Customer Churn Prediction: Techniques, Challenges, and How AI Can Help

What is churn prediction?

Churn prediction is a methodology that involves the data-driven pinpointing of customer accounts that are at high risk of downgrading their engagement, canceling their subscription, or disengaging with your company.

Customer churn prediction is based on multiple parameters:

• Relationship changes – The dynamics within each and every customer account are different. Some may be advocates that left the company or moved to another role. Some points-of-contact may be on maternity leave. Sometimes, companies are acquired and undergo massive restructuring. These changes can happen quite fast and render your customer success playbook irrelevant.
• Communications and sentiment fluctuations – Tracking emails and chats with customers is a good start, but the true sentiment lies in the fine details. Has the communication frequency changed drastically? What words are being repeated constantly? Is there a common theme in all customer accounts? These questions need to be answered.
• Product usage trends – Last but not the least, churn prediction also involves the use of product analytics. The way your customers use your application or service can say a lot about their current status. For example, if a customer is no longer using certain features or isn’t logging in as frequently as before, this may be a red flag that needs your urgent attention.

Churn prediction is a multi-step approach. Understanding the risk factors and uncovering blind spots is just the beginning. You’ll then need to coordinate your efforts with other departments like support, marketing, and other post-sales personas to eliminate the issues you have detected. Even after an account is saved, it should be tracked and monitored closely.

In this article:

• Why is it important to predict churn?
• How do automated churn prediction models work?
• Churn prediction: The challenges
• Key churn prediction modeling techniques
  • Logistic Regression
  • Decision Trees
  • Neural Networks
  • Ensemble Methods
• Getting started with churn prediction
• Taking churn prediction to the next level with Al

Related: The Ins and Outs of Churn Analysis

Why is it important to predict churn?

Implementing an accurate and reliable churn prediction model can help your business achieve sustainable growth. More and more post-sales executives are now turning to this methodology to fine tune their customer success playbooks and strategies.

Here are a few benefits of using churn prediction:

• Detect at-risk accounts – By pinpointing accounts that are about to churn in real time, you have time to save them with support and marketing efforts. You also don’t have to wait for subscription renewal periods in a reactive manner.
• Improved user experience – The insights you gain from your churn prediction models can also be used to enlighten your product teams. The onboarding experience can be finetuned and optimized with these key insights.
• Identify pain points – Having a capable customer churn prediction system in place will also help you understand where the friction points lie. For example, you may discover that certain accounts are avoiding features altogether.
• Create growth opportunities – When you have more active long-term customers, they automatically become prime candidates for advocacy and can also be targeted with upselling or cross-selling campaigns. 
• Better customer success playbooks – A recent Staircase AI survey showed that 62% of post sales executives find their playbook tasks to be partially or fully irrelevant. Churn analysis can help optimize CS playbooks based on current trends.

How do automated churn prediction models work?

A churn prediction model helps identify customers who are likely to leave a company’s service within a certain time frame. The model typically relies on historical data and machine learning algorithms to predict future behavior.

Here are the key components of a churn prediction model:

• Data collection: This involves gathering a wide range of data points from sources such as customer demographics, interaction history, product usage patterns, support tickets, and transaction records.
• Feature selection: Not all collected data is equally important. Feature selection is the process of identifying the most relevant variables that contribute to churn. These features could include frequency of logins, changes in product usage, communication frequency with support, and sentiment scores from customer interactions.
• Model training: Using the selected features, machine learning algorithms such as logistic regression, decision trees, or neural networks are trained on historical data. The model learns to identify patterns that indicate a higher likelihood of churn.
• Validation and testing: To ensure the model’s reliability, it is tested on a separate dataset that was not used during training. This step helps to evaluate the model’s accuracy and adjust parameters to improve performance.
• Deployment: Once validated, the model is deployed to monitor current customer data in real time. It continuously analyzes incoming data to provide early warnings about customers who might churn.
• Iteration and improvement: The model requires regular updates and retraining with new data to maintain its accuracy. This iterative process helps the model adapt to changing customer behaviors and market conditions.

Churn prediction: The challenges

While the benefits of having a capable customer churn prediction model in place are quite clear, implementing one is a whole new story. Traditional methods and techniques are becoming more and more outdated as online businesses evolve.

