Keeping the data in your membership management software clean and correct can offer multiple benefits to your organization:
- You’ll spend less time finding the data you need.
- Your reports are likely to be more accurate.
- You won’t waste dollars on data storage space for old data that you don’t need.
- You won’t waste time and money moving incorrect data around from system to system.
Cleaning and organizing your member data is not an easy task, especially for large member-based organizations. But there are some things you can do to keep the bad data at bay. Here are five poor (but common) data habits to break right now.
How to ensure data is accurate, accessible, and actionable
Ignoring duplicates.
Duplicate records can have serious implications on your data and analytics. It’s time to give them some serious attention.
Let’s look at a typical scenario: A member receives a renewal notice in the mail or via email, navigates to the self-service portal, and can’t remember the right username or password. Because your system doesn’t easily allow for a password reset or a way to search for a username, the member creates a new username, password, and account. Suddenly, it looks like you lost one member and gained another. Worse yet, that person might start receiving communications asking them why they stopped being a member, as well as notices about member discounts available online. It’s a terrible member experience, and it gets your membership reports off track.
There are two issues here that need to be addressed – and can be easily tackled by a modern membership management system:
- The system can alert a user when a duplicate record might 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.
- It’s important to clear the system of duplicates if they manage to get in, despite safeguards. For example, Aptify has an easy merging process for duplicate records. You can 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. And, Aptify’s merge tool will even suggest which field should be saved, in the event there is a conflict.
Remember that when it comes to duplicates, you should have processes in place at both the stage where data is added and the stage when data is reviewed. This way, you’ll prevent duplicates from being added while having the tools to remove them when they do slip by and get into your system.
Living with inaccurate data.
People’s contact information changes frequently, and keeping up with it can be tricky. But consider this: 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 must register for your annual meeting.
These are perfect times to add a section of the member profile to the renewal and registration processes so that, when people go online to renew or register, they are prompted to update their details. One or two quick questions for your member will take only seconds but will provide you with invaluable information.
Allowing inconsistent data.
Ever look at the contacts in your membership management software and see that some numbers have the +1 and others don’t? Or that some addresses use an abbreviation (Ave.) while others spell out the word (Avenue). Those inconsistencies might seem small, but they can lead to inaccurate lists and reports – always leaving you wondering if you’re working with the latest and greatest insights into your membership.
The good news is that these inconsistencies can be fixed by putting systems into place that enforce or clean the data as it’s entered, and you can use other tools to normalize any bad data that slipped through the cracks. For example, with Aptify, you can have the system automatically update phone number data 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.
Entering data without rules, guidelines, or guidance.
The beauty of modern systems is that they’re intuitive, so most people can start working in them with little training. On the flip side, 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 use all caps exclusively, and others do a mix and match with no reason whatsoever. The result: more of that pesky inconsistent data.
What can you do to break this habit?
First, set the expectation from the get-go that everyone must input data the same way. Let your staff know that the data they put into the system is going to be used to format correspondence, and you don’t want some mailings going out in which the person’s name is in all caps, while others’ names aren’t capitalized at all.
Second, create wizards and workflows (or work with your IT team to do so) 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.
Only fixing bad data when you switch software systems.
Don’t wait for a new membership software implementation to do data clean-up. Your entire organization – from the IT department to the business users – should help to keep data clean year-round.
Schedule regular duplicate data reviews and cleanses. Fixing a couple of duplicates here and there or normalizing the title field on a few records regularly is much less daunting than fixing thousands every other year. This approach also keeps your data clean versus letting it get messy between cleanings.
Also, every time you do a duplicate review or cleanse, be sure to review how duplicates 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 your system by working with your users and showing them how you share their concern about working with bad data; however, be sure to insist that they follow specific guidelines for inputting data into the system so that their data is clean and accurate.
Then, work with your organization 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 automated gatekeepers so you can enhance those processes and prevent bad data from getting into the database in the first place.
