In the past, knowing an individual
member was a matter of personal interaction. A member would visit the branch
to cash a paycheck or make a deposit, and over a period of time, staff and member
would come to know each other on a personal basis.
However, in this Internet age, when members increasingly access their credit
unions via remote means, knowing any individual member becomes much more problematic.
Since technology has reduced the opportunities for face-to-face interaction,
credit unions must rely more on technology to solve the problem.
Many credit unions have been using MCIF systems for years in an attempt to leverage
technology to learn more about their members. Today, however, there is a growing
sense that simple MCIF systems cannot carry credit unions into a new era of
primarily electronic transactions. There are two reasons MCIF systems seem to
fall short for a growing number of credit unions.
First, it is still hard to pick a of financial trade publication without reading
the term ''Internet speed.'' While the term may be over-used, it does capture
an important idea. The traditional MCIF model involved capturing basic data,
sending it off to an MCIF provider, getting back reports a few weeks later,
and then launching some sort of marketing campaign based on that information.
Today, when members can switch primary financial institutions with the click
of a mouse, this is simply too slow. Credit unions need to be able to act on
information immediately as it becomes available.
This in turn implies the second area of concern. MCIF is generally a tool used
to segment membership -- in other words, to identify blocks of members who share
some similar characteristic. Today, though, the move is on to analyze data and
use that analysis in such a way that a credit union can identify specific characteristics
of a specific member, and as appropriate, target market to that specific member.
This calls for a level on analysis that goes beyond the capabilities of the
current typical MCIF system.
The next level of analysis beyond simple MCIF is an emerging technology known
as data mining, or data warehousing. This involves gathering all available data-including
complete transactional information-and then making decisions based on a very
deep analysis of the data. The final step is action-preferably immediate action-based
on that analysis. Below are a few terms that are likely to appear in discussions
related to data mining.
CRM - Customer Relationship Management. This term encompasses all activities
and technologies implemented to enhance member service.
Data Mining - Also known
as data warehousing. Refers to the complete collection of any data available
about a credit union's members.
ETL - Extract, Transfer
and Load. The means by which data is moved from a credit union's production
systems to the analysis system.
Mass Customization -
The concept of providing a unique user experience (e.g., on a Web site) for
individual members of a larger group.
MCIF - Marketing Customer
Information File. The means by which many credit unions extract basic marketing
information from their available data.
OLAP - Online Analytical
Processing. The software tools used to perform the detailed analysis required
in a data mining environment.
- The use of mined data to target a specific marketing offer to a specific member.
Predictive Modeling - The
use of advanced analytical software to predict an outcome based on available
In recent months, a number
of companies have made data mining tools available to credit unions. For example,
core processor Ultradata - HFS is very active in this area.
Gathering All the Data
One key to successful data mining is the collection of as much data as possible.
In this environment, no detail is insignificant. It is important to load as
much data as possible into the system. This includes, but is not limited to:
- Static account information.
- Detailed transactional information.
- Information from e-commerce
- Details about member Web
The important point to keep
in mind is that the collection and analysis of the data is really only the first
step in the process. After all, the most detailed information is useless
unless the credit union acts on the information. How a credit union
shapes its policies, procedures and marketing efforts based on the mined data
will ultimately determine its success or failure in this emerging field.
Marketing to the Individual
The ultimate goal of any data-mining
project is detailed information that can be used to market specific products
and services to specific members. Under ideal circumstances, this could mean
making the effort to market a product to one and only one credit union member.
However, in practice, this doesn't necessarily prove cost-effective. Imagine
custom printing one copy of a mailer and sending it to one member in hopes that
they will respond. Statistically, the chances for a response are slim.
On the other hand, when credit
unions market electronically, they avoid costs such as paper, printing, postage,
etc. This means that done by electronic means, marketing to the individual becomes
much more cost-effective.
One-to-one marketing has been
a hot topic among home banking providers for the past few years, and continues
to offer an easy entry point to credit unions. The concept here is simple. When
a member logs into the home banking system, a back-end system instantly determines
the most appropriate marketing message for that member and displays it in a
small portion of the browser window. The member then has the option to click
through for additional information. For credit unions that also have online
loan application and approval, this can prove to be a very powerful tool.
While the Internet is the obvious
electronic channel for this sort of endeavor, it is certainly not the only one.
For example, in spring of 1999, Diebold introduced a one-to-one marketing module
for its ATMs. This technology allows credit unions to display targeted marketing
messages during an ATM transaction. Diebold claims that the message is displayed
only while the transaction is being processed and thus adds no time to the transaction.
While this is all compelling,
it points to a larger obstacle that faces credit unions as they attempt to deploy
these technologies: lack of system integration. Home banking
providers offer one-to-one marketing solutions for their home banking products.
ATM manufacturers offer the same for their ATM machines.
Conceivably, this same technology
could be deployed in the teller line. However, in the current environment, each
deployment would call for the administration of a separate one-to-one marketing
system by credit union personnel. And in each implementation, the member experience
is somewhat different.
One emerging solution to this
lack of system integration is the increasing popularity of the open systems
concept. (See the Data Processing section of Callahan's Credit Union Technology
Survey for a more in-depth discussion of this topic.) One benefit of a true
open systems environment is the ability to deploy a single solution across multiple
delivery channels. In other words, in the perfect open solutions world, a credit
union would be able to analyze data once, make one set of decisions based on
the analysis, and implement one set of rules to drive it's one-to-one marketing
campaign over the Internet, via ATM, at the teller line, or by whatever other
technology channels the credit union avails itself of.
While this potential has yet
to be realized, it is clearly the direction in which various technology providers
are moving. While it is important to consider what a prospective marketing system
can do for a credit union now, it is just as important-if not more important-to
explore that solution provider's plans for the future.