CRM is the Relationship Lighthouse

While the way we transact has evolved, relationships are still at the core of banking.

Over 2,000 years ago, the Romans built lighthouses in Egypt to warn ships of dangers and to keep ships safe, allowing sailors to navigate treacherous waters and carry out their mission. Today’s banking lighthouse is the CRM platform.

If there is one takeaway from 2020, it’s the need to recognize relationship warning signals and opportunities that require personalized service. That’s hard to do without the ability to understand the customer relationship, respond to customer needs and support their financial goals on an enterprise-wide level.

Customer relationship management (CRM) captures conversations, monitors transaction activity, and displays actionable data so any staff member can connect with customers using relevant and personal communications. CRM utilizes customer data to build more engaged relationships through an integrated journey.

Financial institutions that incorporate CRM into their service develop stickier relationships with their customers. Banks leveraging CRM will experience an increase in sales. But this isn’t about dollars; it’s about an increase in connection. Better service leads to a stronger relationship.

“One of the key benchmarks for all financial institutions should not simply be to gain a deeper relationship with its customer base, but to position themselves as the ’go to‘ solution provider for all of the customers’ financial needs,” says Bill LaVigne, COO at The Bank of Elk River in Elk River, MN. “To accomplish this, you must present yourself at the right moment in time with the right solutions and the right advice, building trust and confidence. An effective CRM system provides the platform to deliver those opportunities. The rest is on you to execute.”

Most CRM systems don’t account for the nuances of the financial services industry. Generic CRMs leverage information on age, ethnicity, and gender without fear of compliance landmines. The situation is quite different for banks.

With the input of industry experts, Marquis created CallTrax NEXT, a CRM that uses terms tailored to the financial industry. This unique CRM integrates service, sales, and marketing automation with organized, actionable data. Marquis also helps to assess bank sales and service processes to customize a system that enhances both.

Given the rocky waters of 2020 and the unknown of 2021, you need to consider how to safely sail through the upcoming year. Let a CRM platform be your lighthouse and assist your customers in navigating their financial ship.

 

RYAN HOUSEFIELD is Senior Vice President of Sales at Marquis.

What Works? An Analysis of Campaign Results and Best Practices – Part 1 Featuring Marquis’ CMO, Dr. Tony Rizzo

 

Video Transcription 

What Works? An Analysis of Campaign Results and Best Practices.

Part 1: The Theories Behind Marketing Automation

Dr. Tony Rizzo, CMO, Marquis

One of the questions I get asked frequently is, “What works?” So taking that question to heart, we executed a very extensive analysis of campaign performance throughout campaigns that we managed and produced over 2019. I’m going to share those results with you today.

For those of you that are homeschooling in and amongst the pandemic, bring the kids in the room, we’re going to give them a quick psychology lesson.

Direct marketing needs to accomplish two things. One, it has to capture attention and two, it can’t manipulate. There’s two theories at work. The first is called the Capacity Theory of Attention. And the Capacity Theory of Attention is simply this: We have a limited bandwidth in terms of our subconscious ability to process information. Only those things that are familiar to us tend to break through that filter so we can move from the subconscious to the conscious level of cognition. This is why we do things like personalization. We make things more familiar to that customer/member to open up that filter, so the offer can move to a higher level of process.

The next is called the Psychological Reactance Theory. And for anyone that’s ever had kids, or been a kid themselves, you’ve been told not to do something, “Don’t touch the stove!” and you touch the stove. Here’s why that happens. We are creatures of free will. And if someone tells us not to do something, our gut instinct is to do the exact opposite. From a marketing perspective, if someone senses that you are using data to manipulate them, they will affirm their autonomy in doing the exact opposite. We have to run a balance of creating things that are familiar to a consumer while not manipulating him or her as to create an environment where “I am open to processing your message.” Those two theories are the foundation of everything.

