Information Gold Mine: The Ultimate Guide to Using Association Data the Right Way

June 10, 2021
Tasio Team
Information Gold Mine: The Ultimate Guide to Using Association Data the Right Way

You’re probably sitting on a goldmine of data and don’t even realize it. In fact, now more than ever, associations are collecting mountains of data but don’t have a data strategy to actively drive member growth.

Many times, association leaders feel as though they’re drowning in information with no real way to leverage it to solve their most pressing questions. Instead, they get bogged down with worries about the data itself, effectively blocking them from using it to help them increase growth and member retention. 

Ask yourself: Have I had one of these questions in the past 6 months? 

  • Is the data I have useful or even reliable? 
  • How do I go about getting the data out of our systems in the first place? 
  • With all this data (or “lack of data”), how do I use it to make change for the better? 
  • How can I offer better courses, incentives, or services to my members? 
  • Why are my members leaving? How can I get more of them to renew their membership?
  • Where can I cut costs? What systems or services are wasting time and money?

If you have, then we’ve created this guide to help. 

In this guide, we have outlined some of the biggest issues facing associations when it comes to data management and data strategy. Here, you’ll find both actionable steps to take now to have better data practices and free downloadable templates to help you leverage it for the growth of your association.

Data Goals Worksheet Download

Data Visualization Cheat Sheet Download

Association Retention Playbook Download

Tasio Sample Data Insights Report

Yes, Data Can Solve Your Association's Problems. 

But you need a clear purpose for data if you want it to work well.

There’s no denying that having data is valuable for your organization, but how often are you really thinking about what all those numbers mean? Looking at your association's data at the big-picture level once a year or quarter might be a good start, but it's not enough.

Innovations in the information sphere mean that information is not just a passive snapshot of what did happen, but an active tool to project what is likely to happen in the future. 

When identifying your data goals, it’s important to think about your overall organizational goals. When these are aligned, you can really use your data to achieve your larger objectives.

Data Goals Worksheet Download

What Problems Can Predictive Analytics Solve for Associations?

There are a few key areas where Tasio works with associations to gather information, make predictions based on past behavior, and then develop an action plan that is data-driven and focused on visible results.

Better Member Retention

Keeping members is one of the most critical goals your data can help you with. Good data analysis gives you a clear view of what your members actually care about and are doing, right now to engage (or not engage) with your organization. This leads to better messaging, more value, and a more personalized member journey with your association. 

New Member Acquisition

Your data doesn’t just allow you to peer into the minds of existing members. It helps you get a detailed picture of the inner workings of prospects as well. If increasing new member acquisition is one of your data goals, then leveraging your historical and real-time trends and the relationships between your data points is your golden ticket.

With the right data, strategy, and human capabilities, you can build out behavioral personas that capture what makes different potential members tick and why they’d want to join your membership. Then you can use this information to reel them in.

Increased Member Engagement

Maybe you want to use your data to prompt better engagement. Getting a pulse on real-time and historic data insights can help you devise personalized email triggers, social media campaigns, and other engagement campaigns that deliver more targeted messages and recommendations that truly resonate with members.

Higher Event Attendance

Another area that many association leaders don’t know their data can help with is in event organization and attendance. Based on your current data, it is not hard to make predictions about the success of future events and what kinds of attendance, traffic, and sessions are most important to your membership. 

In addition to accurately forecasting the success of your future events, you can use your historical data to increase registrations and attendance. For example, you can do this by analyzing what patterns have historically predicted that a person that attended a previous event does not return to the next event. You can also analyze the results of historical marketing and outreach campaigns to predict which people are likely to respond positively to a campaign. By combining those two predictions you can identify persuadable people who wouldn’t have registered unless you reached out to them.

Better Certifications and Continuing Education

Not only can good data allow you insight as to the CEU products, certifications, and books that have been most valuable to your members, but it will allow you to identify peripheral needs and motivations that can inform your future offerings. For example, a real estate association member that has spent money on networking activities and books about leadership but whose position is still a realtor at a large firm might be a perfect candidate for a course about starting a real estate agency. 

Record-Breaking Fundraising and Donations

There is no place more fraught with uncertainty in an association than when it comes to fundraising and donation activities. But with the right data analysis, you can actually make accurate predictions about both the amount and timing of donations on a yearly basis. Not only does this mean that you have a better idea of your yearly projected cash flow, but you’re able to be more accurate in your financial forecasts

You can also use your data to identify people that have not yet donated but have a high propensity to donate, or give much more than they have previously. Remember the last time you logged into a streaming platform and saw a message like “Users like you also enjoyed. . .?” With the data you already have you can segment donors and potential donors based on other behavioral patterns. This will allow your fundraising team to target and prioritize potential donors that will have the greatest impact.

