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Business Intelligence

eBook

Learn how data analysis, machine learning classifiers, and other data science techniques can be used to predict churn—with a tutorial.

TABLE OF CONTENTS

In today's economic environment, business owners are faced with higher costs, more competition, and more pressure than ever before. One of the most important things you can do as a business owner is to anticipate the loss of customers and intervene to prevent it. Churn prediction, an application of data science, can help you do just that.

Churn prediction is the process of using data from past customer behavior to identify customers at risk of leaving your business. With the right data and machine learning techniques, you can identify high-risk customers in real time and stop churn before it happens. In this article, we’ll explore how data science works in churn prediction.

Churn is when customers stop using your product or service. That could mean canceling a subscription, letting a contract lapse, or simply not using your product as often as they used to. Typical causes of churn include price increases, poor customer support, or a lack of features.

Whatever the reason, customer churn is expensive and damaging to your business. In fact, it's estimated that average SaaS companies lose 5% of their customers every month, or 43% annually. Some sectors, like telecom, have even higher churn rates.

Businesses with a large customer base face monthly churn of an enormous number of customers. If a firm with a million customers has just a 5% monthly churn rate, that means a loss of tens of thousands of customers a month.

Unsurprisingly, then, churn has been called the “silent killer” of the SaaS industry. That's why it's important to understand why customers are leaving and what you can do to prevent it. Churn prediction is one of the best ways to do so.

Data science is a multidisciplinary field that combines statistical techniques, machine learning, and large datasets to help organizations make better decisions. Implementing artificial intelligence for customer attrition prediction is done as a binary classification problem: Over a given time frame, either a customer will churn or they won’t.

Churn prediction was one of the earliest practical applications of data science, and remains an important tool for businesses of all sizes. By using data science techniques and machine learning algorithms, organizations can identify patterns in customer behavior which can help them predict when customers are likely to leave and take steps to retain them.

There are many different ways in which you can predict customer churn. The most basic approach involves building a statistical model based on historical data. This can be done by analyzing customer records over time to identify common patterns in customer behavior.

Linear regression is a simple model used to predict the value of a target variable (dependent variable) based on the values of one or more independent variables. It is based on the principle of “least squares,” which is a method of finding the line of best fit that minimizes the sum of the squared errors. The equation of the line is a linear equation with the form: y = mx + b.

The “m” in the equation is the slope of the line, which represents the relationship between the independent and dependent variables. The “b” in the equation is the intercept, which represents the baseline value of the dependent variable when all the independent variables are set to zero.

First, it begins by randomly selecting a line (m and b). It then calculates the sum of the squared errors between the predicted values and the actual values. It then adjusts the line (m and b) until it finds the line of best fit that minimizes the sum of the squared errors.

This process is repeated multiple times until the algorithm converges and finds the best-fitting line. Once the algorithm converges, it produces the coefficients of the linear equation (m and b). This equation can then be used to predict the value of the dependent variable for any given set of independent variables.

For instance, a linear regression model might find that customers who have been with a company for a long time have a lower chance of churning. Or, it might reveal that customers who spend more money are less likely to leave.

Another simple model is logistic regression. It is a classification algorithm that uses a logistic function to estimate the probability of a customer churning. Instead of m and b, it uses coefficients that correspond to each of the independent variables.

It is similar to linear regression in that it also finds the best-fitting line, but instead of using the least-squares approach, it uses the maximum likelihood approach. This means that it finds the line that maximizes the likelihood of correctly predicting the class (churn or no churn).

The coefficients of the logistic regression equation can then be used to calculate the probability of a customer churning. For example, if the logistic regression equation yields a coefficient of -2 for the independent variable “time with company,” then customers who have been with the company for longer will have a lower probability of churning.

More advanced algorithms such as neural networks can be used to predict customer churn. These algorithms are able to learn from data and detect complex patterns that may not be easily detected by simple models.

Neural networks are particularly powerful for churn prediction. They are able to learn from data and detect patterns that are too complex for simple models. The “inputs” to the neural network are customer attributes such as age, gender, location, purchase history, and so on. The “layers” of the neural network are the mathematical equations that progressively refine the predictions.

The final layer outputs a single value which is the probability of customer churn. This value can then be used to take action to retain customers or predict which customers are likely to leave.

