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Our DataFlex software has many powerful features that allow you to build a wide variety of reports, both graphical and more quantitative.  But what can be evaluated? That’s what I’d like to discuss today.

We’ve expanded our capabilities numerous times over time and continue to add more data points and categories that can be examined. The more areas of investigation that we add to DataFlex, the more details and trends that credit unions can study.

Shares

There is a standard data record format for shares and loans called AIRES that was introduced by NCUA in 1995. This is used for auditing and other advanced equerries. This is the foundation of what we use in DataFlex. If you are already aware of the AIRES format, this will be familiar ground.

The most frequently used share data fields available in DataFlex include: balance, product type, date opened, maturity dates for certificates, dividend rates, date of last activity, and branch. This by itself is fairly straightforward, but DataFlex empowers the user to create filters and categorize the data even more. When the share data is imported, each product is automatically assigned a product category such as Savings, Checking, Certificates, IRAs, Money Market Accounts, etc. These can be customized for each credit union, and even more fine-grained grouping is available on individual reports and dashboards. Select a range of balances or dividend rates. Organize the free and rewards checking accounts separately and compare the members to look for trends.

Loans

Everything I just described for the share data holds true for the loan data as well. Commonly used loan data fields include: balance, product type, credit score, date granted, payment amount, term, payoff date, original loan amount, accrued interest, credit limit, APR, and delinquency flags. These can all be grouped and filtered as well. At this point you might not think looking at a static snapshot of the loan data is very interesting. That is why we import data on a monthly basis. When imported monthly, the trends over time are tracked and focused upon.

Member Data

In addition to the expected address information, we also utilize data fields for e-mail addresses, phone numbers (home, mobile, work), and contact preferences. Birthdate, age, credit score, member acquisition date are some other fields. When we import member names, we also use special parsing software to separate the names out into first, middle, and last names. This enables us to standardize the name format and by splitting the name up into its component parts, targeted marketing can be addressed using the recipient’s first name.

We also track services/products that members may have such as: ATM or Debit cards, debit rewards, online bill pay, credit insurance, investment services, direct deposit, e-statements, home banking, and mobile banking. Members can also be categorized according to employee/non-employee status, SEG groups, and other custom groupings.

Householding

While it can be very useful to examine individual members, sometimes looking at the household as a whole can be more beneficial. When members’ addresses are imported into DataFlex, they are standardized and grouped together into a household. The share and loan data discussed previously can be aggregated by household, and the data for ages, balances, and account open dates data is also processed to determine the head of household according to each criteria. When you are ready to send out targeted marketing, you can use these criteria to narrow down the focus even further and then address the piece to the head of the household.

 Demographics

For an additional fee, we can obtain and import demographic data for your membership into DataFlex. We find these extra data points very handy in crafting targeted marketing. If you do not have age/birthdate data for your membership, or it is very sporadic, one of the fields we can obtain is Age. The bulk of the supplementary fields are related to home ownership, mortgage, home equity, etc. Also available is the Income range of members. Lastly, Children in the household is another flag that can be used as well.

Contact us to learn more about DataFlex and how to increase engagement

All of these categories show many different facets of a credit union membership. I hope I was able give you some more insights into DataFlex’s capabilities and spark new ideas. Analyzing the various characteristics can lead to new insights and new ideas for targeted marketing. Member engagement and retention can be increased by targeting members in different categories and tracking them through a “matrix” and offering additional services as they increase their engagement. Contact LKCS today to learn more about DataFlex and what it can do for you!