Evaluating Your Association's Data

Good data is not hard to find. In fact, you probably have everything you need right now.

Like we mentioned before, good data can be used to help your association reach its most critical goals. But, of course, there’s a mental block that a lot of association leaders have about using it effectively. Some of the big questions we see all the time are: 

  1. Where do I get the right information?
  2. Is my data good enough? 
  3. Who do I need to go to in order to get this data?
  4. What do I do with it once I have it?

These are definitely important questions, but, odds are, you already have the kind of information you need to attain your key association goals. It’s about knowing what to look for and making a plan to visualize it. 

Watch the "Is My Data Good Enough?" Full Webinar

Where Do I Get the Right Information? 

There are lots of places to gather information in order to make predictions, but the key to remember is what your goal is. For each goal, there might be a slightly different list of data points you want to have, from different sources. 

But, to simplify, there are really only two kinds of information for you to gather: first-party data and third-party data. 

First-Party Data

This is information that you own and instigate completely from inside your association. This can include things like: 

  • AMS Data
    • Demographic and firmographic data
    • Transactional (non-dues) data
    • Certifications
    • Committees
  • Event Data
    • Registration and attendance
    • Session attendance
    • Webinars
  • Community / Website
    • Website logins
    • Page visits
    • Forum participation
    • Newsletter subscriptions

Third-Party Data

This is information that is valuable and pertinent for you to understand your membership, but is not necessarily owned and instigated by your association. There are two types of third-party data, actually. Association-managed third-party data is data from an outside platform that you control while External third-party data, which is completely controlled by another entity.

Association-managed third-party data
  • LMS or CMS records
  • Social media analytics
  • Google analytics
External third-party data
  • Census records
  • Federal Reserve economic data
  • Credit scores
  • Licensing information

Both first-party and third-party sources should be used together to create a cohesive picture of your membership so that you can make the best possible predictions, but remember that you don’t have to boil the ocean. Using what you have access to now is better than waiting for the perfect set of data. Predictive analytics thrive on good, extensive data, but you can still get great insights from using what you have now. 

Is My Data Good Enough?

The short answer is: yes. 

Leaders of associations sometimes think that having cobbled-together member files, incomplete data, or old information makes data prediction impossible. The truth is, all data is good data. 

The actual litmus test of what makes data good depends on whether: 

  1. You can make predictions from it
  2. It helps you solve your overall organizational challenges and questions

Still, not all data is created equal. There are qualities that make some data better than others when it comes to ability to use it to make predictions about your association and members. Usually, quality data that you can confidently use for forecasting to meet the goals of your association has three factors:

  1. Completeness - Are key data points complete or are you missing stuff?
  2. Validity - Is it valid and relevant, or do you have information that isn't quite connected with your big-picture goals?
  3. Recency - Are key data fields that could change over time (e.g. interest areas) timely enough and still accurate, or is it outdated and thus no longer useful?

Best Practices for Association Data Management

Your AMS—Not Quite a Comprehensive Data Solution

In the perfect world, your Association Management Systems (AMS) would support all of your association’s needs, including managing your membership, email marketing, and even payments or events. These kinds of solutions, like Wild Apricot, Aptify, and MemberPress, often integrate with a Customer Relationship Management (CRM system) and strive to provide you a comprehensive way of connecting with members.

In reality, most AMS systems aren’t as robust of a solution when it comes to gathering and analyzing data from all the best sources. For most associations, the “all-in-one” AMS solution is supported by a number of internal and third-party data sources to give a holistic look at your membership.  

The Best Data Management Systems for Associations

The ideal data management system would connect your transactional and analytics data from your AMS with all the other platforms that you’re using, including your LMS, events platforms, and others. This makes for a more comprehensive way to view your data and a better digital experience for both your internal team and your members. 

In other words, all your front-end and back-end systems need to be set up to allow seamless data collection and analysis.

Unfortunately, the perfect system just doesn’t exist, and there is usually a human component to aggregating information so that it can be used to make the best predictions about member behavior. At Tasio, that’s exactly where we come in.

Mining Your Data

Data mining uses machine learning and predictive analytics to find patterns across your ecosystem of data. Acting on the insights you gain from mining your data is a powerful way to accomplish your cardinal organizational goals.

