5 Bad Membership Management Software Habits You Need to Break
Let’s face it—bad data surrounds all of us. Open the Contacts app in your phone, and you see duplicates all over the place. Send out invitations to your birthday party next month, and you get three back because of bad addresses. Call someone to wish them a happy birthday, and they tell you it was two days ago. Or worse, reach out to the CEO at your association’s top client, only to find out they were replaced four months ago—something you should have known.
Unfortunately, keeping the data in your membership management software clean and correct is not an easy task, but fortunately there are some things you, your team, and your users can do to keep the bad data at bay so you can use your data to grow your business. Here are 5 scenarios we often see when helping new or existing clients manage their data.
Learn the business rules you can implement at your organization to ensure the data in your association management software is accurate, accessible, and actionable.
1. Duplicates are everywhere. Duplicates are everywhere.
Duplicate records can have serious implications on your data and your analytics. Let’s look at a typical scenario. A member receives a renewal notice in the mail or via email, and when that member navigates to the self-service portal, they can’t remember their username or password. Because your system doesn’t easily allow for a password reset or a way to search for a username, that member creates a new username, password, and account. Now all of a sudden, it looks like you have one lost member and one gained member. Worse yet, that person might start receiving communications asking them why they stopped being a member, while also receiving notices about member discounts available online! What a horrible experience—and your board won’t appreciate the inaccurate numbers.
There really are two issues at hand here that need to be addressed—and should be easily tackled by any modern-day membership management system like Aptify. The first is the system should alert a user when a duplicate record may be created. You can set up the parameters to check for matches, and then when a user goes to save a potential match, the system will pop up the alert. This alert should be present for both staff users and external users, like members.
The other issue is clearing the system of duplicates if they manage to get in, despite the safeguards. Check to make sure your membership management system has an easy merging process for duplicate records that you have identified. You may elect to run the merge in a batch process, or run it and review each result one-by-one to ensure the correct fields survive. Aptify’s merge tool will even suggest which field should be saved, in the event there is a conflict.
Remember, when it comes to duplicates, you need to have processes in place at both the stage where data is added and also the stage when data is reviewed. This way, you’ll prevent as many duplicates from being added as you can while having the tools to remove them when they do get into your system.
2. You have inaccurate data.
When you get that returned birthday invitation in the mail, you tell yourself, “Wow, I need to do a better job of maintaining everyone’s information.” However, it’s tough, because some friends aren’t good about sharing details like that, while at other times, you’re told the new information but you don’t know what to do with it or how to manage it.
The good news is, at your organization, a significant number of your members are going to your website at least once a year to pay their dues, or maybe twice because they also have to register for the annual meeting. But you don’t make it a habit to force them to go to their profile page and update it, probably for two reasons: 1) your profile page is long and monotonous, and 2) you really just want their money.
Here’s a perfect time to work with your system to figure out a way to add one piece of the profile to the registration process of the meeting and another piece to the renewal process. This way, when people go online to renew, they are forced to update their mailing address, or their email address, or their current employer. One quick question for your member will take only seconds but will provide you with invaluable information about them!
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3. Your data lacks uniformity.
As someone with a touch of OCD, it drives me nuts when I look at my contacts on my phone and see that some numbers have the +1 and others don’t, and that some addresses use an abbreviation (Ave.) while others spell out the word (Avenue). If you’re at all like me, I bet you get heartburn when you go through your system and see the same inconsistencies.
The good news is that these inconsistencies can be easily fixed by putting systems into place that enforce or clean the data as it’s put in, and you can use other tools to normalize the bad data that slipped through the cracks. For inputs such as phone numbers, you can have the system automatically update the number to add in a dash, parenthesis, or country codes so it remains uniform, while you can use tools like drop-downs so all salutations are consistent in your system. Just remember, the time to normalize your data isn’t when you switch systems—it’s on an ongoing basis!
4. You let your people enter data without any rules, guidelines, or guidance.
The beauty about modern-day systems is that they’re pretty intuitive, so most people can get in and start working in it with little training. The bad news, however, is that if you assume people can just go in and do what they want, your data will quickly show it! Some people do not capitalize names, some people use all caps exclusively, and others do a mix and match with no reason whatsoever.
First, setting the expectation from the get-go that everyone must input data the same way is extremely important. Let people know that the data they put into the system is going to be used to format correspondence, so you don’t want some mailings going out where the person’s name is in all caps, while others’ names aren’t even capitalized.
Second, being in the IT department, you have the ability to create wizards and workflows that can fix bad data before it’s committed to the database. For example, if someone puts in “john doe,” you can have the system produce a pop-up that says, “Are you sure you didn’t mean John Doe?” You can also integrate with other systems that are responsible for maintaining and normalizing data, like addresses. This way, you don’t have to enforce accuracy when inputting addresses but you always know your addresses are uniform and correct.
5. You only fix bad data when you switch software systems.
Data cleansing around a membership software implementation is a great time to tackle this project but in no way should it be the only time to tackle it! Since you’re in the IT department, you are partially responsible for keeping the data clean; the business users also have a responsibility because they rely on the data to do their job.
First, scheduling regular duplicate reviews and cleanses is imperative. Fixing a couple duplicates here and there or normalizing the title field on a few records regularly is much less daunting than fixing thousands every other year. It also means that your data is likely giving you bad trends and analyses if you wait that long.
Second, every time you do a duplicate review or cleanse, you should review how a duplicate got through or how a normalization event was skipped. See if you have an opportunity to add in a new safeguard or automated process to prevent that kind of event from happening again in the future.
Remember, bad data has long-reaching ramifications. It can affect the simplest thing like the greeting on an email, to more complex things like retention and join rates. Prevent bad data from getting into the database management system by working with your users and showing them how you share their concern that you don’t want them working with bad data; however, be sure to insist that they follow specific guidelines for inputting data into the system so that way their data is pristine.
Then, work with your team to use your system’s tools to keep bad data out. Put in checks to prevent duplicates from being entered and saved, use processes to suggest changes during data entry to enforce normalization, and while regularly cleansing your data review the errors that have made it past the automated gatekeepers so you can enhance those processes to prevent bad data from getting committed to the database in the first place.
If you’d like even more help with managing the data in your system, please be sure to check out our Data Management eBook.
About Eric McDonald
As an Engagement Manager with Aptify, Eric helps clients determine whether Aptify is a good fit for their organization as they look for a new AMS. As part of the process, Eric works with clients to consider implementing new systems and new business processes to boost efficiencies and drive greater change throughout the organization. Eric is based out of the New Orleans office, calling the “Big Easy” home since 2010 when he moved there to earn his MBA from Tulane University. Outside of work, Eric loves to CrossFit, spend time with his spouse, cuddle with his Corgi, Bentley, and watch every Saints game—Who Dat!