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 the new era primarily electronic transactions. There are two reasons MCIF systems seem to fall short for a growing number of credit unions.
First, it is 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 typical MCIF system.
Beyond MCIF The next level of analysis beyond simple MCIF is an emerging technology know 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.
One-to-One Marketing-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 data.
In recent months, a number of companies have made data mining tools available to credit unions. For example, core processor Ultradata is very active in this area. Likewise, earlier this year, Boeing ECU announced that it was forming a CUSO specifically to provide Web-based data mining services to credit unions.
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 partners.
Details about Web site usage.
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 two 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. 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.