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Jason Green

By: Jason Green on January 13th, 2021

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Systematically Categorizing and Cleaning Your Data for Results

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In the era of Big Data, there’s a tendency to think that more data equals more value. Organizations hang onto every scrap of information in case it holds the key to some future success.

Actually, Big Data isn’t about size but about detail. Accurate detail helps you understand your members and can anticipate their needs. Low-quality data doesn’t offer this kind of insight, and in some cases, it can even make things harder.

There are also some practical reasons to remove low-quality or obsolete data, such as:

  • Keep your email sending score up: Email sender reputation significantly impacts deliverability and inbox placement. Your sender score can go down if you send emails to invalid addresses, such as opt-outs, spam traps, or unrecognized recipients.
  • Keep your marketing costs down: Marketing automation platforms charge per contact. If some of your contacts are invalid, that effectively means that you’re throwing money down the drain. Data cleansing helps lower your marketing automation costs while optimizing performance on other platforms like email automation.
  • Sharpen your digital marketing: When you chop away the dead wood, you get a clearer view of who your members really are. That allows you to create highly accurate member personas and to design buyer journeys that convert.

Data cleansing is something that every organization has to deal with eventually. The question is whether you wait for your data quality issues to hit crisis level, or you take a proactive approach and start implementing cleaning and categorization procedures.

What data should you clean?
The goal of data cleaning is to remove anything that is obsolete, inaccurate, or that might hinder your marketing efforts. When you’re working on data quality, you need to look out for four things:
1.      Unsubscribes
Users can choose not to receive email communications at any time. If they ask to unsubscribe, you should aim to honor that request as quickly as possible. This may require you to sync between systems—if the user updates their marketing preferences on the AMS, that information has to travel to the email and marketing automation platforms.

It might sound counterintuitive, but a user-friendly unsubscribe process can actually improve engagement and conversions. Users who don’t want to receive emails will see that you’re professional and respectful. Meanwhile, you can focus your energy on people who are interested in your messaging.

2.      Invalids
Emails can bounce for a number of reasons. The recipient’s mailbox might be full, or the server might not recognize them, both of which could indicate that the person no longer uses that inbox. If you keep sending emails to dead addresses, the server will eventually flag you as an irresponsible sender and lower your sender score.

Email lists may also include spam traps. These are fake email addresses that exist to catch spammers—send an email to one of these, and your sender score will plummet. Fortunately, you can identify and eliminate spam traps with the right data process.

3.      Unengaged or outdated
Here’s a common scenario: you make contact with someone once, and then they ignore all your follow-up emails. Eventually, you have to stop emailing these people. Otherwise, you’re likely to end up in their spam folder, which can impact your sender score.

And just because someone was responsive in the past doesn’t mean they’re a promising lead today. If you haven’t interacted with a contact for some time, there’s a chance that their data is obsolete. The older the data, the greater the risk of obsolescence.

4.      Anti-persona
Some people will engage with your content despite not being in your target market. For example, someone might choose to provide their email address so they can download an ebook, but when you look at their details, you see that they don’t work in your industry. In most cases, you can discard this contact.

On a more subtle level, you need to watch out for outliers that can skew your analytics. For instance, a recent graduate might access materials about senior management. This doesn’t mean that you need to rethink your graduate member persona to include more late-career information. Instead, you can flag this data as an outlier and ignore it in your analysis.

How to effectively clean and categorize data

Effective data management is about having the right processes in place. Here’s how to get started.

1.      Give your data a face
Associations are not in the data business. They’re in the member business.

Data exists to help you better serve your membership and your industry. So, when you’re looking at data, you start by figuring out what that information can tell you about members and their needs. You can put a human face on your data with these steps:

  • Identify personas: Look at commonalities between individual contacts, such as age, location, and job title. You can also develop personas on behavioral data, such as website users, conference attendees, or people who like eLearning modules. Eventually, you’ll have a persona matrix—a set of personas that covers the entire membership.
  • Segment by persona: When you have a persona matrix, you can divide your members into smaller segments. For each contact, you’ll need personal and behavioral information that helps match them to the appropriate segment.
  • Map content: When you understand your personas, you can identify the kind of content most appealing to them. For example, recent graduates might want eLearning and certification, while senior members might seek out speaking opportunities. You’ll need analytics data to tell you what kind of content has appealed to similar personas in the past.
  • Create a journey: With your persona and content map, you can devise a journey that will nurture each lead to conversion. At each step, you’ll require data about engagement so that you can keep improving journey outcomes.

This structure helps you think about what data you need and what you can delete. It also helps ensure that you’re taking a member-first approach to all decisions.

2.      Create an accountability structure
Data quality stretches beyond the marketing department. It’s up to the entire association to work together and ensure that you have access to reliable information.

To achieve this, you’ll need to build an accountability structure within your organization. This means answering questions like:

  • Who will own the data in terms of reaching objectives?
  • Who will own the integrity of the data?
  • Who will analyze and make recommendations about the data?
  • Who will organize the data in a way that marketing approaches are executed upon?

Clarity is essential here, or else you may end up with tons of superfluous information. Or—worse—you might lose vital data.

3.      Choose an approach to cleaning
There are three main data cleaning strategies: reactive manual cleanse, proactive manual cleanse, and automation.
  • Big sweep: This is a reactive approach, but you may need to do a big sweep if your data is out of control. With a big sweep, you perform a full manual audit of data. That means verifying against the AMS, flagging obsolete records, and identifying anti-persona leads. You’ll also need to run a verification tool to help remove invalid email addresses or spam traps.
  • Procedural: This proactive approach is a manual sweep performed on a regular basis. You’ll need to agree on Standard Operating Procedures (SOPs) with the relevant data owners and assign a team to complete the clean-up. However, as you’re performing this regularly, it should be a faster and more straightforward process than a big sweep.
  • Automated: Marketing automation and email automation tools can handle a lot of the work involved in data cleaning. With the right integration, you can set your automation platforms to sync with the AMS and automatically delete invalid or obsolete data. You can also implement an automatic re-confirmation process, wherein you send an email to say, “Please click this link if you want to keep receiving our emails.”

Procedural automation is the optimal approach as it ensures consistency while freeing up resources. However, automation is only possible if you have the right tools, the right integration, and the right data strategy.

4.      Monitor outcomes
It all begins and ends with the members. If you get the data strategy right, you should start to see positive results, such as increased signups or better engagement. If not, then you might need to troubleshoot your process. Some common problems include:
  • Low deliverability: You may still have some bad data in your contacts list. Try running a verification tool and removing old records.
  • Low open rates: Your content might not be precisely targeted to the segment. Review the available data and try to determine whether you’ve built accurate personas.
  • Low ROI on marketing automation: Focus on people with a higher lead score. If your lead scoring isn’t an accurate predictor, you may need to review the member journey.
  • High bounce rate: Remove any old contact data. You may also run a reconfirmation campaign to eliminate obsolete records.

Great data leads to great outcomes. If you stay on top of data quality, you’ll meet your marketing goals while keeping your costs low.

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About Jason Green

Jason has an uncanny ability to communicate complex technical solutions across client teams while executing from non-technical descriptions. He has a Bachelors of Science in Information Technology Web Management and spent 13 years working at the National Association of Colleges and Employers. Having worked in an association for that length of time, he knows how best to communicate the value of technology solutions to empower the member experience.