Customer Churn Prediction using Machine Learning
Learn about customer churn prediction in insurance and how machine learning can help you reduce the churn rate.
For an insurance company, signing a new contract is only half the battle. Then go customer retention and loyalty, which are challenging to build but necessary for the long-term success of the company.
Customer churn can have a great impact on the insurer’s bottom line. A 5% monthly churn rate might sound pretty innocent to some people. Yet, compounded yearly, the insurance business may reveal that it’s losing a significant amount of its policyholders.
That’s why reducing customer churn is worth prioritizing, and the most reliable way to do it is via machine learning (ML). In this article, we will walk you through customer churn prediction with the help of machine learning technology, discuss its opportunities and stages of implementation.
What is customer churn analysis in the insurance industry?
Before going over to customer churn reduction strategies, let’s make sure we stay on the same page and understand the term of customer churn right.
In insurance, customer churn refers to the situation where an existing customer stops using the services of the insurance company. A churn rate is usually measured in percentage, and one of the most popular ways to calculate it is to use the formula:
Customer churn is tightly linked to the concept of Customer Relationship Management (CRM). Its idea is to build and maintain customer loyalty and long-lasting relationships with customers since the costs of acquisition are usually much higher than those of retention.
In the insurance sector, policyholders have to be satisfied with the services provided to stay loyal. This way, customers would probably maintain different policies within the same insurer. However, if another insurance company offers a better policy for a more optimized price for insurers’ customers, or if a policyholder has a few claims still not settled or is dissatisfied with the service, they can leave the company and choose the competitor.
For example, health insurers in the Netherlands are especially concerned with retention and customer churn reduction. Basic health insurance has been mandatory for all citizens here since the early 2000s, but the government regulates the price. So the coverage and its price are practically the same from one company to another. This creates a dynamic and competitive environment where insurance companies put a great effort into retaining customers. Insurers try to keep policyholders satisfied and loyal so they don’t switch to competitors.
3 ways churn affects your insurance business
Well, churn is not good. But what are the specific negative effects of customer churn on the insurer’s performance?
First of all, we’re speaking about a heavy financial burden as churn is the major cause of lost revenue. There is also extra pressure on the team that has to make up for the reduced income, and the only way to accomplish this is to engage and acquire new customers. At the same time, Forbes warns businesses that winning a new customer usually costs five times more than retaining the existing one.
Secondly, an insurance churn rate can tell you a lot about the quality of customer service. The PwC survey proved that even the most loyal customers are not ready to tolerate the brand if they have had several bad customer experiences with the company.
Read related case studies on how to improve customer service by enhancing cold calling effectiveness and improving lead quality in insurance.
Thirdly, customer churn is a great indicator of the insurer’s growth potential. The company can compare its churn rate to growth rate (the number of new customers) and see whether it’s moving in the right direction. If churn is becoming higher than the growth rate, it’s a red flag for an insurer to review its retention strategy.
As obvious, reducing the churn rate is important for insurers to stay competitive and get consistent revenue streams. For some reason, most insurers choose a reactive approach to address customer churn. They make changes and adjustments to their strategy and analyze the outcomes at the end of the month, calculating the churn rate. It could be more effective, though, to predict customer churn in the insurance industry using machine learning and take proactive measures against churn in the very beginning.
Why use machine learning for churn prediction?
Churn prediction with the help of machine learning
Insurance companies usually have at their disposal lots of data as they’re used to store many attributes for the policies they underwrite. Imagine an insurer that can reuse this information: use the data to predict customer churn; identify at-risk policy keepers; and take action to gain back the trust and loyalty of potential churners.
This is a real-life scenario where machine learning is used for customer churn prediction. Based on the current and previously gathered data, churn prediction models aim at detecting early churn signals and recognizing so-called at-risk customers, i.e, those that are on the verge of leaving the insurer.
For example, the scholarly article “Case Studies in Applying Data Mining for Churn Analysis” tells about customer churn prediction using a decision trees learning model. In one case discussed, it was said that the model predicted 16 potential churners out of the 20 customers that eventually left the company.
Another study specializing in vehicle insurance titled “Predictive Churn Models in Vehicle Insurance” warns that precision and sensitivity in insurance churn prediction are more difficult to achieve as compared to, for example, telecommunication. An accuracy of 70% or more is still possible to gain, especially if auto insurers choose an artificial neural network.
The real secret sauce for using ML in customer churn prediction is an opportunity to discover the hidden factors behind the insurer’s churn. ML algorithms can help to identify what’s specifically causing churn within your company, such as interactions with customers or the time factor, for example, after what period users stop using the service.
Read also about intelligent underwriting and the benefits of ML in insurance underwriting.
Benefits of using ML for churn prediction
If insurers need a few more reasons for churn prediction using machine learning, review the next ones. ML technology is good for churn prediction because it can:
Track at-risk customers: As said, ML can help insurers analyze the behavior patterns of their policyholders and indicate the probability of churn. A company will be able to predict potential churners beforehand so as to interfere and reduce the overall probability of churn.
