This is the first in a series of articles that will describe how you can use data and analytics to improve your marketing success. What will be described is not a quick-hit solution, but a discipline that successful companies have learned to employ that creates the opportunity for continuous improvement of their business. This is not the next best marketing list, but a process to put your credit union on the road to success!
In any construction project, the most important component is having a strong foundation. A strong foundation ensures that a structure will withstand the test of time. It also allows for future expansion or changes to the design as needs grow or change. The key component of the foundation of any service business is data. People have an intuitive sense of the definition of data, but it is helpful if we start with the dictionary definition:
da-ta From the Encarta English dictionary
1. factual information:
information, often in the form of facts or figures obtained from experiments or surveys, used as a basis for making calculations or drawing conclusions
2. information for computer processing:
computing information, for example, numbers, text, images, and sounds, in a form that is suitable for storage in or processing by a computer da-ta (new definition)
2. information for computer processing:
computing information, including member or prospective member data that describes their credit profiles, where they live, hobbies, interests, what marketing offers they responded to, how often they have been solicited, etc, in a form that is suitable for storage in or processing by a computer
The first definition refers to the process of experimentation and will be further addressed in a future article. The second definition is the one that is important for building a data foundation. It is helpful if we think of this definition in a marketing context:
It is the last part of this definition, "...processing by a computer" that we need to examine first.
Clean Data is Essential
Computers are essentially dumb machines. They are very literal in their interpretation of information they process. This is why it is of the utmost importance that data that you capture about your members or prospective members be captured correctly and consistently. As a simple example consider the following:
J. Smith is not the same as John Smith. John Smith is not the same as John Smithe. CA is not necessarily the same as Ca.
You can see these simple examples, how a minor change can potentially be misinterpreted by a computer. These seemingly minor input errors can result in the following:
Creation of duplicate accounts: Two separate accounts get created that really are the same person.
Matching Problems: Input errors can lead to problems pulling credit bureaus, obtaining demographic information including addresses and phone numbers, and matching to other internal files or external files.
Reporting Problems: In the above example producing a report by state could result in two separate outputs for California, one for CA and one for Ca.
Analytic Problems: Incorrectly inputted data can potentially lead to false conclusions and limit the ability to make analytically derived business improvement.
Rule No. 1: Information about your members or potential members needs to be carefully entered into your systems and standardized where possible.
There are many ways to set up processes in your business to avoid these types of errors. Some software packages have built in functionality to help standardize some data elements. For example, some packages have drop down boxes for state fields so California never would get input as Ca. Even with the best software packages, it is still important to adopt standards in how member data is captured. This is especially true for name, address, and Social Security Numbers. Some initial suggestions on standardization:
Try to avoid using abbreviations for first name. Thomas should not be input as Tom, Jonathan should not be Jon, etc.
Be sure to include generation codes such as Jr., Sr., etc. if appropriate.
If possible, set up edits so that numbers and letters do not get input in the wrong fields. For example, someone's Social Security number should not have any letters and their name should not have any numbers.
Set up edits for field lengths if appropriate. Social security number should not be more or less than nine numbers.
Consider software packages to confirm addresses and phone numbers. As an example, there are packages available that provide a town or city name by inputting a zip code. These packages are great for validating input data and confirming information provided by a member.
Following these tips will help you build a strong data foundation for your credit union.
Treat Your Members' Data Like Diamonds
Precious stones like diamonds can represent significant value. Because they are small and are enticing for thieves, care must be taken in safeguarding them when they are not being used. Without the proper care, diamonds can be lost or stolen. Your member data is like diamonds. This data represents value to your credit union provided it stored and safeguarded carefully. This data is also a covenant between you and your members or potential members. It is therefore important that this data is carefully safeguarded from unscrupulous or careless eyes lest it wind up being used inappropriately or illegally. Like a diamond, data is easy to steal if you are not vigilant. Some tips on how to protect data:
* Limit access to online systems containing member data to only those people who need the system to perform their jobs.
* Limit the distribution of reports that contain individually identifiable member data. Make sure to shred these reports when they are no longer useful.
* Keep track of which employees have access to member data and if possible what data they access.
* Safeguard and limit access to any files that contain member data. This includes any spreadsheets, word documents, etc. Delete these files when they are no longer being used.
Rule No. 2: Having access to public and non-public information about your member is a privilege. While this information can create value for your company, it also represents a covenant between you and your member that you will safeguard their information.
Protecting your members' data is like building a moat around your foundation. It provides protection of your foundation against disaster. It is also essential. Not properly protecting non-public information can lead to legal consequences that would be disastrous for any credit union.
I Have Clean Data, Now What?
Once you have completed your foundation with clean data, the next step is to build a construction framework. For purposes of your business, the framework consists of reports and analytics that give you insight into how well your business is doing. These can include, helping you to understand how effectively your marketing dollars are being spent, how efficiently your operations are running, as well as provide insights into your company's profitability.
Utilizing data mining techniques, your prospect pool and member base can be segmented to provide a clear picture of which members or potential members are the most profitable. These same techniques can be used to find new potential areas of opportunity.
All this can lead to the development of statistically derived tools designed to give you the ability to predict performance of your members or potential members. Predictability leads to increased profitability for your credit union.
In future articles, I will describe some of the techniques that can be used to provide business knowledge from data. I will also describe how to supplement your data with data from outside sources to increase profitability of your marketing efforts.
Robert Zelikson is co-founder of Zelcom Group, Schaumburg, Ill., a firm specializing in both risk management and marketing analytics. For info: www.zelcomgroup.com.