If you look closely, you’ll notice that much of your life—your relationships, your finances, your politics—is predicated on being able to predict the future.

You date people with certain behaviors and character traits, thinking they will help provide the life you want later. You bet on the stock market, hoping that the companies you invest in now will perform well over many years. You even try to understand the views and assess the temperament of politicians to determine whether they will respond well to both known and unknown leadership challenges down the road.

The common thread is data. In all aspects of life, people seek to gather enough information to make assumptions about likely future outcomes. In a similar way, if you can predict member behavior, you can leverage that information to increase member acquisition, optimize benefits, and boost satisfaction. Consider the possibilities:

• What if you could identify members who are not getting value from their membership and reach out to them before they leave you?
• What if you could focus your volunteer recruitment efforts only on members who are likely to accept volunteer opportunities?
• What if you could target promotion of your new education program to members who are highly likely to register?

Combining demographic, transactional, and behavioral data gives your association a crystal ball into the minds of your members.

When you have the right information, you can get the right message in front of the right people at the right time. Getting these results comes down to your data. But association leaders and membership professionals need to think about their member data differently.

Traditionally, associations rely on two types of data:

• demographic data: who the member is, professional information, and so on
• transactional information: what the member bought, registered for, and attended

This data has never given associations a full picture of their members’ priorities, challenges, and motivations, but it was typically all that was available in their association management (AMS), customer relationship management (CRM), or marketing automation systems.

In recent years, a third type of data has changed the game for member-based organizations: behavioral or activity data, which gleans insight about members based on what they do online.

Where can you look to find behavioral information about your members? The easiest answer is your website and private online community. Members’ activity in community software—from logins and discussions to member profiles and file downloads—can show you a lot about your members without their explicitly telling you anything.

Combining demographic, transactional, and behavioral data gives your association a crystal ball into the minds of your members. But gaining insight into each member’s goals, interests, and problems is just your first step.

Reading the Crystal Ball

There are three steps to using your association’s new crystal ball to increase membership, retention, and revenue.

Step 1: Connect your data. Gaining business intelligence relies on compiling the data from all your systems—including marketing tools, AMS and CRM software, and community platforms—into a single location. Many have integrations that are easy to set up. Once systems are connected, you’ll be able to create a list of members based on combined demographic, transactional, and behavioral data.

Step 2: Create your list. Based on these three types of information, develop a profile of the members you want to target. Be clear about your goal in each case:

• Are you trying to turn frustrated or disengaged members into participating members?
• Are you trying to convert student or virtual members into full-pay members?
• Are you trying to recruit advocates or volunteers?
• Are you trying to get more people to sign up for an event?

For each of these goals and their associated messages, you can define criteria to create a member profile that is most likely to respond to those messages.

For example, you might want a list of members at risk of letting their membership lapse. The profile of such members might be people who have been members for less than a year (demographic data) who did not attend the annual meeting (transactional data) and who in the past have participated in online community discussions but have not logged in during the past 60 days (behavioral data). Once you have a profile of an at-risk member, you can optimize it over time.

Step 3: Pick your outreach plan. You’re not making the most of your data unless you do something with it. It’s time to decide how you want to get your message to members on your list. Start with the primary communication channels associations use:

• Email. Send automated email drip campaigns to members that meet your profile criteria. For example, if a longtime member has stopped participating in your private member community, after a certain number of days of inactivity she will get a series of re-engagement emails to bring her back.


• On screen. Set up dynamic calls to action to get members to convert on relevant offers. For example, members who viewed presentations from last year’s annual meeting who have not yet registered for this year’s event can receive targeted on-screen messages in your private member community urging them to register.


• Personal outreach. Notify member services or support representatives via email or text message that outreach to a member is needed. For example, when a member meets the criteria for someone at risk of dropping his or her membership, member services representatives can be alerted to reach out to that member before it is too late.

Many associations are sitting on a goldmine of data that will help them predict the future actions of both individual members and their membership as a whole. Following this model to use demographic, transactional, and behavioral data will enable your association to provide more relevant resources to members and see higher conversion rates on offers for years to come.