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.
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.