Update 8/18/16: During a recent National CU Foundation Twitter chat on financial wellness, I posed a question about how these credit unions are able to locate members who may not realize they need help. Or not even know help is available…from anywhere. Coastal FCU (Raleigh, NC, $2.6B) responded with: “A lot of it is data analysis. You look for indicators and warning signs in your member activity.” They’re using the same concepts of Big Data which I discuss below, but for an amazing purpose: Helping their members who may not even know they need (or can get) help! To paraphrase the end of this post, it sure sounds like they’re “maximizing the connection to their members” and “delivering a higher standard of service” for all of them! How do you use data analysis to help your members?
Originally published on CUInsight.com
I know what you did last summer.
It’s funny to think how just a few years back, this was a terrifying proposition. “Oh no, our stalker knows!” Now, your stalker is any given friend who checked your Facebook profile. Or, more true to our topic…Facebook’s algorithms showed me your activities proactively. That hiking trip you took triggered markers that are similar between us. Facebook knew I would enjoy seeing your vacation photos.
This is one direct application of Big Data. It’s a topic so complicated that I could spend an entire year of posts delving into the principles. But I won’t (you’re welcome!). Instead, I’m going to try to offer some examples so you can understand the opportunities it presents.
By keeping a digital eye on everything I post, share, like, and comment upon, Facebook’s “Big Data” engine learns my preferences. Maybe I tend to Like many things one friend shares. That’s an easy conclusion; show me everything from that friend, because I’ll probably enjoy seeing it. What about that friend I haven’t spoken to in years, yet shares photos from Machu Picchu? Facebook knows I was there last year, so it’s likely I’ll be interested in seeing their adventure in the same place. More so, I always seem to share/comment on posts regarding ocean conservation. From what I say, Facebook knows I’m interested in environmental responsibility, so I’m going to see more of what friends say regarding this topic.
These examples are rather pedestrian. See a pattern, follow to result. Yet Big Data gets interesting when it starts drawing conclusions. Facebook can determine your political stance, religious beliefs (intensity or lack thereof), income level, and other character traits with stunning accuracy. It can figure out your emotional state, both short and long-term. This is why you get those anti-depressant/counseling ads when you’re feeling your worst and wedding planning ads after you’ve been in a rewarding relationship. A popular example of this predictive capability was when a department store sent a woman a baby catalog before she even knew she was pregnant.
Big Data isn’t just about having thousands of lines in a spreadsheet. It’s the ability to take all this data and gain valuable insights which can be put towards a better product/service offering. Like your member programs.
Too many credit unions I speak to have little to no tracking, no matter the program. I’m not talking Artificial Intelligence level analysis, just a few numbers to know what’s happening. Are your initiatives successful with the members you had hoped? What percentage of website visits results in a loan application? How do you compare to other institutions in performance metrics? And so on.
I’m a huge fan of data analysis because there is just so much information hidden within even the smallest datasets. Here’s an example from my business, starting with these data points:
- # of member prospects
- # unique visitors to website
- # of sales/loans
- # of members at credit union
From these four values, I can determine the following (with comparisons to all other credit unions for each):
- Sales ratio
- Unique visitors per 1,000 members
- Prospects per 1,000 members
- Prospect close rate on a rolling 60-day period (along with average close rate among all credit unions)
- Sales per 1,000 members
- And I can generate even more on demand
With access to your basic member information, you can easily determine dozens of behaviors and trends. Take your auto loans. Do members living in a certain ZIP code prefer Toyotas? Or do some ZIP codes have higher used car purchase ratios? You may even find that members with last names shorter than 5 letters, who also have your credit card and a CD, buy vehicles with a 10% higher down payment average than other groups. How can your marketing team take advantage of this new information? Sure, that’s a silly dataset, but it shows how much you can dig into what you already have.
Do all of your services meet the Big Data Test? Take the average member referral program. If you’re like the vast majority of credit unions, it requires a member and friend print out a PDF, fill it out by hand, then fax or bring it into a branch. Test results: Fail.
At the end of the day, embracing the ideas of Big Data helps to maximize the connection with your members, thus, delivering a higher standard of service at the lowest effort and cost. And isn’t that the goal of your credit union?