One method, backpropagation, is used to tune the weights of the neural network. This is done by feeding the network a large amount of data and adjusting the weights until the network produces the desired prediction.

Decision trees are another type of machine learning algorithm used for churn prediction. They are similar to neural networks in that they are able to learn from data and detect complex patterns. However, they are different in that they are able to represent data in a much more intuitive and visual way.

A decision tree is composed of nodes and branches. Each node represents a feature or attribute of the customer and each branch represents a decision. The algorithm begins by randomly selecting an attribute and a decision (branch). It then proceeds to the next node, again randomly selecting an attribute and decision. This process is repeated until a prediction is made.

Random forests are a similar type of algorithm that uses an ensemble of decision trees to make predictions. Instead of relying on a single decision tree, random forests use many different decision trees, each of which has been trained on a different set of data. This allows random forests to be more accurate than a single decision tree.

There are many other types of “ensemble” algorithms that can be used for churn prediction, such as boosting and bagging. These algorithms are powerful and can be used to make highly accurate predictions, but they can also be time-consuming, computationally expensive, and less interpretable than something like a decision tree.

Overall, there are many variables that influence customer churn and these can be difficult to measure. For example, perhaps customer loyalty is based more on customer service than on monetary spending, but only for smaller firms. Countless other variables, like the client's number of employees, the salesperson’s experience, or the time of year, can have a dramatic effect on customer churn.

The complex interactions between these variables can't be captured by simple linear models, so more sophisticated machine learning algorithms – such as deep learning and artificial neural networks – must be used.

Building a churn prediction model starts with looking at customer data, whether it's from a CRM, stored as a CSV, or collected from service providers. Data preparation and preprocessing are key components of the workflow. Data types can include demographic, customer experience, conversion, customer lifetime value, and business model data.

While many think "big data" is necessary to understand a customer, this is not always the case. A Harvard Business Review article explores that "small data" such as sampled insights from customers, can be just as effective.

To select your data in Akkio, you simply hit "Upload Dataset" or select the tool you're already using, and the machine learning model can be created with no coding required. You can optionally merge multiple sources of data, such as financial records, customer service call logs, or survey results. You’re now well on your way to building a Flow that results in a churn model that can be deployed anywhere.

Then, you simply select the column representing the churn target and click predict. Akkio uses Neural Architecture Search (NAS) to quickly identify the most effective model for the data, eliminating the need to manually test multiple models.

Once a model has been trained, you can view model performance metrics, like precision, recall, F1 score, and RMSE. Precision refers to the percentage of correct positive predictions made by the model. Recall refers to the percentage of actual positive cases that were correctly predicted by the model.

The difference can be understood by looking at it in terms of a simple example: If a model is asked to predict whether a client will churn or not, precision would be the percentage of clients that the model predicted would churn, and were actually churned. Recall would be the percentage of clients that churned, and were correctly identified by the model.

The F1 score is a measure of a model’s accuracy. It is the harmonic mean of precision and recall, and takes into account both false positives and false negatives. Finally, RMSE (Root Mean Squared Error) is a measure of how close the predicted values are to the actual values. The lower the RMSE, the better the model is performing.

If you're seeing low accuracy or precision metrics, you may need to adjust the data sources, add more data points, or improve data quality to improve model performance.

Once the data has been prepared and a model has been identified, the next step is to make predictions. You can then identify existing customers or new customers at risk of churning.

Traditionally, businesses would need to build teams or hire data scientists to build a model manually using Python. However, this process can be time-consuming and expensive. With Akkio, you can create a machine learning model quickly, without coding or hiring additional personnel.

Below, you can try out an interactive churn model created by Akkio. Simply plug in sample input values, and get a prediction of whether the user will churn.

When you go to create your own model, you’ll have several deployment options, including an API, integrations with tools like Snowflake and Zapier, and the ability to make an embeddable model like what’s seen above.

Being a business owner in today's fast-paced, competitive environment is no walk in the park. It's essential to identify and address the drivers of churn before they become costly losses.

Data science and machine learning techniques are powerful tools that can help you predict customer churn and develop strategies to retain customers. Akkio is an intuitive, no-code platform that makes it easy to create churn models and leverage predictive analytics to identify at-risk customers and reduce your churn rate.

Sign up for a free trial of Akkio to build customer churn prediction models and increase your customer retention.