6 Predictive Analytics Steps to Reduce Customer Churn

Keeping customer churn in check is dire, as it typically costs more to acquire a customer than to retain customers. To help businesses retain their customers, data scientists and IT analysts should consider customer churn analysis to better predict customer behavior. Here’s a quick overview of the tools necessary to predict customer churn.  

1. Calculate Your Customer Churn Rate 

A good way to get started is to know your organization’s customer churn rate. This is a key performance indicator (KPI) used to measure customer attrition. To calculate this KPI, use the following formula: 

(Lost Customers ÷ Total Customers at Start of Chosen Time Period) x 100 

When calculating your churn rate, it’s important to be accurate. This often depends on the sales cycle for the product or service. This is also true for defining the target variable in your churn prediction model discussed below. While churn rates vary widely across industries and businesses, a churn rate greater than 7% is generally a sign of high customer dissatisfaction. 

2. Integrate Data 

Predictive analytics analyzes data to make predictions about future or otherwise unknown events. However, its accuracy requires lots of historical data to train a prediction model, both qualitative and quantitative. You’ll need to combine traditional transactional and account datasets with call center text logs, website logs, marketing campaign response data, competitive offers, social media, and many other customer data sources to develop a truly holistic understanding of past churn behavior.  

3. Build a Churn Prediction Model

A churn prediction model typically uses supervised machine learning to segment customers into two groups—the ones likely to churn and those likely to stay. Supervised means that the churn prediction model needs to learn from historical training data using target variables and features. The target variable is the dependent variable that you’re trying to forecast (the customer left or stayed). Features are input variables that are important to identify customers who churn, things such as customer account information, demographics, socio-economic data, products, and services owned, customer service interactions, and more. It’s important not to use too many features which can increase the chance of false predictions.  

In the training phase, machine learning algorithms will uncover shared behavior patterns of those customers who churned. Then, once trained, algorithms can check the behavior of future customers against these patterns – and point out potential churners. 

4. Assess Your Churn Risk Score 

A churn prediction model using a machine learning algorithm with a churn risk score helps understand the likelihood of customer churn. The model assigns each customer a churn risk score with some ranging usually from 1-100; the higher the score, the higher the likelihood the customer will churn.  

There are three churn risk groups:  

• High Churn Risk: 76-100.
• Medium Churn Risk: 51-75.
• Low Churn Risk: 0-50.

5. Segment Your Customers 

You can use customer segmentation to group customers based on shared characteristics to aid sales, marketing, and service efforts to prevent churn. Churn risk groups are an effortless way to target customer segments who are likely to churn. However, you may want to use machine learning algorithms for creating finely-tuned segmentations that produce better results.  

For example, you can use behavioral segmentation to group customers likely to churn according to behavioral traits, such as low product usage or poor customer service interactions. This knowledge enables targeted, personalized, and proactive retention efforts to prevent churn.   

Combining risk scores with value-based segmentation is also especially useful for understanding which customers to retain. Just as not all customers are equal, neither are all customer segments. Some groups of customers are high value, purchasing your products and services repeatedly, ordering large quantities, and generating large profit margins. Other customer segments are low-value, with larger customer acquisition costs, low-order volumes, few repeat purchases, and low profitability. due to price competition and discount demands.  

6. Use a Cloud Data Platform 

A cloud data platform offers the best foundation to execute predictive analytics for customer churn. The Actian Data Platform enables data scientists and IT to collaborate across the full data lifecycle with immediate access to data pipelines, scalable compute resources, and preferred tools. In addition, the Actian Data Platform streamlines the process of getting analytic workloads into production and intelligently managing machine learning use cases, such as predictive analytics to reduce churn. 

With the Actian Data Platform’s built-in data integration and data preparation, aggregation of model data has never been easier. Combined with direct support for model training tools, and the ability to execute models directly within the data platform alongside the data, it’s easier to capitalize on dynamic cloud scaling of analytics compute and storage resources. 

About Teresa Wingfield

Teresa Wingfield is Director of Product Marketing at Actian where she is responsible for communicating the unique value that the Actian Data Platform delivers, including proven data integration, data management and data analytics. She brings a 20-year track record of increasing revenue and awareness for analytics, security, and cloud solutions. Prior to Actian, Teresa managed product marketing at industry-leading companies such as Cisco, McAfee, and VMware.