<img height="1" width="1" style="display:none" src="https://www.facebook.com/tr?id=142903126066768&amp;ev=PageView&amp;noscript=1">

Steven Ramirez, CEO at Beyond the Arc, on Predictive Analytics

Screen Shot 2017-09-20 at 1.53.11 PM.png

This week we feature Steven Ramirez of Beyond the Arc for a discussion on predictive analytics. Ramirez details exactly why this topic matters and gives practical suggestions on how financial institutions can make it work for them. You can watch the video and read the transcript below.

Why do you think predictive analytics is so essential for banks and credit unions?

I think that we’re at an unprecedented time in terms of the importance of focusing on the customer. It really is about meeting customer needs and there’s no better way to understand those needs than by using advanced analytics like predictive analytics.

When does predictive analytics work well?

When it works well it’s the ability to find out that a customer has a particular issue before it leads to them leaving the bank. Or if you’re looking at your marketing campaigns to be able to spend less money but have the same or better impact. Those are some of the pay offs of predictive analytics.

It may be a factor of looking at things, by your first guess there are certain indicators that you would be looking for, right? If people seem dissatisfied in this survey, that would be a really obvious way. By the time that someone is saying they are dissatisfied it’s usually far too late in the relationship and so by using an analytics we can start to see some of the early warning signals. So for example, it might be a pattern of declining balances that start to identify that a move is likely. And with predictive analytics we can really take that down to the individual and so we can score every single customer that you currently have out of 50 million customers and then identify exactly which ones are at the greatest risk of leaving.

You want to be able to hold on to customers. But predictive analytics is also a great driver for upsell and for cross-sell. It really comes down to understanding a customer and then finding out who is most likely to accept, for example, a cross-sell offer.

What would that look like in practice?

If you’re leading a marketing campaign, right now the toughest thing to figure out is just how much email can you send? How much direct mail? There is such a point of saturation. And so rather than just continue to carpet bomb everyone, if you could really target and know that out of your population for this particular offer that 38% of the population are likely to respond, wouldn’t it be great if you could actually just target that 38% directly and leave out the rest and that they don’t have to see that solicitation. That is what predictive analytics allows you to do.

One of the things that has happened with predictive analytics is that it’s much more democratized. 10 or 20 years ago it was only the largest banks and institutions that could do this and could do it well, but now with new tools really a smart business analyst, you don’t need to be a PHD, you don’t have to have advanced degrees in statistics, a smart analyst working on a laptop with access to data from the organization can build effective predictive models. And we’re seeing that now more. So now the midsized, small, any type of organization can now get access and use this effectively.

What’s the difference between those who are on the leading edge and those who aren’t?

The key thing I think as with many types of analytics is, do you take that data and act on it? It is really that bridge to action and that is the key success factor. It is not about having the smartest analyst and it certainly isn’t about having the most amount of data. It really is being able to have a process that takes those insights and helps you drive them into the business.

I think one of the things that we’re hearing a lot about now is about artificial intelligence. It’s the ability to now not only predict, but really to interact based on those predictions. Well that artificial intelligence is based on machine learning and predictive analytics. These are sort of the DNA that feed these artificial intelligence systems. So as you’re hearing more about AI, really what you’re hearing about is an increasing sophistication and power behind predictive analytics, and I think that’s really exciting.

Banker's Guide to Big Data