Given that it costs five to 10 times more to acquire a new customer than to retain an existing one, it seems obvious that all businesses should be engaged in some level of churn prevention.
But many businesses’ strategies result in addressing customer churn only once it’s too late, which is unfortunately an expensive (and generally ineffective) method. The alternative is churn prediction. If you can predict with a high level of accuracy which customers are likely to leave, you can capture them at that critical decision period before it’s too late.
Sound too good to be true? It’s not! Using predictive analytics to anticipate customer churn is actually a very accessible way for even smaller and less experienced teams to get started in the world of machine learning - it's as easy as following the seven fundamental steps to completing a data project. Yet many businesses still aren’t doing it.
Here are the top five barriers cited as a hinderance to starting a churn prediction project and tips to overcome them and get started.
1. Admitting that churn is an issue
The first step to resolving a problem is admitting that there is a problem. And for some businesses, this is a hurdle to overcome if they are gaining customers quickly - it’s hard to see why churn should be addressed when the overall number of users is rising. But the reality is that churn has the power to plateau the growth of any businesses even if that business is gaining customers quickly.
2. Settling on the right definition of a user or customer
Before deciding what it means to churn, you need to decide when users or customers are eligible to even be considered users or customers. If all they do is sign up for an account but never use it, have they churned? Or were they not engaged enough to even be considered a customer in the first place?
3. Settling on the right definition of churn
The next step is deciding after what period of time of inactivity (or waning activity) constitutes churn. 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.
4. Getting data
This step is actually much easier than most businesses think, because the minimum data required for a churn prediction model is simply some form of customer identification and a date/time of that customer’s last interaction. Almost all businesses already have this data available. Of course, you can develop more accurate and robust predictions by adding features with more data, but this shouldn’t be a barrier to getting started.
5. Getting predictive
If you’re a beginner when it comes to machine learning and algorithms, it shouldn’t stop you from predicting churn to improve your business. You can use a tool like Dataiku Data Science Studio (DSS) to run open source algorithms to predict churn in a clickable interface without having to write any code. Or, if you're already an expert, code your own models in Dataiku and leverage its ability to deploy those models quickly into production.
With these barriers out of the way, you’re ready to really dive in to a churn prediction project. But once you get started, there are still some pitfalls - like making sure to turn your predictions into actionable results and avoiding predicing churn only as a one-time, one-off project. For more on how to use the results of churn prediction and details on how exactly to execute, refer to our how-to guidebook. It contains tips and complete step-by-step instructions for beginners as well as more advanced walkthroughs: