Structuring Member Churn Data to Predict who Stays/Leaves Non-Profit

timo — Mon, 19 Mar 2012 16:29:58 GMT

Structuring Member Turnover Data for Non-Profit

We are conducting an analysis to predict member turnover or churn – which members will leave – over a one year period.

Members may be already on board at the beginning of theyear, or they may join during the year. At the end of the year, T2, their Status is either Stay or Leave. Length_of_Membership (measured in months)will be a strong predictor, as we know that new members have a higher leave rate. Status is teh Target variable.

The challenge for us is to structure the data in a way that does not overstate the impact of Length_of_Membership. Over a year, Stayers will have a Length_of_Membership through the end of the period (year). Leavers will have a Length_of_Membership through their LeaveMonth.

Join Month	T2_Status	Leave_month	Length_of_Membership
May	Stay		9
May	Leave	July	3
Jan	Stay		12
Jan	Leave	July	6
Feb	Stay		11
Feb	Leave	April	3

The problem is when we look the proportion of Leavers at the end of the period by Length_of_Membership we may be overstating the proportion who leave at a given Length_of_Membership level – because, for example, Stayers with Length_of_Membership=3months will be just who join in October (3 months prior to T2), while Leavers will be all those who Left with Length_of_Membership =3 months at any time throughout the12-month period.

Length_of_Membership is an important predictor, but our current approach to structuring the data overstates its impact. We could cut the time interval down, but that reduces the incidence of Leavers and presents a new set of challenges (as our data set is in the range of n=10000, with a 10% Leave proportion, and we are already oversampling). Using cohorts seems to make this overly complicated…

Any suggestions/perspectives much appreciated.

Timo

Structuring Member Churn Data to Predict who Stays/Leaves Non-Profit

Doc_Duke — Mon, 19 Mar 2012 17:01:30 GMT

This is not my field, but this google search had lots of interesting articles

modelling churn site:sas.com

Doc Muhlbaier

Duke

topic Structuring Member Churn Data to Predict who Stays/Leaves Non-Profit in SAS Data Science

Structuring Member Churn Data to Predict who Stays/Leaves Non-Profit

Structuring Member Churn Data to Predict who Stays/Leaves Non-Profit