While many applications of business intelligence require substantial technical expertise and resources, use cases do exist that organizations can apply right away. Measuring and preventing churn, or customer attrition, is perhaps the most notable of these applications. Using churn as a business metric isn’t new. Many large service sector companies such as wireless phone carriers, Software as a Service companies, and banks use advanced churn modeling to find out why their customers leave.
These organizations understand that limiting customer churn is central to maintaining their profit margins. Ernst & Young’s 2012 Global Banking Survey reported that the number of customers who changed banks increased from 38 percent in 2011 to 45 percent in 2012, resulting in millions of dollars being lost and gained between organizations. Any decent business textbook will expound on the necessity of keeping current customers happy. That’s because the acquisition costs of converting new customers far outweigh the cost of maintaining current customers, as does keeping them at their current level of service. Churn helps forecast customer behavior by describing the likelihood that a customer will stay loyal to a company, or move to a competitor.
Due to their importance, creating churn models has grown increasingly sophisticated. To increase the accuracy of their predictions, organizations are integrating predictive analytics into their data strategy. This lets them not only understand why customers leave, but allows them to identify customers who are on the verge of leaving, and respond with marketing tactics to change their behavior.
The Specifics of Churn
Churn has two main divisions: voluntary churn and involuntary churn. As you may expect, involuntary churn signifies the loss of a customer due to forces beyond the company’s control (such as death or natural disasters). Voluntary churn is of the greatest interest to businesses because they can control the factors of influence. But churn doesn’t only refer to a customer’s decision to take their business elsewhere. It also entails a subset known as partial churn, which describes customers who downgrade their services, fall behind on their bills, and generally become less profitable customers.
Past churn models have shown that customers rarely leave because of one singular event. More often their disenchantment with an organization grows over time due to a buildup of unsatisfying and frustrating events. By the time a competing offer comes around, customers have been ready to jump ship for some time.
That’s why many large financial institutions collect and store data on every interaction their organization has with a customer, such as calls, web interactions, transactional data, credit card histories, social media interactions, and even in-person bank visits. Banks then store these mountains of data, both structured and unstructured in nature, in data warehouses to query with analytics software.
While not all businesses have access to such expansive data collection methods, certain standards should exist for data used in developing a churn model. A customer id, event id (describing the type of interaction between the customer and organization) and timestamp form the minimum amount of detail necessary for churn, while 30-90 days represents a minimum timespan for gaining an understanding of customer behavior.
Predictive Analytics and Proactive Retention
Service industries in particular have an advantage in gathering transactional customer data due to the inherent business model. Gathering such data is necessary to determine the casual factors that lead up to a churn event. Once the data has been collected, companies can use predictive analytics to establish connections between a dependent variable and a churn event.
Often, the answer lies in the interaction of several dependent variables rather than just one. Finding the connections between these factors (which could include service locations, prices, customer service wait times, etc.) falls to predictive analytics, a subset of data mining software that uncovers patterns in historical and current data to predict future events with a significant degree of accuracy.
Predictive analytics software most commonly use logistic regression to uncover the various paths customers take to a churn event. Logistic regression analyzes data to find a relationship between one or more independent variables and a dependent variable that can only have two outcomes.
In the case of churn, the independent variables would be the different interactions that may cause customers to leave, and the dependent variable would be whether the customer does in fact stay or leave.
Once a predictive analytics software segments customers based on churn probability, business can then practice what’s known as proactive retention. Basically, organizations can formulate retention campaigns to prevent current customers from leaving, or reducing their subscription services.
Given the precise customer information your model has produced, you can create more targeted and effective marketing campaigns. While predictive analytics can’t forecast the future with 100 percent accuracy, this software does generate probabilities about future events that are far superior to any previously available technology.
The goal of creating a customer churn model is to continually reduce your business’s churn rate until you realize negative churn. While this is of course more easily said than done, the ROI of utilizing a churn model properly can be substantial. For a SaaS organization that depends on monthly reoccurring revenue, using churn properly can result in much higher growth. One simple model predicts a realization of -2.5 percent churn equaling $180,000 per month.
While proper data collection will always remain a prerequisite to good analytics – note the old adage “garbage in, garbage out” – creating a churn model with predictive technology offers businesses an incredibly powerful tool for understanding and anticipating customer behavior.
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