Customer churn, also known as customer attrition, is a major problem for businesses that rely on recurring revenue. Customer churn costs businesses billions of dollars every year, and it’s only getting worse as customers become more and more fickle. In fact, it’s been estimated that the average company loses 10-15% of its customers each year. That number may seem small, but it can have a huge impact on a company’s bottom line. Fortunately, there’s a way to combat churn: by using machine learning to predict which customers are likely to churn.
In this blog post, we’ll discuss how customer churn prediction works and why it’s so important. We’ll also provide some tips on how you can use machine learning to predict churn for your business.
What is Customer Churn problem?
Customer churn is a problem that occurs when customers or subscribers stop using a product or service. It can be measured by the number of customer cancellations or the percentage of customers who fail to renew their subscriptions. Customer churn can be a major issue for businesses, as it can lead to lost revenue and decreased customer loyalty. Customer churn can be caused by several factors, including poor customer service, high prices, and competition from other companies. Churn can have a significant impact on a company’s bottom line, as it can lead to lost revenue and decreased customer loyalty. To combat customer churn, companies must work to identify the causes of churn and take steps to address them. This may include improving customer service, offering discounts or promotions, and increasing marketing efforts. By taking these steps, companies can reduce customer churn and improve their financial performance.
Customer churn prediction is important because it allows businesses to take proactive steps to prevent customer attrition. For example, if a machine learning model predicts that a particular customer is likely to churn within the next month, the company could reach out to that customer and offer them a discount or other incentive to stay with the company.
Of course, predicting churn is only half the battle; businesses also need to take action on those predictions. But even if businesses only act on a small percentage of predictions, they can still see a significant reduction in customer attrition.
How does customer churn prediction work using machine learning?
Customer churn prediction is the process of using machine learning to identify which customers are likely to stop doing business with a company. This is typically done by training a supervised machine learning model on historical data, such as customer purchase history, account activity, and customer service interactions. The model is then used to make predictions on new data, such as whether or not a new customer will churn within a certain period of time. Customer churn is a classification problem and the machine learning model can be used to classify whether a customer will churn or otherwise. The following are common features used for training machine learning models for predicting customer churn:
- Length of time a customer has been with the company
- Number of products/services a customer uses
- How often a customer interacts with the company
- Frequency of purchase
- Amount of money a customer spends with the company
- Customer demographics (location, age, gender, etc)
- Whether the customer is satisfied with the company’s products or services
- Customer tenure (number of months subscribed)
- Type of product/service
- Customer engagement (online activity, social media activity, etc.)
- Purchase history: The history of a customer’s purchases can be analyzed to see if there are any patterns that indicate they are at risk of churning. For example, if a customer always buys the same product every year, and then stops buying it, that could be an indication that they are about to churn.
The following represents workflow that can be used to train machine learning classification model for predicting customer churn:
- The first step is to collect data on past customers who have churned. This data should include features information such as those mentioned above including the customer’s demographics, behavior, and engagement with the product or service. Once we have this data, we can split it into two parts: a training set and a test set.
- The training set is used to train the machine learning model. This is done by feeding the data into the model and telling it which customers churned and which didn’t. The model then “learns” from this data and adjusts its internal parameters accordingly.
- Once the model has been trained, we can then use it to make predictions on the test set. This allows us to see how accurate the predictions are and whether or not the model is “overfitting” the data (i.e., memorizing specific cases rather than generalizing from them).
- If the predictions are accurate and the model is not overfitting, then we can be confident that it will perform well on unseen data in the future.
There are a number of different machine learning algorithms that can be used to predict customer churn. Some of the most popular ones include:
- Logistic regression: This is a type of regression analysis that is used to predict the likelihood that something will happen. In the case of customer churn, this algorithm can be used to determine which customers are most likely to cancel their service.
- Neural networks: This is a type of machine learning / deep learning algorithm that is based on the way the brain works. It can be used to predict customer churn by learning how to identify patterns in data.
- Decision trees: This is a type of machine learning algorithm that is used to predict events by splitting data into branches. It can be used to identify which factors are most predictive of customer churn.
- Random forest: Random forest algorithm creates a number of decision trees, each of which predicts churn independently. The predictions of these trees are then averaged to produce a more accurate prediction of churn. This algorithm is especially useful for large data sets, as it is able to handle high volumes of data without sacrificing accuracy. There are many benefits of using a random forest machine learning algorithm for predicting customer churn. First, the algorithm is highly accurate and can predict churn with a high degree of precision. Additionally, the algorithm is relatively fast and easy to use, making it a great choice for larger datasets. Additionally, the random forest algorithm is relatively robust and can handle a wide range of data types.
Few things should be kept in mind if you’re looking to build machine learning model to predict customer churn for your business:
- Data quality is important: In order for your predictions to be accurate, you need to have high-quality data. This means data that is complete, consistent, and free from errors.
- Look for patterns by asking right questions: When you’re exploring your data, look for patterns that might indicate which customers are at risk of churning. For example, do customers who frequently contact customer service tend to churn more? Do certain types of products have higher rates of attrition?
- Evaluate different models: There’s no one-size-fits-all solution when it comes to machine learning models; what works for one business might not work for another. So, it’s important to experiment with different models and find the one that works best for your data set.
There are a number of different KPIs to measure churn, but the most common value metrics is “net churn“. Net churn is calculated by taking the number of customers who have stopped doing business with a company and subtracting the number of new customers acquired during the same period of time. For example, if a company has 100 customers at the beginning of the month and 90 customers at the end of the month, their net churn rate would be 10%.
Customer churn prediction use cases
The following are few use cases related customer churn prediction using machine learning:
- Churn prediction in the retail Industry: The retail industry is one of the most competitive industries in the world, and predicting customer churn is essential for retailers in order to stay ahead of the competition. In the past, retailers have used traditional methods such as surveys and customer loyalty programs to predict churn. However, these methods are often inaccurate and can be costly. Machine learning provides a more accurate and cost-effective way to predict customer churn.
- Churn prediction in the banking industry: Banks are another industry that is highly competitive, and predicting customer churn is essential for banks in order to stay ahead of the competition. In the past, banks have used traditional methods such as surveys and customer loyalty programs to predict churn. However, these methods are often inaccurate and can be costly. Machine learning provides a more accurate and cost-effective way to predict customer churn.
- Churn prediction in the telecommunications industry: The telecommunications industry is another industry that is highly competitive, and predicting customer churn is essential for telecommunications companies in order to stay ahead of the competition. In the past, telecommunications companies have used traditional methods such as surveys and customer loyalty programs to predict churn. However, these methods are often inaccurate and can be costly. Machine learning provides a more accurate and cost-effective way to predict customer churn
Conclusion
Customer attrition (or “churn”) is a major problem for businesses that rely on recurring revenue—but it doesn’t have to be. By using machine learning to predict which customers are likely to churn, businesses can take proactive steps to prevent attrition and keep their customers happy. If you’re looking to use machine learning for customer churn prediction in your business, keep these tips in mind: data quality is important; look for patterns in your data; and test different models until you find one that works best for your business.
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