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Fintech Whiteboard Episode 1: Data Analytics In a Nutshell

Welcome to Fintech Whiteboard with John Best! This week, join John as he explains the key aspects of data analytics and how to implement it in your organization.

Fintech Whiteboard Episode 1: Data Analytics in a Nutshell

Hello everyone, and welcome to this edition, actually the first edition, of Fintech Whiteboard. And so, we’re going to try to understand exactly how data analytics works, and I have a very simple way of looking at it. So, hopefully this will help you and your team understand, and how to implement it, and kind of what you’re looking for, and also some of the key pieces of it.

Data Analytics in a Nutshell.

To understand Data Analytics, we’ve got to start at the beginning. The first thing that you have to talk about is the data.

You’ve got data all over your credit union. The data is everywhere. You have data in your core. You have data possibly in credit cards. You have data possibly in a mortgage system. There’s data in home banking. There’s data in mobile. There’s data basically everywhere. Your first step on your Data Analytics journey is to corral all that data.

A lot of times you’ll hear it called a data warehouse. There’s a lot of different names for it, but somehow it’s got to get all this data so that you can sync it up in one nice little database where it’s all correlated. Just so you understand why this is difficult or because it doesn’t seem that difficult when I draw it out, it is because over here, you’ll have some duplicate data.

So, in the credit card system, that’s on your core. They may call it due date. That might be the way they term when the date of your credit card is due, but over here, they may call it next pay or something along those lines. You’ve got all this data you need to normalize, collect, and get it into this data warehouse. That’s Step One.

Now, assuming that you get step one done, and oh, by the way, this is where a data governance committee will come in. Why you need a data governance committee, is because these people are the ones that have to sort of figure out things like this. They’re also the people who when you get a new system, so let’s say you add something new like voice, and it has specific data that you can use, they’re the ones that decide how that fits into the data warehouse. So, that’s Step One, your data warehouse.

Now, let’s go on to Step Two.

The next thing that happens is, you’re going to find that there are all kinds of people who have algorithms. What is an algorithm? An algorithm is basically a formula that can be applied to the data, right? Plus, the algorithm is going to equal some outcome. For instance, this algorithm may help you find all of the people who would be interested in a new car, and it may use data from lots of different sources, and may use data from your auto lending, it may use data from Facebook, maybe you’ve purchased some Facebook data.

However, somehow it’s going to, you’re going to take the data, you’re going to apply this algorithm, probably from a vendor or somebody, and it’s going to spit out a file for you to work with. This file is usually going to be a list of names, emails, something that you can use to identify the members and possibly some extra data, like maybe the rate that might be attractive to them, or who knows what, but it’s going to spit out most times a file.

Now, once you have that, you’ve got your data, you’ve got your algorithms, and now you have your output, the next step is what to do with this file. That’s where things get interesting. Most of the time, when we want to work on this, we get this file. There’s a couple of different ways that we could deal with it. We’ve got to tell this member that, you know, they can get whatever this offer is. Some people react well to email, right? So, you may want to do an email advertisement and you need a system to do that.

Some people may be ok with seeing a banner in your home banking or your mobile, right? You may want to send it to it that way. You may want to send them, or put a banner out, and you may want to customize that banner. You want to make sure that it’s only reaching the people that care because this was a very custom list. Some people may be ok with just old fashioned snail mail, so you may just want to send them an advertisement, or put it in their statement, or whatever you need to do there.

You may want to call them. You may have such a good deal that it’s worth an outbound phone call, right? However you do it, you have to have good data. You have to have their email address. You might need their phone number. Oh, by the way, you could also SMS them this deal. You could send a text. There are lots of different ways. Now. my question to you is: Do you know how someone wants to be contacted? Do you have the data to support it?

If you know how someone wants to interact, you can easily get their attention. You may think, ok good, I’ve got their attention, but now what? Well, this is where things get really interesting.

You’ve got your data. You’ve got your algorithms. Now you have your offers and your marketing, basically. However you are going to get this data to them, and remember, the key here is that this is very targeted data, so we’re looking to go ahead and find this person directly. It’s very individualized data. Let’s say that you’ve got all this way and suddenly you now have to do fulfillment.

Let’s say that they’re interested in this car rate that you’ve just offered them. You know, they they logged into home banking, they saw a fantastic banner that said, “You could get a 0% loan because you have insurance with us,” or whatever it is, and this member is very, very, excited. He wants to take you up on it and get moving. He can’t wait to go get a new car. So, he clicks on the banner, and once he clicks on the banner, what happens?

Well, that’s the interesting part, right? Nine times out of ten, what I’ve seen at most credit unions is that it’s going to go to a form, and the form is just going to look like any other email form that you can see. That form is just going to ask some information and it’s going to say, click, submit and, someone will get back to you. The problem is, when we when we do that, these people are pretty savvy these days, and they’re not happy because what they wanted was to click a few buttons and have something in their hand that would allow them to go buy their new car tomorrow. This section is fulfillment and it’s a problem because you’ve got to sort of master all of these sections. If we look at this line, Step One, you’ve got to get your data.

Then Step Two, you’re going to have algorithms. They may come from a lot of different places. They’re not always going to be offers. Sometimes they might be dashboards to tell you what to do with rates, or what to do with a certain mortgage, or here is some accounts to look into, but I would say a lot of times, they’re going to be offers because the offers are what’s going to drive the margin. Getting the the right person, and you can think of it like this, we’re going to get the right person, the right deal or the right offer, at the right time, on the right channel, and we’re going to make it easy.

One click. That’s what people are looking for. Like we said, I understand that not everybody can do the one click thing, because if it were super easy, everyone would be doing it, but this is the essence of digital.

What does this mean when we chase it down? I’ve got Step Three: getting the offer out to them, and then Step Four, which is fulfillment. What does this mean for you?

It means that you have to make sure, Number One, that your contact information for your members is up to date. Two, you’ve got to make sure that you have a digital marketing capability. Three, you’ve got to make sure that you have a home banking, mobile, whatever these digital channels are, that you have a way to provide digital fulfillment.

Finally, you have to trust your data. That’s why it all starts right here, over in the data warehouse, and that’s why you need a data governance committee, and that’s why this is not easy stuff, and everybody struggles with it. That’s the key. This is the continuum. Data Analytics in a Nutshell.

If you have any questions, I’d love to hear from you. We do this kind of work for a lot of folks, so if you’re interested, please go to big-fintech.com and you can talk to us there, or email me at jb at big-cu.com, or if you’re looking for me on the old Twitters, you can find me @JBFintech.

This has been a Fintech Whiteboard and Data Analytics in a Nutshell.

Thanks, everyone.

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