Socially-Distanced Marketing, Strategy, and The Force

Tag: artificial intelligence

Deeper Connections – Part 3

Originally published on

It’s now been two posts on the “Two Peoples” topic. What have we learned?

  1. There is a technology divide between those who are immersed and those who live more traditionally
  2. The way people interact with the world and each other is evolving
  3. Tech itself is changing beneath our feet (and around us in “the cloud”)

For nearly all of us, the idea of technology in our lives revolves around things mentioned in the first post. What phone you use, the devices you connect to it, even your “smart home” accessories. It’s primarily the hardware, and, as we learned in the second post, the services you use on them. So, that’s the future: Ever-improving devices with more interesting apps.

Not quite. There’s an area of growth which seems so far-fetched that we discount it as “distant future”. But it’s here today.

Artificial intelligence.

We aren’t talking the adorable bots from *batteries not included, nor are we concerned with T-1000 units “terminating” their target. AI (or more accurately, machine learning) today is in some ways like a traffic light. It does one thing. However, unlike a traffic light, it’s always improving how it does that one thing. And you use these 1st generation AI systems everyday.

Your Facebook feed is a form of machine learning. It tailors posts shown based on what it learns you enjoy. The more you use it, the better it gets. Your iPhone keyboard is the same way. It actually adjusts the size and location of each key by tiny amounts as it learns where your fingers press most often. It even figures out how you talk to better predict the next word you’re going to write (and it knows whether you’re typing in a social or professional manner).

Search Google for the image of a cat. You just asked their machine learning system. Their latest endeavor is a platform called Deep Dream (caution: highly geek). Besides trippy imagery, it shows how a computer actually learns. Fascinating, as Data would say.

Interesting, but, once again…why? The first two parts related to what technology you use knowingly. Those spawn the interest in visible tech. Modern app platforms. Game-like member engagement. All great, and important. But it’s the machine learning which will offer the “just what I wanted” capability of future financial services.

Computers are smartest with tons of data. Big Data, you could say. With it, a learning system can figure out when a member is at risk of overdrawing their account or might be in the market for a car. How thrilled would they be if you could suggest adding overdraft protection an hour before they make a costly transaction? Or notify them of a great auto rate and car research system the day after their vehicle has engine troubles?

Unfortunately, I’m not smart enough to even offer the breadth of examples this future will offer. But I’ve read a lot from those who are. A recent CU Broadcast interview dove headfirst into the data side (without mentioning the AI part). Coastal CU does data analysis for member habits. Affinity CU just expressed interest in the concept after a short chat. And that’s just in the course of a few days. Much like winter, change is coming.

So how can you stay ahead of your competition while providing historically-great member service? Focus on what you do best, and find partners which excel in their complementary areas. By working together, perhaps we stand a chance against our computer overlords.

I mean, serve your members in new and exciting ways.

For far too much detail on Artificial Intelligence and just how close we are to unbelievable changes, read this amazing post by Wait But Why’s Tim Urban.

A Geek’s Thoughts on the Election

Today, this country made a decision, but it isn’t a decision in which anyone should be proud. Whether you voted for your candidate or against the other one, or a mixture of both (or went third-party), the conclusion is a troubling result. We’ve said that experience is irrelevant. We’ve said that decorum is unnecessary. We’ve rewarded hateful speech and actions, probably because we share the fears from which they derive. We’ve legitimized a lot more, but it’s not even worth diving into it here.

In my research of machine learning (artificial intelligence), a common theme arose around the idea of logarithmic change. This means that as change (in this case, computer performance and “smarts”) occurs, it occurs at a faster rate than prior. Not only does a system get smarter as it “grows”, but it gets smarter more quickly. Think of it like a car which goes 0-60 in 4 seconds, 60-120 in 3, 120-180 in 1, 180-240 in 0.1, and finally 240-300 in 0.0001 seconds. Once it’s going 500, can you even process that type of acceleration? More importantly, how would you describe the velocity increase at 1,000? If you’re struggling to wrap your brain around it, that’s ok. You’re not alone. We perceive the world linearly, and this is at the core of many challenges.

Our world has been in the midst of this increasing rate of change for all of its history. However, only within the past decade or so has it become so impactful on the average person’s life. Minorities are rapidly becoming the majority, social norms are shifting at an accelerated rate, and the divide between what our knowledge contains and what the average person knows (or even *could* know) is growing exponentially. You could probably describe the basic idea of how your VCR worked. How about your iPhone?

This is why the challenges of today (and tomorrow) are so difficult to reconcile. We think in a linear fashion: Last year was that, this year is such and such, so next year will be a derivative of those. Except this no longer applies. Change accelerated and next year will be something we can hardly imagine.

And neither candidate appeared to grasp this fundamental concept.

This election was an expression of deep-seated fear of the unknown (be it gay marriage, traditional gender roles breaking down, ethnic diversification on a majority scale, expanding capabilities of a surveillance state, and any number of other topics). What many always knew to be true simply isn’t anymore. Like being in an earthquake, people’s “bedrock” is cracking. Anxiety over what an ever-increasingly changing future will bring led Americans to make rash decisions all the way through the election process.

I don’t have any answers. I’m pretty sure our President-elect doesn’t, either. So we’re going to have to work together and figure out how we will move forward while navigating this wildly-accelerating car.

Image credit: Me, seeking inner balance and focus.

Be Your Member’s Favorite Stalk…ahem, Tracker!

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

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?

© 2020 Credit Union Geek

Theme by Anders NorenUp ↑