How to predict Customer Churn
Churn prediction consists of detecting which customers are likely to cancel a subscription to a service based on how they use the service. It is a critical figure in many businesses since it is often the case that acquiring new clients costs more than retaining existing ones.
Once you identify the customers that are at risk of churning, you should know exactly what marketing action to run on each individual customer to maximize the chances that the customer will remain a customer.
Since different customers exhibit different behaviors and preferences, thus churning for different reasons, it is critical to proactively communicate with them in order to retain them in your customer list. This means knowing in advance which marketing action will be the most effective for each and every customer.
Why it is so important?
Customer churn is a common problem across businesses in many industries. If you want to grow as a company, you have to invest on acquiring new clients. Hence, every time a client leaves it represents a significant investment lost. Both time and effort then need to be channeled into replacing them. Being able to predict when a client is likely to leave and offer them incentives to stay can offer huge savings to a business.
As a result, understanding what keeps customers engaged is extremely valuable, as it can help you on developing your retention strategies and roll out operational practices aimed to keep customers from walking out the door.
Predicting churn is a fact of life for any subscription business and slight fluctuations in churn can make a significant impart to your bottom line. Thus, it makes sense to put some effort on quantifying if and when a customer is likely to churn. In order to do so, we should predict the answer to the following question: “Is this customer going to leave us within the next X months?” There are only two possible answers here, 'yes' or 'no'; a binary classification task.
What are the main challenges?
Churn prediction modeling techniques attempt to understand the precise customer behaviors and attributes which signal the risk and timing of customer churn. As you understand, predicting churn is not a walk-in-the-park task so below I mention just three points to consider while you are working on a task like this.
To succeed at retaining customers who are ready to abandon your business, Marketers & Customer Success experts must be able to predict in advance which customers are going to churn and set up a plan on what kind of marketing actions will have the greatest retention impact on each customer. Hence the key here is to proactively act and engage with these customers. While simple in theory, the realities involved with achieving this “proactive retention” goal are extremely challenging.
Accuracy of the technique used is obviously critical to the success of any proactive retention efforts. If the Marketer is unaware of a customer about to churn, no action will be taken for that customer.
Special retention-focused offers or incentives may be provided to happy, active customers, resulting in reduced revenues for no good reason.
Your churn prediction model should rely on (almost) real-time data to quantify the risk of churning, not on static data. Although you will be able to identify a certain percentage of at-risk customers with even static data, your predictions will be slightly inaccurate.
How to get started?
To do the actual predictions, you can build an effective model using machine learning and Python coding. Scikit-learn library is great for accomplishing this since it contains a host of common machine learning algorithms. You can check my Github account to see an example for predicting churn in the telecoms industry. I have used one of the most popular and easy to understand algorithms, the random forest.
Bringing it all together, predicting customer churn is important only to the extent that effective action can be taken to retain the customer before it is too late. The ability to predict that a customer is at a high risk of churning, while there is still time to do something about it, represents a huge additional potential revenue source for every online business.
Have you conducted a churn rate analysis? Get in touch and tell me about your experience!
