Data Mining to Identify New Markets (1,176 words)
December 2000
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What's a financial institution to do when it has reached a plateau in its current market?
That was the situation Dallas Teachers Credit Union (DTCU) found itself in last year when it realized it had little room for growth. But its status was even more critical. It knew it might never expand much beyond its current 147,000 members, or at most its projected top level of 250,000 members, without a change to its charter. You see, the credit union was chartered as an occupational group in 1931 and could not offer its services outside the education market.
"We had hit the ceiling of that market," says Jerry Thompson, DTCU's senior vice president and chief information officer. "So we needed to go back to the State of Texas in January or February to request a change to our charter from occupational-based to community-based."
Opening the Door to a New Market
The DTCU needed to present a strong case before the Texas Credit Union Department to convince it of the need for an upgrade in its membership charter. Today, regardless of a bank or credit union's size, data mining technologies can help to identify new markets—both in terms of geographic area and demographics. Thompson says the credit union used information housed in its IBM EZMart data warehouse to make its case. "We went into the warehouse and looked at transactions using ArcView Business Analyst" software from ESRI, a developer of geographic information system software.
"Looking at our existing base showed excess capacity and the fact that we had a lot of potential in underserved areas," Thompson notes.
Usually one would look at geographic area in five-mile circles around the branches, but this would lead to gaps where no branches exist. Some innovative thinking led to the idea to use school districts as the basis of the geographic database build so there'd be no gaps. "With very few restrictions, every area has to be served by a school," Thompson explains.
"We did a three-year build on the file to reveal a potential field of 2.7 million in the geographic area. This included a combined 40 school districts in the 10-county Dallas area," he says.
The DTCU was granted its new charter on June 1 of this year.
Mapping Its Members
Geographic analysis of its data warehouse has been instrumental in helping DTCU in other ways, for instance to visually represent its member base within the surrounding communities. "The most exciting thing about all this is we can take the top 10 percent most profitable members and spatially map them across the field of the membership area," Thompson explains, adding "this is where the money comes from. These are the branches, the ATMs, they use."
Another interesting fact the DTCU learned is that it has almost 100-percent checking account penetration within a seven-minute drive of branch location. "Then it drops off dramatically. The real interesting thing here is that it's based on time, not distance. It shows that people want to be near a branch—whether they visit it or not. This is the reason the dot-coms are having so much trouble," asserts Thompson.
He adds, "Now we've taken this, overlayed it with Acxiom data and profiled with lifestyles, and Acxiom gives us back these clusters so we can decide where to build future branches."
As its member base grows, the Dallas Teachers Credit Union averages two new locations per year. More factors come into play than just the number of members when building a new branch. Sometimes the new branch needs to alleviate high traffic at an existing branch.
"We just opened a new branch in a suburb that is relatively close to a branch that was overloaded," Thompson says. "We wanted the drainoff."
One-To-One Marketing
By mining the data in its warehouse, DTCU was also able to learn some interesting things about its existing customers, such as the predictability of bankruptcies. "We looked at the data warehouse to find what about bankruptcies was predictive and then could track those individuals more closely," Thompson explains. This is important, since part of DTCU's charter as a credit union is to serve the community.
If it started to look like a problem was developing for a certain member, DTCU could offer individual counseling services in-house, refer them to an outside agency or offer alternate payment plans.
"Another way we're using the data is running a cross-sell model with part of the IBM solution," adds Thompson. Through EZMart, DTCU has been able to run segmentation models on its customer base to see which segments should receive specific promotions.
Predictive modeling also helps determine in what sequence people get certain offers, based on answers to questions such as, "What products do people get initially"—say a checking account, an auto loan or a credit card? Adds Thompson, "We have 107 scenarios in test right now. It's a very complicated algorithm, but with great potential."
To help it in cross-selling certificates of deposit, individual retirement accounts and other investment vehicles, DTCU is using Unica Corp.'s Affinium predictive modeling suite to determine which product information and promotions are most likely to meet members' needs.
The next phase of the program implementation will allow DTCU to offer truly customized one-to-one service to its members. "IBM's e-commerce folks will be in soon to talk to our call center reps about how to use this system," explains Thompson. "Basically, what will be able to happen is when a customer calls in, and his or her number and information pops up on screen, a cross-sell screen with relevant product offers will pop up, too."
As an example, Thompson says, "If a client's auto loan is nearly paid off, we'll know that he had a Chevy Suburban and could work out a promotional arrangement with a local Chevy dealer."
In the future, Dallas Teachers Credit Union also plans to use demographics and traffic patterns to determine the placement of billboards and ATMs.
The Price of Results
None of this technology comes cheap, of course. Thompson admits it all would have been out of reach for a smaller financial institution like DTCU without exceptional circumstances: The organization had agreed to be the test site for the IBM pilot program.
Was it worth being the guinea pigs? Thompson says certainly it was, though there are always drawbacks to being first. "IBM gets to template from this and use it for future roll outs. So other companies will benefit from all the time and effort we put into learning the systems," he points out. "We benefited because our financial commitment was not as great as you might expect. We could not have afforded it. And IBM provided a lot of support." Thompson jokes, "We're on the larger side for a credit union, but we're no Bank of America. Where they might have 600 programmers they could pull from, I have a total staff of 15 with two programmers. Here we all have our normal day-to-day jobs of running the financial institution in addition to working out the kinks in this new system."
