Benefits of Customer Churn Prediction Using Machine Learning

Because of a highly competitive market and a wide range of products/services (Internet, television, mobile networks, etc.), such giants as AT&T, Sprint, Vodafone, and T-Mobile have already utilized machine learning for reducing churn rate. Today even smaller companies and startups try AI applications right after their services enter the market.

In the wireless network segment, the average monthly rate of customer churn is 2.2%, whilst the yearly one is 27%. Interestingly, the annual cost of client attrition is $4 billion in the U.S. and Europe and around $10 billion globally.

Supposedly, not less than 1.5 million customers would keep doing business with the same companies if the correct prediction was increased by the rate of 1%. It would also mean a $54 million benefit annually.

Customer churn happens when a client stops buying a retailer's products, avoids visiting a particular retail store, and prefers switching to the competitor. From a financial perspective, retail businesses always need a sure-shot strategy to control customer attrition. Measuring and reducing churn rate is one of the most important retail business metrics since it is used to reflect customers' responses towards the product, service, price, or competition.

Even the very simple ML algorithms are capable of capturing, analyzing, and representing the data while providing specific forecasts based on the patterns of conversions, the number of re-visits, and purchases done by a certain customer.

The recent failure of an American retailer Toys R Us proves that modern businesses can't ignore insights provided by AI. If the company had managed to build and implement a churn control pipeline in time, it wouldn't have announced bankruptcy.

Churn rate is one of the key performance indicators for subscription-based businesses. Back to the roots, this model was invented by the book publishers in the UK in the 17th century. Tech giants such as Google, Amazon, Apple, and Netflix have been using the same strategy until today, yet with the help of machine learning for customer churn prediction. For instance, when Netflix finds out segments of at-risk subscribers, the company starts proactively engaging them with special discounts or offers. There are also hundreds of middle-sized companies that just started implementing AI for the attrition forecasting.

The companies, utilizing subscription business models, used to implement machine learning to identify users who are not satisfied with the current services. This is how they get an opportunity to address customer issues before it's too late. It is important to find at-risk users ten or eleven months before their renewal. In this way, marketers and salespeople will have time to contact those users, specify the pain points, and create a flow to help them realize the value they get from the service.

This is the case when financial institutions will be interested in a position under the curve. If you already have a machine learning model that is able to forecast customer churn with an accuracy of 85%, it may seem to be good enough. But what if that accuracy rate was 87% or 88.6%? Don't underestimate data scientists who train machines by assigning different classification tasks.

For example, UBS, the largest Swiss bank, utilized ML algorithms to avoid client churn after HSBC, its main European competitor, launched several ads campaigns aimed at billionaires who were already with UBS for a long time.

In marketing, machine learning can assist in segmenting customers into different groups of attrition risk. Once algorithms work, a particular churn ratio will be forecasted for each data point in each group.

Thus, marketers can focus on certain groups and certain customers, identify the same pain points, and consider solutions based on the ML analysis.