If you are managing an online consumer community it is paramount that you track why a valued community member closes their account. For me every active community member or member account that was acquired by monetary means is a valued member. As a community manager responsible for tracking the ROI of an individual community member and for controlling community member acquisition costs you must be able to understand your membership. You must also be able to track, predict and correct the community’s behavior. Understanding why a member account was closed is part of that equation.
Imagine managing a global online consumer community of millions. Ninety percent of your entire community was acquired through affiliate recruiting campaigns and each registered member cost your company $3.00. Now add the disheartening fact that your annual community growth rate is flat. You are racking up hundreds of thousands of new member accounts each month, but you are also losing an equal number of community members each month. Are you prepared to answer why when your CEO comes knocking on your door? Do you have any ideas how to stop the mass exodus and create a positive community growth rate?
Tracking why a community member closes their account can help you answer those questions. When I took on the operations of a community I asked similar questions to the ones above and no one could answer why members were leaving in droves. No one had a clue about the member intent or motivation for closing their account. Our members participated in online market research studies. We paid $x.xx amount to bring them and in and for every completed study they generated $xx.xx in revenue. I implemented some of the following attributes for the account closure datapoint:
- Voluntary unsubscribe through website (optional open field for reason why)
- Voluntary unsubscribe through email (optional open field for reason why)
- Voluntary unsubscribe through customer service desk (optional open field for reason why)
- Inactive for six months
- Non-disclosure violation
- Multiple account violation
- Email bounce notice
The list above does not include all the attributes, but does show you some of the most important ones. Not only will this tell you how community members are closing their account, but it will also give you a good indicator about the community health and costs for operating the community. For example, if 80% of the voluntary account closures are occurring through your customer service desk then you have an opportunity to save on costs. A member should be able to close their account through an easy unsubscribe process when they log into their account on the website. They also should be able to unsubscribe easily from any email you send them. If a majority of the unsubscribes are requiring your paid customer service staff then you must improve your member account closure processes. What does each account closure cost in customer service staff time? Calculating the ROI on solving this problem in conjunction with reducing support costs is easy.
What if 90% of the account closures was due to a Multiple Account violation? It is possible you have discovered an affiliate fraud issue, and that can cost you millions. Trust me I have seen this firsthand. Have you seen a spike in unsubscribes because of email bounce notices? Is there an issue with your email engines? Did you trigger massive SPAM complaints recently? Tracking this data can help you resolve some serious issues.
We also collected the date of account closure. This was important because we tracked members that voluntarily closed their account within 30, 60 and 90 days of registering their account. Were we delivering a different experience that was promised upon registration? Were we failing in executing the messaged goal of being a community member? In my situation we promised members they would generate points from completing market research studies. Once they met a threshold of earned points the member could exchange their points for cash or prizes. If a member was closing their account within 30 days we reviewed their study history. We found that many new members would never qualify for studies we invited them to participate. That drove more detailed research into community satisfaction in relation to study participation. New members that failed to qualify x times in a row generally closed their account. We began tests by sending a dummy study within the first week of being a member. Everyone qualified for the dummy study and we saw immediate results. Now members would tolerate not qualifying for studies beyond x. That increased their chances of qualifying for a study and forced us to improve the study invite method. Our new study invite target methods improved the community experience which in turn resulted in a positive community growth rate.
I hope you now see the value in tracking why members leave your community.