If you want to retain more members, for example, you could use data mining to create a list of members to target. Here is one step-by-step pathway to accomplish this goal:

Gather data that signifies the engagement activities of existing and past members.
Create a model using those factors that is highly predictive of renewal using machine learning and artificial intelligence techniques.
Use this model to score and rank all members.
Divide members into groups ranging from least likely to leave to most likely to leave.

Create data-driven member personas based on revealed interests, so you can understand the true value individual members get from your organization.

This would give you two lists and behavioral profiles:

  1. List of members and behaviors that indicate a high risk of leaving your organization.
  2. List of members and behaviors that indicate a high level of retention.

With these lists in hand, you could target those who show a significant risk of leaving before they actually drop off, and improve your overall retention by focusing on high-retention behaviors. This will let your membership team prioritize the outreach strategies that will have the biggest impact.

Collaborating on Third-Party Data

As we mentioned before, there are two types of third-party information that supplement association-owned data in guiding decision-making:

  1. Association-managed third-party data (e.g. data from your LMS, CMS, events, newsletters, social media analytics, Google analytics)
  2. External third-party data (e.g. census and public records, economic data, credit scores, licensing information)

These mainly differ in terms of the control you have over them, but the concerns people have are largely the same for both:

  • “How much third-party data is too much?”
  • “Which sources are valuable and valid?”
  • “How can we access this information to make it useful?”

Effectively managing your association’s third-party data requires you to think about your larger organizational goals and what data you already have. Consider three overarching questions:

  1. Does this information help answer the questions I have about my members?
  2. How reliable is it?
  3. Is it accessible enough to provide real value in light of the time it takes to gather it?

Without answering these questions, you wouldn’t know how much or which sources of outside information are worth mining, leading you to waste time and resources on accessing and storing information that may or may not help your cause.

You could also consider safely exchanging information with a non-competitive organization that serves your same types of members. If done responsibly, such “collaborative information relationships” can:

  • Streamline the process of gathering information.
  • Address any issues you may have with partial member data.
  • Give you a clearer picture of your membership base so you can serve them better.
  • Increase the value and experiences members gain from your organization.
  • Better inform you of the kinds of future members you should target, and how and when to do so to increase conversion.
Talk to Tasio About Gathering and Organizing Your Association Data

Visualizing and Understanding Types of Data 

Once you have collected your data based on the specific outcomes you’ve chosen, the next step is to manage it in such a way that you can actually begin to see patterns and take action.

Remember, your data is the key to solving problems, but it is only valuable if you can actually understand what it’s telling you about your membership. Some of the key areas that you might want to consider are:  

  • Member Retention
  • Member Acquisition
  • Member Engagement
  • Marketing Segmentation
  • Marketing Effectiveness
  • Event Attendance
  • Event Booth Sales
  • Sponsorships
  • Donations
  • Certifications
  • Advocacy
  • Government Relations

Turning your data into a graph or chart allows you to really see the trends in relationship to the goals above and the metrics that are associated. For most of the models that you’ll need to make good predictions, there are only a few things you’ll need: 

  1. Data (as comprehensive and recent as possible)
  2. Excel
  3. Graphing capabilities

Since most people have access to these tools, it really is possible for anyone to visualize their member data. Of course, it’s much easier said than done. 

We actually provide a step-by-step walkthrough of exactly how to visualize your data in our Association Retention Playbook: The 5-Step DIY Membership Retention Strategy and Workbook.

Association Retention Playbook Download

Understanding Types of Data

The very first step in understanding your data is to analyse what type of data you have. Understanding the type of data you have will drive the types of analysis available to you going forward. Once you develop this skill, you will be able to immediately know what visualizations are possible and what types of modeling techniques and problems you will be able to solve with the data.

The main types of data you should be familiar with are:

  • Numeric Data
  • Categorical Data
  • Binary Data

Numeric data is likely what you think it is. Any type of data that has a number associated with it is numeric. Total sales, number of registrations for an event, number of certifications held, total donated in the previous year are all examples of numeric data.

Categorical data is any type of data that can be placed into a discrete bucket. If it can be an option in a dropdown menu, it is categorical. Examples of categorical include member types (non-member, student, professional, retired), product categories (events, membership, courses, other non-dues)

Binary Data is any data that has a yes-or-no outcome, either an event happened or it did not. Binary data can be treated as a special type of categorical data for most visualizations. Member renewals, event attendance, and recertification are all examples of binary data.

