How to Combat ‘Non-Subscription Churn’ with Predictive Analytics
Simply put, non-subscription churn happens when users or customers, who can end their relationship with your business at any time, leave your website or sales funnel. These types of customers may gradually reduce their purchase frequency over time, or they may all of a sudden never buy again.
Companies in nearly every industry have to address this type of churn because it has the power to plateau the growth of any businesses even if that business is gaining customers quickly. The most successful companies address it by building predictive models that accurately predict churn; then they take action by building targeted marketing campaigns around preventing it or by making product changes that combat churn.
Following a seven-step procedure, companies can deploy predictive analytics to identify potential ‘churners’ and then take steps with short term marketing campaigns to re-engage with them
How will your specific business define churn? This step is crucial – defining a churn period that is too long risks creating predictive models with artificially low churn rates, not capturing enough people and defeating the purpose of predictive modeling. But defining a churn period that is too short makes it difficult for marketing teams to evaluate churn prevention campaigns because they ultimately can’t distinguish between organic actions (users or customers who would have come back anyway without intervention) and effective campaigns.
It’s also a good idea to do basic analysis upfront (unsupervised/clustering) to decide which users should even be considered in the churn analysis. For example, if someone used the product or service only one time, are they considered a churner after that? Or is there some minimum threshold after which a user should be considered and included in churn analysis?
The minimum data required to predict churn is simply some form of customer identification and a date/time of that customer’s last interaction. This data, though not incredibly detailed, would allow you to build models to predict churn at a basic level.
However, the reality is that adding additional data on top of this minimum data set is recommended and highly encouraged. The more data included, the better the churn predictions will be, so if available, also include things in the dataset like static demographic information about users, details on specific types of user actions, etc. The more sources, the better.
Remember that this step of the process can account for up to 80% of the total time spent on the project, so don’t be discouraged as you get your data into a useable format. Take time to ensure you understand what all the different variables in your data mean before moving on to cleaning up different spellings or possibly missing data to ensure everything is homogeneous.
