Customer churn has been a growing problem across industries. Businesses are losing millions of dollars every year to cancellations, and it is affecting their balance sheets.
In recent years, the advent of predictive analytics and its initial success in retaining customers has given hope to leaders of these companies. For example, video streaming giant Netflix recently claimed to have saved almost $1 billion by retaining customers using predictive algorithms. Successes like this have resulted in the rising popularity of predictive customer churn models over the last decade.
In such an environment, many retention leaders are focusing on developing churn prediction models with a much-focused agenda of identifying which of their customers are more likely to cancel. With so much visibility and attention, a data scientist would work on just one churn model with a focus on improving its value by measuring how well the outcome is predicted and in customer retention scenario, it would be how many and how accurately the model has identified at-risk customers.
Why Customer Churn Still Isn’t Slowing Down
In 2018, the churn rate in most industries across the U.S. was greater than 20%. Why are companies still struggling to contain churn despite putting in place an advanced churn prediction model and investing in retention programs?
The reason lies in how machine learning models work. It is important to understand that a machine learning model can answer only one question at a time. Most predictive churn models are effectively focused solely on producing a more accurate and refined classification of customers between “at risk” and “not at risk.” Apparently, this classification alone is not sufficient to actually reduce churn.
Why A Single Churn Model Is Ineffective
1. No insight into the context of risk
The company may be aware of who its high-risk customers are, but it won’t know from the model output why a particular customer would want to cancel in the first place.
2. No clarity about customer value
A churn prediction model doesn’t tell you which of the identified at-risk customers is more valuable. In this scenario, your retention agents end up giving costly offers to low-value customers.
3. It doesn’t allow timely and proactive engagement
Marketers do not know how much time they have before a predicted high-risk customer will cancel. This will hinder their planning on who to target first.
4. The lost opportunity of customer winback
Winning back lost customers is more profitable than new customer acquisition. The single model approach will only predict the risk status of active customers and won’t even consider winback chances of recently canceled customers.
Multiple Predictive Models Approach For Customer Retention Boost
Each customer’s journey is unique, and multiple factors drive a customer’s desire to cancel. The objective of predicting churn analytics should go from identifying who is most likely to cancel to understanding finer and subtler details like why customers will cancel, when will they cancel, how valuable these customers are, what can be done to save them, what offers might work for them and which of the canceled customers can be won back.
The right approach to extensively predicting customer behavior is to have multiple predictive models, each predicting different dimensions of customer behavior.
Typically, customer retention needs and goals vary from business to business. However, here is a quick plan that I have seen working in most cases:
1. Identify the right metrics for optimization
Keep an eye on the metrics you would like to improve. For example, if the intention of the company is to increase profitability through retention efforts, then the model must identify high-value customers with a higher rate increase propensity. Identifying the right metric will help to measure the model’s impact.
2. Take an inventory of available data
Predictive models are data guzzlers. In other words, the more data you have, the higher the accuracy of the predictions. Typically, most companies have more customer data than they realize. By taking stock of your existing data sources, you are not only improving your model’s output but also putting the assets that you once considered redundant into good use.
3. Create models based on multiple use cases
Use existing data science capability or engage an external data science company that specializes in deploying machine learning for customer retention to create the models needed to create the multidimensional view of churn risk and opportunities.
4. Focus on insights as well as actions
Deploying multiple models helps you in two ways. If one model helps you to predict at-risk customers, then another model can be used to uncover opportunities to save the customer.
5. Create a framework
Ensure that all the models work in tandem and integrate the right insights with the right customer touch points to drive timely retention actions on a large scale.
Customer churn dents your balance sheets by millions of dollars in lost revenue every year. It is high time for customer-facing businesses to realize that only predicting churn is not sufficient to prevent churn. Deploying multiple predictive models on available customer datasets can help not only in predicting churn, but also in uncovering opportunities to mitigate the churn risk.
This article was published on the VOZIQ blog.