October 19, 2009
Founded in 1935 by 25 Boeing employees, BECU
(Boeing Employees' Credit Union) is the fourth largest credit union in
the country based on asset size ($8.5 billion). BECU now has
approximately 1,000 employees and over 625,000 members, predominantly
located in the Puget Sound area. There are two major financial centers
in Washington state along with a network of more than 45 cashless
neighborhood financial centers. BECU is very member-focused and
provides a full suite of financial services, including savings
products, mortgages, consumer loans and wealth management services.
BECU required a better perspective on member identities to achieve more
effective market and member growth strategies. It needed member data to
be centralized into a single repository providing data access to all
authorized employees. BECU had a wealth of information to record,
including the daily changes to member accounts, and staff needed access
to those data for analysis in real-time.
Starting in 1999, BECU began collecting data into a single repository
so that employees could get a complete view of each member, resulting
in significant time savings and unparalleled member service. Using the
Sagent Data Flow warehouse and query engine from Pitney Bowes Business
Insight (PBBI), the repository took a year to initialize and during the
past 10 years it has continued to mature into a robust data base.
Two groups of Sagent Data Flow users emerged after implementation,
including 300 Web link users out in the field who are interested in
knowing the immediate status of branch production and specific member
information. They continually access this information over the Web by
running reports created by in-house analysts. An additional 80 users do
intensive analytical research to gain valuable insight into member
data, such as how well BECU markets a particular product to target
members, which ATMs members are using, and where members are conducting
their transactions. Analysts have a complete view of member activity
through the power of Data Flow to query member behavior and manipulate
the data to create meaningful reports.
Business intelligence also makes BECU more nimble at managing
transactional risk under a three-year-old member access segmentation
program. Through this program members are automatically qualified to
access and withdraw more than the standard amount of cash from an ATM
and they are granted higher provisional credit limits on check
deposits. Normally, a teller would grant such extra access. Since
BECU's operating model has many locations without tellers, automating
the process that grants this additional access is necessary.
BECU categorizes members into risk levels using a regression based
scoring model built from business intelligence data. The risk levels
determine who can receive extra access without unmanageable added risk.
BECU continually tracks withdrawal access level usage for each member
to monitor its value to members. The vast majority of the membership is
low risk and now enjoys higher levels of access to withdraw funds. With
this technology, BECU can provide better service to members who do not
have easy access to tellers.
In 2007, BECU easily integrated site location intelligence into its
suite of analytic tools. The AnySite site analysis and decision support
solution from PBBI added a location based component to the company's
existing business intelligence strategy. Designed to visually display
member concentration and product usage by location, AnySite
incorporates data generated from Sagent Data Flow, which is used to
analyze the relationship between BECU's branch site performance and
trade area characteristics.
When BECU considers adding a new branch or ATM, business intelligence
helps the company make better decisions for site location. The AnySite
solution reports the number of members, their usage of BECU products
and the general population demand for financial services around any
specified location. The program plots this information against a
current branch or ATM site's performance to better predict whether a
potential branch or ATM should succeed at the location under
consideration. It used to take days to crudely and laboriously estimate
member and population demand based on insufficient relevant demographic
data by ZIP Code. Now BECU can precisely define a location's geographic
market area by using AnySite and, within an hour, produce a wealth of
data relevant to the area's demand for financial services.
BECU conducts regression studies to see what market characteristics
apply to a successful or unsuccessful branch. Locating a branch in an
area with an existing member base offers a much better start because
much of the company's new business is driven by referrals from existing
members. The population density, distance to the site, and consumer
demand for deposits and loans also correlate to the success of a site.
To date, BECU has used the technology as a valuable tool when making
decisions on where to open or re-locate over 15 branch locations.
Prior to using AnySite, BECU knew there were $100 billion in deposits
by financial institutions in western Washington state, reported by bank
branch location. It didn't know where the individual customers were
located. AnySite includes data on consumer demand for loans and
deposits based on where consumers both live and work. These data
greatly help to pinpoint the areas that need more effective marketing
or even a new branch, with the ultimate goal being to increase the
volume of deposits and loans by acquiring new members and better
serving existing members.
BECU has used Sagent Data Flow in conjunction with AnySite for several
mission-critical projects. For example, the board asked if the credit
union was close to reaching a saturation point with member penetration
in the Puget Sound area. The board was concerned about whether
potential saturation would soon impede the historical growth rate - a
10 percent increase year over year.
Information from Sagent Data Flow, including member addresses along
with when they joined BECU, allowed AnySite to locate each member
relative to retail locations. AnySite was able to define trade areas
around all BECU retail locations and determine the penetration rate for
each of these specific micro-markets. The company could then clearly
see new member growth over the past three years by market, as well as a
prevalent trend for more rapid growth in the markets with a higher BECU
market share of consumers. This suggested that there was room to grow
at a consistent rate for the near future before reaching a plateau.
When it comes to BECU's marketing efforts, PBBI is the source of
information to both select prospects for promotions and measure the
results. Sagent Data Flow pulls from a myriad of source systems at the
credit union, including the core transaction system, loan origination,
member access usage and third-party service providers.
BECU merges member specific data on usage of BECU products and services
with block group level population information from AnySite to create a
host of potential predictors for how likely members are to use a
particular financial product. These data are exported to a statistical
software package. Based on the predictors, members are statistically
"scored" on their likelihood of using the product being promoted.
Members who are unlikely to respond to the promoted product, for
example an offer of credit, are identified and removed from the
preliminary list before further screening for credit worthiness by a
credit bureau. Offers of credit are then sent to qualified members by
direct mail, which includes a unique promotional code identifying the
responders. By entering this unique identifier into the loan tracking
and application process, the offer of credit specific to the member is
verified and the tracking code is automatically transferred to the main
transactional system and swept daily into Data Flow. This completes a
feedback loop where the target is identified and tracked for
effectiveness using Data Flow.
Organizations like BECU are often "drowning in information, but thirsty
for knowledge." By combining location and business intelligence, BECU
has successfully created a system to better leverage its data when
making mission critical decisions and to enhance communications with
customers.
Ed. note: Cal Bierley will participate in a webinar on 10/22/09 in which he will further describe this case study.
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