Is there a reliable way to predict customer churn rate?

Modeling churn won't stop you losing customers, but it will give you more accurate information about what's happening, and it will help pinpoint when conditions improve or worsen. It will also give you a basis for doing experiments which try to improve your churn rate.

Here's a tiered approach to modeling churn.

0: For initial planning, assume 50%.
1: Look at the monthly totals for signups vs. cancels vs. inactives (improvement: uses your data).
2: Do regression analysis (improvement: better data + forecasting).
3: Do cohort analysis (improvement: stop lumping together people with dissimilar behavior).

Regression analysis will attempt to model a generating function which underlies your data. Your customer data is assumed to follow the trend the function describes plus some noise. This gives you a clearer picture than looking at raw totals, plus you can do some forecasting by assuming that the trend will hold for some distance into the future.

Cohort analysis tries to break up your customer data into major clusters. This can be against any number of dimensions, e.g. join date, account type, customer demographic, tablet vs. smartphone vs. desktop, screen resolution - really, any dimension which provides a reasonably consistent segmentation result, and where the customer segments will have different responses to your products or marketing.

You cannot avoid losing customers. There is a natural churn in every industry. However, churn prediction modeling can reliably indicate which individual customers are most likely to churn BEFORE they do – and thus take some action to try prevent it from happening. While most churn prediction approaches rely on static data and metrics, a more successful approach is based on dynamic micro-segmentation and monitoring of how customers migrate between micro-segments (based on changes in their behavior) before migrating to churn. This approach allows you to identify existing customers who are about to take the same route and thus churn. At this point, you can take action before they churn (e.g., send a particular offer or incentive that you know has worked in similar cases in the past for other customers). My startup’s founder has posted a detailed article on this approach – click here or search for Optimove churn prediction.