Know your pain points: There are so many areas where an insurance company might need improvements, such as a long renewal process, affordability, or system errors. ML algorithms will help you track the most likely causes of customer churn so you can eliminate them and retain more customers.
Optimize services: An insurer will also know what matters most to its customers and what they’re looking for in its services. The company could use these insights to improve its customer service.
Increase profits: Churn prediction helps insurers retain customers better and, thus, prevent reductions in revenue. A company keeps its revenue streams at a steady level, which is good if we recall that retention is cheaper than customer acquisition. Furthermore, an insurer can use the obtained insights from its data to improve its cross-selling and upselling practices.
Reducing customer churn is not the only use case of ML in insurance. Refer to 5 Applications of Machine Learning in Insurance and Best Use Cases to explore other ways how ML can be useful to your insurance company.
Implementing machine learning for customer churn prediction
1. Goal definition
Before an insurance company moves forward with building an ML solution, it should understand its existing problem and define the insights it wants to get from the analysis, i.e. the main goal. For instance, data scientists that the company will work with will need this information to understand what type of ML problem they are going to solve: classification or regression.
Although this might sound complicated a bit, here’s an explanation:
Classification is to define to which class or category a data point (or a policy keeper in our case) belongs to. In basic understanding, an ML algorithm will be trained to divide customers into churners and non-churners and answer questions like “Will the customer leave the company?” “Renew the insurance policy?” “Downgrade it?” A specific type of classification problem called anomaly detection can also help insurers track atypical behavior patterns of their customers.
Regression aims at evaluating the relationship between the variable and data values that influence it. In simple words, the outcome of regression always includes a number, for example, the period when the customer is projected to leave the company. In the case of classification, it’s always a suggested category (churner/non-churner).
2. Data collection
The next step will be to decide what sources to use for data collection and actually gather this data. In machine learning, data matters a lot, and the quality and relevance of the insurer’s data will directly impact the results that the ML model produces. Some of the sources that insurance companies could consider include:
Types of policies held
Data related to customers’ locations
Sales and customer support records and call transcripts
Customer reviews on social media or review platforms
All types of feedback provided by customers on request (surveys, follow-up emails, and so on)
If you don’t have enough historical data or you’re not sure which sources to choose, no need to worry. Intelliarts’ data scientists have enough expertise to help you with data collection.
3. Data preparation and preprocessing
Data scientists will also need to prepare the raw data and convert them into a suitable format for an ML system to digest. To put it differently, the data points should be structured according to the same logic, and data scientists have to get rid of any inconsistencies.
The process of preprocessing could include these techniques:
As its name suggests, feature extraction helps to leave out the most discriminative and irrelevant information and, thus, shorten the number of variables, i.e. attributes, that impact the final results in machine learning.
Feature engineering means determining a set of features that describe the relationship between the customer and the service provided. In other words, these features have to describe the observations that ML algorithms will base on to predict customer churn probability. For example, this could be the division by age (younger vs. older) or the division of the cost of insurance by the average user’s salary.
Finally, the idea of feature selection is to revise all the extracted features and choose only those that correlate with customer churn the most. This is how data scientists get a dataset with the most relevant features.
4. Modeling and testing
The core of this project is the actual development of a churn prediction model. Data scientists usually test several models before they make their final decision. Also, building an ML model always engages multiple iterations: specialists have to train the model, tune parameters, evaluate, and monitor performance. They stop only when they notice that the model makes the most accurate customer churn prediction based on the training data.
Here is the list of ML algorithms that are used for customer churn modeling the most commonly:
Support Vector Machines
In case, you’d like to read about ML algorithms in detail, here are the posts covering traditional ML and deep learning techniques.
By the way, studies show different results on how various ML algorithms perform when it comes to customer churn prediction. However, the research on the insurance industry proves neural networks as well as the random forest algorithm to be among the most effective ML techniques to use, with an accuracy of more than 90%.
Interesting results were also obtained with the use of the logistic regression technique. The case study on the major health insurer CZ proved that a logistic regression model predicted churn well. It also gave important insights into the correlation between specific variables and customer churn. The churn model tracked the relationship between churn rate and contact moment, the number of times insured, discounts, premiums, and so on. This information was valuable for sales and marketing departments for further customer retention.
5. Deployment and monitoring
The final step is to put the customer churn prediction model in production, which also means testing its performance and integrating it into the current system. However, data scientists should also regularly monitor the performance of the model and retrain it if needed, for example, in case of serious data changes.
Reduce customer churn with machine learning
Customer churn is a big problem in the insurance industry, causing financial losses and lowering your growth potential. One great solution is to be proactive and predict customer churn using machine learning. In this case, an insurance company can react quickly and take measures to prevent appearing new churners.
If need a consultation on how ML techniques can help your insurance company prosper and reduce customer churn, drop us a line. Our team of professionals will be glad to dispel your doubts and discuss how a customer churn model will meet your specific business needs. Let us stop your customers from leaving, and by that – save up on retention costs.