Machine learning (ML) applications: customer churn prediction

TLDR:

ML tools make it possible to predict and prevent churn before it occurs. Churn prediction is used to identify areas of improvement and maintain happy customers.

Outline

• Intro

• What is churn prediction?

• Use cases

• Fastest way to build a churn model

• Closing thoughts

Intro

The value of customer retention should be a high priority for all businesses. Numerous studies have shown that the cost of customer acquisition to be 5 times higher than customer retention. Traditional marketing efforts often target the former, ignoring the value of maintaining their current customers. In his research, Fred Reichard of Bain and Company found a 5% increase in customer retention can increase profits by over 25%.

Understanding how to retain customers is one of the most important business metrics. Historically, research and implementation regarding customer retention has been an expensive process. Machine learning (ML) has changed this by introducing a quick way to predict customer churn.

What is churn prediction?

Churn is the metric that measures the rate at which a business will lose customers.
Examples of churn are:

• Unsubscribing from a service
• Shopping with a competitor
• Not renewing of a contract

Churn can be measured yearly, quarterly, or monthly, but for most SaaS companies whose services renew every month, it is the latter. The method for calculating churn rate is very simple: dividing the total number of customers churned at the end of the month by the total number of customers at the beginning of the month.
Historically, churn prediction has not been as simple. Churn prediction is about identifying customers who are likely to churn. Perfecting this prediction process allows businesses to leverage reliable information about their current customers, giving them information to build effective customer retention and marketing strategies. The ultimate goal of predicting churn is to prevent churn from occurring.
The recent prevalence of data that companies have access to has allowed them to use data science and machine learning to build extremely accurate churn prediction models. This gives decision makers data-driven metrics, not assumptions, to shape their customer strategy off of.

Use cases:

Prediction models built with machine learning are reflective of all the data they’re given, making each churn prediction unique to the business’s needs.
Any business with customers and data has a use for building a churn prediction model. For a consumer goods company, a churn prediction model could be used to decipher how successful a coupon sponsorship was.
When offering promotional deals on websites like Groupon, many companies struggle with attracting customers that won’t churn after a one-time use of a code. Using a model that can predict the churn rate of these promotional deals gives businesses insight on how much of a discount they can offer, if at all, to attract an influx of customers to their website without impacting their bottom line.Building a model off of customer and product details, discounted rate, and churn rate of people after a promotion gives business data on how to better shape their marketing strategy in the future.
The rise of subscription-based services has also led to the widespread need for churn prediction models. Take Spotify, Apple Music, and Netflix–all subscription-based services who rely on renewal to maintain profit. Identifying traits of customers that renew their subscriptions, and in-turn, traits of those who don’t give these companies are important to business strategy. At Netflix, building a churn prediction model with customer data, type of content, number of new releases, and a competitor’s releases would allow Netflix to see specifically what type of media users want to consume, and how the lack of certain media causes users to churn. Netflix can then build their marketing and business strategies off of the content that keeps their users subscribed.

Fastest way to build a churn model:

The fastest, and most effective way to build a churn prediction model is using ML. With ML, a much greater amount of data is able to be used and analyzed, leading to the most accurate and comprehensive results.
The first step to building any model is to gather the right data. The more data a company has about their customer, the better the model will become in predicting churn. Data for a churn model should include at least customer data, product data, and purchase history.
For example:

• Customer ID
• Zip code
• Frequency of purchase
• Types of products purchased
• Last purchase date
• Marketing emails opened
• Coupon codes used

Frequency of store or online visits
There are countless other features that businesses can plug into their models to get more informed insights on customer churn.
Once data has been collected, it must be cleaned and analyzed to ensure that the model has the right information to run off of. To learn how to manually clean your data, get started here: https://www.mage.ai/blog/churn-prediction-p1-gather-clean.
For building churn models, using an AI SaaS tool can be the quickest and most effective way to get accurate predictions.
Mage’s churn prediction model first begins with a customer uploading their data. After that, Mage will offer suggestions on ways the model can be improved by removing or adding columns, shifting rows, or applying various transformer actions.
Once training has been completed, a churn prediction model will be pushed out for deployment. Quickly generating a model for churn allows for the quick implementation of any improvements that need to be made to retain more customers.

Get started here to build a churn prediction model!

Closing thoughts

Any business that has customers can and should use churn prediction to prevent churn from occurring. While generating new customers is important for growth, ensuring that existing customers stay is essential to the longevity of any successful business. The ability to predict when a customer is likely to churn allows businesses to look at previous churn not as a metric of failure, but as an opportunity for improvement.