For example, if the data from mobile ordering showed a preference for specific items or times of day, menu enhancements and staffing adjustments could be made to meet those preferences, and in turn improve the customer experience even more.
How does this apply to auto lending? Think of all the data generated as your members swipe, click, and digitally manage their finances.
This treasure trove of information is an invaluable resource to gaining a deeper understanding of members’ behavior.
Armed with this insight, what if you could develop your targeting so members receive not only personalized offers, but only those that are actually relevant? What if you could enhance your risk-adjusted pricing model so you could provide the most attractive rates?
There’s a good chance that, as your members’ experience improves, so would your business, which would lead to more data—and more insights.
While there are many considerations involved with launching a data analytics strategy, including available resources and expertise, here’s a simple five-step process to help you begin looking at your data:
1. Identify a narrow question. For example, “can we identify and present auto loan offers to members who are about to pay off an existing loan?”
2. Capture and use only a subset of data. Gather only the data that is most relevant to address the question.
3. Take a simple approach. The term “analytics” can be intimidating. Don’t think you need sophisticated modeling formulas, etc.
4. Don’t rush the results. This is a learning opportunity. Give your team time and freedom to explore and research.
5. Test and learn. Based on what you learn from these simple analytic experiments, conduct some targeted campaigns to test your preliminary insights.
Embrace your data as an opportunity, and create a vision illustrating what you’d like it to do for you and your members.
Predictive analytics would let us know ‘Sam’ has clicked on this site this many times, and he’s obviously interested in purchasing a vehicle,” says Brett Lee, chief retail officer at CoVantage Credit Union in Antigo, Wis. “In those moments we want to try and be alongside Sam, or ahead of him, and send him some sort of indicator that says ‘hey, we’re here for you.’”
Data analytics has become a key strategy for financial institutions. McKinsey & Co. noted that 90% percent of the world’s top 50 banks are using advanced analytics.
It won’t likely be long before your competitor down the street soon will be using it as well—if it’s not already.