One Variable - Numeric

Histogram

The very first visualization in your arsenal should be a histogram. A histogram allows you to see how numeric data is distributed across your dataset. Histograms put summary statistics like an average into a greater context by showing how common that average actually is.

When reading a histogram, you should look for three things. First look for the tallest point. That is the most common data point in your dataset. Second, look for how wide the chart is, that tells you if there is a lot of variability in your data or if it is fairly uniform. Third look for “skew” meaning one side is chunkier than the other. That tells you that there is some phenomenon clustering your members around a certain data point, and you probably want to dig in to what that is.

For example take the two charts below. They both show the average distribution of member’s ages, and both charts have an average age of 50. This first chart shows that member age can range anywhere from 30 to 70.

In the second chart though, the variability is much smaller even though the average is the same. The youngest member is around 40 and the oldest is around 60.

One Variable - Categorical / Binary

Bar Chart

Bar charts allow you to compare different categories of data side-by-side. When you have a bar chart you can see which categories have the most data in them.

Donut Chart

Donut charts are bar charts that are connected to make a circle. These show the same information as a bar chart, but the presentation is often more intuitive for end users. A donut chart not only communicates the number of members in a category, it also communicates the relationship between those categories as a percentage of the whole.

Two Variables - Both Categorical / Binary

Stacked Bar Plot

Stacked bar plots are excellent when your goal is to explore the relationship between two categorical variables, for example membership renewals and membership type. In these charts you create a bar for each member type and show the number of renewals and non-renewals “stacked” on top of each other. This allows you to see if there is a significant difference between renewal rates and types of membership.

It can often be useful to view stacked bar plots as a percentage of the total for each category, rather than the count. This is especially useful in situations like we have here where there are many of one category (Professional) and few of the others.

In the chart below, you can more easily see that Emeritus members renew at 90%, Professional at 75%, and Student at 60%.

Two Variables - Both Numeric

Scatter Plot

Scatter plots allow you to explore the relationship between two numeric variables, like the number of continuing education hours attained and total dollars spent. You’ve likely seen a plot like this before. When looking at a scatter plot, you are looking to see how closely the dots come to form something like a line. 

For example, the scatter plot below shows a very strong relationship between CE Hours and Total Spent.

Whereas the plot below shows a very weak relationship

This plot lies somewhere between.

One interesting technique you can use with scatter plots is to change the color of a dot based on some categorical data point. Often this can reveal an interesting relationship between 3 different data points. For example if I make a scatter plot of CE Hours and Total Spent, then color the dots red if they did not renew or blue if they did, you see an interesting pattern jump out below.

Two Variables - Numeric and Categorical / Binary

Box Plot

Box plots allow you to inspect the relationship between numeric and categorical data. The way to read a box plot is each box represents a category, the height of the box is determined by the maximum and minimum values of the numeric data.

So for example the red box shows how much money was spent by members who did not renew. The blue box shows money spent by members that did renew. You can see that the maximum spent for non-renewing members was a little over $300, and the minimum amount was right new $0. The average amount was a little over $200.

Compare that to members that did renew. The maximum was slightly more than $500, the minimum was around $125, and the average was about $300. When you see box plots that have significant differences like the ones below, it shows that there is likely a relationship between the categorical and numeric data you are exploring.

Density Plot

Another way to visualize the relationship between categorical and numeric data is with a density plot. Density plots are very similar to histograms, but they smooth out differences which makes it easier to compare. 

The plot below shows the same data that was represented in the box plots. The way to analyse these plots is to look for multiple peaks on the plot, or a significant difference in shape between the two categories. When you see these peaks, or other changes in shape, this is a signal that there is a relationship between these two data points.

Data Visualization Cheat Sheet Download

Conclusion: Data-Driven Associations Take Action

But you don't have to do the data analysis dirty work alone.

Data was once critical to only accounting and a few back-office processes. Today it’s central to any organization, and the importance of managing it strategically is growing by the minute. If you aren’t acting on your data strategy (or haven’t even developed one yet), it’s time to catch up very fast.

The possibilities are endless when you understand the data that’s so integral to scaling your organization. We make it easy to take advantage of real-time data and real-time insights using predictive models and machine learning techniques.

At Tasio, we take all the guesswork out of analyzing data and presenting actionable strategies that your association can take to increase member retention and provide a more meaningful member journey. 

Tasio Sample Data Insights Report

Are you ready to realize the power of your data? Join us to jumpstart your data strategy and drive real growth.

Tasio Team

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