Customer Churn – How To Use Artificial Intelligence To Easily Win The Fight

If it seems like you’re constantly battling high customer churn rates, read below to learn how you can leverage machine learning, a subset of artificial intelligence, AI, to reduce them. Effectively managing customer churn rates or customer attrition can mean the difference between profit and losses for many companies.  

Studies have proven it could cost greater than five times more to acquire a new customer than retain an existing one. Some debate whether this statistic is relevant if the customers you retain are not the right customers. In a previous article, I discussed the importance of using AI-embedded personalization methods to meet your customer’s needs. Personalization becomes even more beneficial once you’ve identified those valuable customers who are more likely to churn. 

What is machine learning?

Machine learning involves the ability of machines to recognize and learn from patterns in customer data. The machine adjusts its actions or makes predictions based on these patterns without human intervention. In churn prediction, the patterns it recognizes are the similar characteristics of customers who stopped renewing, buying, or engaging with a company’s services.  The next step is to yield an assessment of the immediate or future risk of current customers churning with similar characteristics. The next step is to segment those customers.

By implementing machine learning, not only can you identify which customers will churn, but you can also identify which ones have a higher probability to churn and the timeframe in which they might do so. You can use these insights to establish customer retention strategies. 

Data is key!

Churn prediction data used by companies differs depending on the industry to which the company belongs. The customer’s data can come from the company’s customer relationship management system, google analytics, feedback on social media, and surveys. 

Algorithms that predict churn rates could use thousands to millions of data points. The following are some of the types of customer data included in a prediction model: 

  • Usage behavior
  • Responses to promotions
  • Customer service interactions
  • Payment history
  • Length of contract

Is Churn Prediction Using Machine Learning Right For Your Company?

Companies who wish to predict churn with machine learning must consider the best method to do so. Larger companies with significant financial resources and data may already have human resources, such as data scientists, machine learning engineers, and other analysts, currently solving business problems with AI. With an existing department, your only goal may be making the business case for executing churn prediction. 

A medium to a smaller-sized company may need to solicit a vendor’s churn prediction software. Of course, ROI is important regardless of the size of the company. Some platform vendors state their customers experience an average of 3-5% increase in retention rates when using their services. 

Below are some factors to consider to help you determine if predicting churn with machine learning is right for your company:

  • Number of current customers and lost customers
  • Number of customers with high customer lifetime value (CLV)
  • Average value of contracts/subscriptions
  • Amount of customer data you possess

Telecommunications Industry Example of Churn Prediction

Companies have been using machine learning to predict customer churn for over fifteen years. A telecommunications company used account information, such as length of service, contract type, payment history, churn history, and types of value-added services the customers possessed as data points. It coupled that with demographic data to develop a strategy for predicting and preventing churn.

This early example from the telecommunication industry revealed customers who paid for services individually were more likely to churn vs. those who’d bundled them. It also indicated higher churn rates for customers with daytime overage charges higher than $45 versus those less than $45.

Impact of Churn Prediction

By analyzing insights about your customer’s behavior at scale, your team can offer better bundle packages, intervene with discounts, and offer more attractive contracts. Proactively addressing customer’s needs will reduce the likelihood of churn. If you’re ready to use machine learning to reduce churn but not sure of what to look for in a vendor or what questions to ask, contact us at AI Business Partners.

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