If you ask most charities which of their donors are leaving the most quickly and why, their answer will often be a straight up ‘I don’t know’. Acquisition is king, rather than retention. There is nothing inherently wrong with this standard approach, assuming the organisation in question can always top up its bucket with new donors. But what happens when acquisition becomes more difficult? All of a sudden the picture isn’t as rosy as it once appeared and, more of than not, a high churn rate is the culprit.
Despite all the data we might keep about our donors, delving deeper into a churn problem is rarely the top priority. However, it is only by understanding our churners that we can make informed decisions to reduce our churn rate.
Using Survival Curves to Fight Churn
Survival analysis, or time to event analysis, reflects the time until a participant experiences a particular event. In this case, the event of interest is donor churn. How we define a ‘churn event’ is important and includes ‘active’ churn events, such as cancellation calls, and ‘passive’ churn events, such as missing three monthly donations in a row.
The power of this analytical technique is that it allows us to compare differences in ‘survival’ between groups in order to find out how certain behavioural or personal characteristics affect churn. For example, how important are age, acquisition channel, and gender to donor retention? We can also investigate how these different factors interact to affect churn rates.
Exploring Churn Rates
Click on the characteristic you are interested in to find out how it affects churn. The faster the drop off from a Survival Probability of 1, the higher the churn rate and more urgent the problem. This data is sample data only, not reflective of any particular charity.
Dataro helps our charity partners to understand what factors are driving churn in their organisation, so they can develop strategies to improve donor retention. Key metrics include:
Acquisition Channel – We often find that Face-to-Face has the highest churn rate while mail or online perform better. How does it look in your organisation? This intelligence might help you to shift acquisition focus to a different channel, to improve engagement strategies, or to be more selective about which donors you acquire.
We often find that gender has only a small impact on retention, but that is not always the case. If you find a substantial difference in retention between genders, perhaps your acquisition methods or messaging is in need of review?
How much do your donors agree to contribute on signup? Results vary between organisations, but survival rates based on initial contribution can assist in refining your original sign-up request. Is trying to get as much as possible on sign up really the best strategy?
How important is a donor’s age when it comes to retention? The conventional wisdom is that younger donors churn more frequently, but is that really the case for you, and by how much? This modelling will allow you to identify ‘higher value’ targets by understanding how age plays into likely donor Lifetime Value (LTV).
There are many ways to investigate churn, but survival curves are a very useful tool to clarify which donor characteristics are really driving your churn rate. If you want to know more, Dataro provides a full historic churn investigation as part of our Ready-to-Launch Health Checks. These reports analyse your fundraising programs to identify the areas where Dataro’s machine learning propensity modelling approach will have the biggest impact.