4 Reasons To Use Machine Learning For Churn Prediction

No business is immune to the risk of losing customers, but is there more you could be doing to retain them? A monthly customer churn as low as 5% doesn’t seem like a huge loss, however, compounded annually, a business could be losing half of their customers. A lost customer is costly to replace when acquiring a new customer is 5 times more expensive than retaining an old one. Churn has become one of the biggest challenges, both for companies and the Product or Customer Success teams responsible for retaining them.

Predicting if a customer is going to stop using your app/product/service has traditionally relied on business rules imbued with market knowledge. These rules are based on managers experience rather than on thorough data analysis. Individuals and teams can understand specific touchpoints that result in negative interactions, however, it becomes difficult for a human to analyse historical data from lots of different touchpoints (including Omnichannel), understand which combinations of factors caused the customer to churn, or to group customers that are more likely to churn in the future.

An alternative to this traditional approach is to harness the large amounts of data collected on customers and train Machine Learning models to automatically learn those rules. In other words, instead of handcrafting limited rules that help us predict which customers will churn, organisations can delegate this traditionally rule-based task to machines by providing them with lots of relevant data and examples of churners and non-churners and let the data models speak for themselves.

Churn prediction solutions with AI help business growth by re-engaging and retaining customers likely to churn.

Marcia Oliveira explains 4 reasons why Machine Learning for Churn Prediction is more efficient than traditional methods.

Adopting Machine Learning for Churn Prediction has several advantages over traditional business rules:

1.

Machine Learning relies on finding patterns and relationships in large amounts of data, the rules discovered by the Machine Learning model are guaranteed to be supported by evidence instead of intuition/hunches.

2.

Unlike humans, who are limited by the number of variables/factors they can account for when crafting their business rules, Machine Learning algorithms can process and extract patterns from many variables, which results in (typically) more complex and comprehensive rules.

3.

Assuming there is good quality data available, Machine Learning is able to learn highly accurate rules in a much shorter time when compared to a human which usually needs a significant amount of experience and domain knowledge to devise accurate rules, i.e., the ROI tends to be higher for Machine Learning.

4.

A fourth advantage is that Machine Learning is able to timely detect concept drift and adapt the rules accordingly, thus being more adaptive to changes, i.e., if the accuracy of the churning predictions start to degrade over time, Machine Learning quickly detects this and adapts the rules to the new scenario, ensuring the prediction of churners is reliable for the business.

In short, while there are tools that can help you make sense of your data, AI enables you to apply learnings before it’s too late, ensuring a high customer retention rate and improved ROI.

Originally published at https://www.skimtechnologies.com on April 16, 2019.