Our study was the sample size that you see here on the screen, a pretty extensive look in terms of the number of campaigns that we executed throughout 2019. Globally or strategically, we approach marketing with something called Predisposition of Response. What does that mean? It means the campaigns that you’re going to look at, if it’s a campaign, a one-time event, for example, there are over 20 different filters that went into putting the target audience together. Could have been geographic, psychographic, demographic, balances. Could be exclusions, delinquencies. Could be tenure. All of those things, all of these different attributes, went into building a campaign profile.

Why? To get that Predisposition of Response. Not everyone. I’m looking for the one.

Now, from a marketing automation standpoint, on average, 30 different filters were put in place to build or to get to that audience of one. The heavy lifting with a lot of this work, and work in your campaigns, is done on the front end. I’m trying to capture attention. I’m trying not to manipulate. One of the ways that I can do that is by filtering appropriately. All of the campaigns that you’re looking at have this Predisposition of Response and this maniacal focus on the target market, in order to get to that look-alike profile.

Now, if we do that, when we do that, it leads to an increased response rate. In particular, I’m going to speak to the power of marketing automation. Because one of the things that I’ve really questioned is, “Is marketing automation worth the effort?” It’s a lot of effort to do daily marketing, right? All the way from the front end of segmentation to the back end of production. A lot of work. Is the juice worth the squeeze?

We’re going to show you that it is.

What also leads to increased response rates across the segment of campaigns that we looked at, over 400 campaigns, was, again, maniacal focus on brand. If you put your marketing materials on a table and look at them from a direct marketing standpoint across the board, and they look boring to you, you’re doing the right thing. You are focusing on your brand. Why is that important? Remember, we talked about capturing attention and not manipulating? That Capacity Theory of Attention basically tells me I only have a limited brand bandwidth, right? So if your stuff doesn’t look consistent, psychologically, people are going to ignore it. Not that they want to ignore it, but they will ignore it. So brand consistency, across the board, played into higher response rates.

Now, from a data perspective, we have seen the number of data sources we use to put together our Predisposition of Response explode over the past several years. A lot more data sources are being added – transactional data. It could be from a credit card. It could be from ACH, we’ve seen a lot of that come into the system. A lot more demographics. A lot more psychographics. A lot more geographics. So all of that is coming to fruition as we look to do a better job of segmentation. So practically, what does that look like?

You’re looking at a profile of 100,000 Home Equity users. Primary indicators of Home Equity skewed around several different elements; invitation to apply ITA, income, net worth, loan-to-value, the year the home was built. These were primary indicators of propensity to own a home equity loan.

Secondarily, we have some propensity models. On a scale of one to 100, were you in the market for a home equity loan? On a scale of one to 20, were you in the market to refinance your home equity loan? Were you a mail order buyer? Did you have children? What was your tenure? It’s those things (remember, we talked about the other 20 or 30 different filters) that go into creating this funnel. Well, this is what it practically looks like. These are things that we think through in order to break through the barrier of offer resistance.

Across the board, globally, I’m going to share three numbers. The first is $5. For every dollar invested in our approach, this Predisposition of Response, we generated $5 in profit on the back end. I’ll take that bet any day. The second number is 5%. This is representative of the number of consumers that we mail to, on average. So not big numbers, right? These are small numbers. The last one is 13%. And this is our average combined response rate compared to an average DMA number of 9%. We do much better than the national benchmark. Again, working on this Predisposition of Response, using our data to focus on most likely buyers.

We categorized the results across a number of different categories. The first was categorically channel, right? We did direct mail only, email only, and then a mix of email and direct mail. We categorized across campaign type, be it preapproval, reboarding, onboarding, prospecting … we categorized it that way so we could look at the data.

Two big key findings that we found in the study: the first was that if we use direct mail and email together, your balances go up by 2x in almost every case. The second finding that we found, when we use time personalization, also known as marketing automation, your performance, overall balance performance, response performance, goes up by 4x.

So there is something to marketing automation, there is something to recognizing an event in the consumer’s life with you and doing something with it that creates a lift. It creates greater receptivity. It creates better response.