That was the situation Dallas Teachers Credit Union (DTCU) found itself in last year when it realized it had little room for growth. But its status was even more critical. It knew it might never expand much beyond its current 147,000 members, or at most its projected top level of 250,000 members, without a change to its charter. You see, the credit union was chartered as an occupational group in 1931 and could not offer its services outside the education market.
"We had hit the ceiling of that market," says Jerry Thompson, DTCU's senior vice president and chief information officer. "So we needed to go back to the State of Texas in January or February to request a change to our charter from occupational-based to community-based."
Opening the Door to a New Market
The DTCU needed to present a strong case before the Texas Credit Union Department to convince it of the need for an upgrade in its membership charter. Today, regardless of a bank or credit union's size, data mining technologies can help to identify new markets—both in terms of geographic area and demographics. Thompson says the credit union used information housed in its IBM EZMart data warehouse to make its case. "We went into the warehouse and looked at transactions using ArcView Business Analyst" software from ESRI, a developer of geographic information system software.
"Looking at our existing base showed excess capacity and the fact that we had a lot of potential in underserved areas," Thompson notes.
Usually one would look at geographic area in five-mile circles around the branches, but this would lead to gaps where no branches exist. Some innovative thinking led to the idea to use school districts as the basis of the geographic database build so there'd be no gaps. "With very few restrictions, every area has to be served by a school," Thompson explains.
"We did a three-year build on the file to reveal a potential field of 2.7 million in the geographic area. This included a combined 40 school districts in the 10-county Dallas area," he says.
The DTCU was granted its new charter on June 1 of this year.
Mapping Its Members
Geographic analysis of its data warehouse has been instrumental in helping DTCU in other ways, for instance to visually represent its member base within the surrounding communities. "The most exciting thing about all this is we can take the top 10 percent most profitable members and spatially map them across the field of the membership area," Thompson explains, adding "this is where the money comes from. These are the branches, the ATMs, they use."
Another interesting fact the DTCU learned is that it has almost 100-percent checking account penetration within a seven-minute drive of branch location. "Then it drops off dramatically. The real interesting thing here is that it's based on time, not distance. It shows that people want to be near a branch—whether they visit it or not. This is the reason the dot-coms are having so much trouble," asserts Thompson.
He adds, "Now we've taken this, overlayed it with Acxiom data and profiled with lifestyles, and Acxiom gives us back these clusters so we can decide where to build future branches."
As its member base grows, the Dallas Teachers Credit Union averages two new locations per year. More factors come into play than just the number of members when building a new branch. Sometimes the new branch needs to alleviate high traffic at an existing branch.
"We just opened a new branch in a suburb that is relatively close to a branch that was overloaded," Thompson says. "We wanted the drainoff."
One-To-One Marketing
By mining the data in its warehouse, DTCU was also able to learn some interesting things about its existing customers, such as the predictability of bankruptcies. "We looked at the data warehouse to find what about bankruptcies was predictive and then could track those individuals more closely," Thompson explains. This is important, since part of DTCU's charter as a credit union is to serve the community.
If it started to look like a problem was developing for a certain member, DTCU could offer individual counseling services in-house, refer them to an outside agency or offer alternate payment plans.
"Another way we're using the data is running a cross-sell model with part of the IBM solution," adds Thompson. Through EZMart, DTCU has been able to run segmentation models on its customer base to see which segments should receive specific promotions.
Predictive modeling also helps determine in what sequence people get certain offers, based on answers to questions such as, "What products do people get initially"—say a checking account, an auto loan or a credit card? Adds Thompson, "We have 107 scenarios in test right now. It's a very complicated algorithm, but with great potential."
To help it in cross-selling certificates of deposit, individual retirement accounts and other investment vehicles, DTCU is using Unica Corp.'s Affinium predictive modeling suite to determine which product information and promotions are most likely to meet members' needs.
The next phase of the program implementation will allow DTCU to offer truly customized one-to-one service to its members. "IBM's e-commerce folks will be in soon to talk to our call center reps about how to use this system," explains Thompson. "Basically, what will be able to happen is when a customer calls in, and his or her number and information pops up on screen, a cross-sell screen with relevant product offers will pop up, too."
As an example, Thompson says, "If a client's auto loan is nearly paid off, we'll know that he had a Chevy Suburban and could work out a promotional arrangement with a local Chevy dealer."
In the future, Dallas Teachers Credit Union also plans to use demographics and traffic patterns to determine the placement of billboards and ATMs.
The Price of Results
None of this technology comes cheap, of course. Thompson admits it all would have been out of reach for a smaller financial institution like DTCU without exceptional circumstances: The organization had agreed to be the test site for the IBM pilot program.
Was it worth being the guinea pigs? Thompson says certainly it was, though there are always drawbacks to being first. "IBM gets to template from this and use it for future roll outs. So other companies will benefit from all the time and effort we put into learning the systems," he points out. "We benefited because our financial commitment was not as great as you might expect. We could not have afforded it. And IBM provided a lot of support." Thompson jokes, "We're on the larger side for a credit union, but we're no Bank of America. Where they might have 600 programmers they could pull from, I have a total staff of 15 with two programmers. Here we all have our normal day-to-day jobs of running the financial institution in addition to working out the kinks in this new system."
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