Member Retention in Professional Orgs

Jim answers questions from fellow Drillers
(More questions with answers here, Work Overview here, Index of concepts here)

Q: I have recently purchased your book Drilling Down and going through the many interesting concepts.

A: Thanks for that!

Q:  I work for a membership Organization and we would like to conduct some analysis into who we may lose and approach them even before their membership lapses.  But the only problem here is that we carry data only on the purchases made (though many of our members do not purchase our products and stay a member) and web site visits.

A:  Are you *sure* that’s all the data you collect?  I once worked with a professional membership org that thought they only had one data source, but turns out they had 8 – from 8 different areas of the org – that nobody really knew about.

Q:  How do I know if a particular member is going to resign and lapse soon with this limited amount of behavioral data?  Recently it’s been a concern that we are losing members who have been with us for more than 10 years and who are in their mid career profession (aged between 30 to 45) and indicated no specific reason for resignation. 

This has been going on for the last few months and now we would like to strategically target these customers and approach them even before they react negative.  What concepts could help me to do this? Your guidance would be much appreciated.

A:  OK, my answer will be in two sections: if you (hopefully) find you have more data than you think, and if you really don’t have any other data to fall back on.

The first thing you should do is make sure you don’t have any other data sources.  Purchases and web sites visits might be the most obvious, but do you have:

Membership Campaigns
Conference registrations
Customer Service incidents
Local or regional meetings
Training sessions
Orders for training materials

and so on.  In some orgs a lot of this is siloed  or even outsourced, but the data is still there – it’s a question of whether you can access it.  Think through the business model, and think about any possible interaction points with a member.  Then ask, Where would this data be?  You might be surprised at what you have.

Once you have more data, subscription relationship analysis of this type and predicting churn come down to three main thrusts:

1. Number of activities – similar to banking customer analysis, we often find that the number of different areas a member has tangible contact with is predictive of retention or defection.  For example, some members attend the annual conference and others do not.  Some members participate in local meetings and others do not.  Some buy training materials and others do not.  Some members do all three, some two, some one.

On average, the members engaged in all three activities are the most likely to remain members relative to those engaged in only two.  And members engaged in only two activities are the most likely to remain members relative to those engaged in only one.  This is a “run rate” kind of retention, an expectation.  From a Marketing perspective, this means you want to always be adding to the number of activities a member engages.

2. Change in number of activities – when you see a member drop from 3 to 2 activities, this is a clear signal that there is a problem of some kind, it’s a dis-engagement from the org.  You want to take action when you see these events, find out what is happening and if there is anything you can do to correct this.

The reason driving this downgrade may be a soft incident, say a content problem, or may be a hard incident, like payment problems. Either way, the org needs to find out if the issue can be addressed.  If you start to see “clustering” of these kinds of downgrades in relationship quality, it’s likely something more systemic is going on.  Often you will see certain segments who exhibit similar problems.

For example, members acquired through a certain publication may exhibit similar downgrade behavior at roughly the same interval from joining.  This is evidence of a systemic problem – something about folks from this source is unique, and for whatever reason, the org is not satisfying their needs.

3. Predicting change in activities – if you want to go further down this road and actually *predict* a change in the number of activities before it happens, you can look at the Recency within that activity.  A member who goes to conferences on a regular basis who then skips one is in danger of defecting from that activity – for some reason, the conferences are not providing the value they used to.

Or, something has changed with the member, their position in the LifeCycle has moved.  The conferences still provide the same value as they did before, but this value is shrinking for the member who (perhaps) needs more challenging or different content.

The same could be said for attending local meetings or buying training materials, etc.  For a known user of a specific activity, how long has it been since they used it?  Does this non-usage break an established pattern of usage?  If so, you have a triggering event for a marketing / membership intervention.

OK, so what if you really don’t have any more data? You can create it.  One of the easiest and least intrusive ways to do this in a member org is with surveys.

Why?  Membership orgs have embedded permission to interact with members; it’s the nature of being part of such a group. What I mean by this is asking members to take part in a survey is not only quite natural, it’s often expected and perhaps even appreciated.  After all, what could be more aligned with a membership org than asking the members where the org should be headed and where they would like to see it go?

By thinking through and properly crafting such a survey, one should not only get a sense of potential friction points in the org overall but also get a sense at the individual level of which members are becoming dissatisfied and more likely to defect.  Implementation of a program like this means, of course, that the survey responses are tracked at the individual level so that action can be taken at the individual level. You can’t just do a random popup on the web site to make this work.

Moreover, in the out years, one can reverse engineer the reasons for defection.  Each year, analyze the population of members who defected in the previous year, and ask yourself, How are these people similar?  Do they come from similar backgrounds or industries?  Did they join in the same year? Come from the same marketing source?  You may already have such survey data and simply had not thought of using it for analyzing and predicting which members would defect.

Finally, I am fully aware that often, bringing these kinds of issues to the table can be painful for a membership org.  In my experience, many of these orgs are highly politicized and there is a certain “we’ve done it this way for 100 years” attitude.  When you present data that contradicts long-held beliefs there can be tension.  And this is fine – when the org is on board with the project.

So my final advice would be this – whether you have the data or generate it, the success of a project like this can revolve around the strong commitment of the org to actually do something about member defection. Ask this question of management first:

If we find members are leaving because of something we are doing or not doing, will we change this, even if the problem contradicts long-held beliefs?  The answer will show you how committed the org is to fixing member retention.

“It depends” is not the answer you want to hear from these senior players, because this means they don’t have a retention problem, they have an acquisition problem.

Unless they are willing to change, they need to recruit more members that are not like the members they have if they want to decrease defection in the membership.

Hope that helps!


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