There is no doubt that understanding why your clients left or are about to leave in the near future remains one of the most challenging issues for anyone in marketing. The complexity of this particular problem and the lack of sufficient data can make it almost impossible to accurately predict the likelihood of customer churn. With advancing digitalization, recent improvements in AI churn analytics have put completely new solutions within the reach.
According to HBR studies, reducing churn by only 5% can lead to a profit increase of almost 25%, so it definitely provides a solid return on investment. This is likely to bring added value to your firm, because it is almost always much cheaper to keep existing customers instead of looking for new ones and spending lots of money to get their attention. But before digging deeper into the matter of the churn problem, it is worth stopping for a while and thinking about how we define churn.
Definition of churn
Churn occurs when one of your customers decides to no longer spend money on your product or service. It is expressed as the ratio of the number of customers who left your company to the overall number of customers over a specific period. It seems easy to understand, but looking only at past data won’t tell us too much about the future.
What is even more important, it won’t give any insights that could be used as preventive action.
Approaches to churn prevention
There are two commonly used approaches to prevent or decrease customer churn.
Proactive churn prevention
This approach refers to customer actions that indicate that they may cancel their subscription or stop buying your product.
In fact, this is the type of churn that is most difficult to overcome, but it doesn’t mean that you shouldn’t give a try. Among possible solutions, some stand out, like encouraging loyalty and discovering possible causes of defection.
Simply put, offering a seamless customer experience should be treated as the main goal on the way to reducing churn rate.
Reactive churn prevention
Unlike the first type of churn, this refers to a situation when a customer forgets or doesn’t use your service or product temporarily.
Even though the churn event has happened or is about to happen, it doesn’t indicate anything about the customer’s satisfaction. In order to prevent such a situation or recover already lost customers, you should employ specific reactive actions. Especially in the case of data-rich industries, you should try to discover the signs leading to churn and take preemptive actions.
How to predict? How to prevent?
The best-performing solutions come with recent AI advancements. Regardless of the method used to resolve churn type problems, a few things remain unchanged. First of all, you would like to get an answer to which specific features are the most important from the point of understanding churn reasons:
- number of transactions
- number of days between transactions
- number of products sold
- number of days between transactions
and other related statistics.
Another type of question expected to be answered are whether low or high values of a certain feature are likely to have an influence on the decisions leading to churn.
Ultimately, the most important question is which client are most likely to leave your brand and what is the likelihood of such event?
For many years, it was somehow challenging to assess the impact of a feature on the output, especially in the case of mathematically complex methods. Luckily, thanks to a recent new wave in Machine Learning methods development, data scientists resolved this problem and introduced concept of SHAP values (SHapley Additive exPlanations).
With their help, you can explain the output of any machine learning model. There are two main ways to show the effects of features on the output of the churn model. The first is to plot the SHAP value of certain features and compare it to the value of the features in a dataset. This is a so-called features dependence plot.
The latter is to present the importance of all features on the same chart, called a feature importance chart, with regards to their SHAP values. This alternative also gives us the possibility to embed the level of certain feature value to this chart as the third dimension.
These two methods have proven to be very useful and provide powerful explanatory tools, but you shouldn’t forget about having a big picture of what has really pushed the customer to make the decision to stop using your product/service. First of all, it is always good to know the typical behavioral pattern of your customers. A very useful tool used in order to unveil more details is the well-known cohort table, which lets you figure out the percentage or number of clients who have returned to your product/service with regards to past previous months.
Typically, it is presented for the last 12 months. It is especially useful when you can preview certain clients just from the analytics and analyze trends within one solution in order to discover interdependencies. Apart from looking at the whole population of customers, sometimes it might be also necessary to look deeper into the features.
To do so, the best idea in most of the cases is straightforward—prepare a distribution histogram together with boxplot giving insights about the main statistics.
Once we put together all the information from the analytics, we should receive an extensive answer to the initial questions, like who is going to churn and what drives the customers to leave.
The issues connected to customer churn have always bothered businesses of all sorts, but nowadays it’s possible not only to gain insights about the past tendencies, but also estimate the likelihood of churn in particular customer groups and prevent it from happening.
Machine learning algorithms can deliver accurate predictions and help your business retain customers and eliminate the reasons for revenue losses.