Let’s take a closer look:

1. Stakeholder changes – As mentioned earlier, relationship changes are happening all the time, often without prior notice. Once a champion leaves his or her position, it directly impacts your account. This is a real account risk (and a new opportunity with new personas) that can literally crop up overnight and can be extremely hard to track manually in companies that are scaling up.
2. Sentiment trends – Human beings are unpredictable by nature and the same applies to your customers. It’s extremely tricky to quantify and measure sentiment because customers are constantly changing their minds and developing new feelings about your product or service. This is something most businesses are still struggling with in 2023.
3. Reactive metrics – most traditional customer satisfaction metrics (NPS and CSAT surveys, CSM pulse, etc.) are reactive in nature and also don’t give you a complete picture, since you’ll never have 100% of the responses. Furthermore, breaking down the results of these activities and creating actionable insights manually is difficult.
4. Scaling up – Businesses often scale up fast and in a non-linear fashion. This means that customer success executives need to ignore the noise and detect trends that apply to multiple accounts. What people are talking about at scale? What topics are generating friction across the board with a certain group of customers? Gaining these insights manually is extremely challenging.

Key churn prediction modeling techniques

Here are the primary methods that can be used to predict customer churn.

Logistic Regression

Logistic regression is a statistical method for binary classification problems, where the outcome is a categorical variable that can take on one of two possible values, such as churn or no churn. The model works by fitting a logistic function to the data, which outputs probabilities that can be mapped to the binary outcomes.

This function is defined as the sigmoid function, which produces values between 0 and 1, making it suitable for probability estimation. Each coefficient in the model represents the log odds of the dependent variable (churn) for a one-unit change in the corresponding predictor variable, holding all other predictors constant.

For example, in a customer churn scenario, features such as the number of support tickets, frequency of product use, and customer tenure can be included in the logistic regression model. By analyzing the coefficients, companies can identify which factors are most strongly associated with churn.

Decision Trees

Decision trees are a type of supervised learning algorithm used for classification tasks. They work by recursively splitting the data into subsets based on the value of input features, creating a tree-like model of decisions and their possible consequences. Each node in the tree represents a decision point based on a feature, and each branch represents the outcome of that decision.

The process begins with the root node, which represents the entire dataset. The algorithm selects the feature that best splits the data into two distinct groups based on a criterion like Gini impurity or information gain. This process is repeated for each subsequent node, resulting in a tree structure where the leaves represent the final classification outcomes.

Decision trees are intuitive and easy to visualize, helping understand the decision-making process behind churn predictions. For example, a decision tree might reveal that customers who have not logged in for over a month and have a high number of unresolved support tickets are at high risk of churning.

Ensemble Methods

Ensemble methods combine multiple individual models to create a more accurate predictive model. By aggregating the predictions of several models, the ensemble can achieve better performance than any single model alone. Two popular ensemble techniques for churn prediction are Random Forests and Gradient Boosting Machines (GBMs).

Random Forests consist of multiple decision trees trained on different subsets of the data. Each tree makes its own prediction, and the final output is determined by averaging the predictions of all the trees (for regression tasks) or by majority voting (for classification tasks). This reduces overfitting and increases model stability, as the errors of individual trees tend to cancel out.

GBMs build trees sequentially, where each new tree attempts to correct the errors of the previous ones. This is done by fitting the new tree to the residual errors of the existing model, gradually improving the overall prediction accuracy.

Neural Networks

Modern AI systems used for churn prediction are primarily based on neural networks, also known as deep learning. Neural networks are machine learning models inspired by the structure and functioning of the human brain. They consist of layers of interconnected nodes, or neurons, where each connection has an associated weight. Neural networks can capture complex, non-linear relationships in data.

A basic neural network architecture includes an input layer, one or more hidden layers, and an output layer. The input layer receives the raw data, while the hidden layers perform various transformations and computations. The output layer provides the final prediction, typically as a probability score indicating the likelihood of churn.

Training a neural network involves adjusting the weights of the connections through a process called backpropagation, which minimizes the error between the predicted and actual outcomes. This iterative process continues until the model achieves satisfactory performance.

Neural networks can significantly outperform simpler models in terms of accuracy. For example, a neural network might uncover subtle interactions between user engagement metrics and support history that contribute to churn risk.

Getting started with  churn prediction

The ideal churn prediction model can vary based upon the industry you belong to and other factors like geolocations, company size, target audience profiles, and more. But the foundation of most modern churn prediction models are the same.

You’ll need to integrate the following components into your model.

Start off with granular customer segmentation

Customer segmentation is important because most products serve multiple user segments. Every segment has different needs, product usage, and service requirements. Some customers are high-touch and need to be approached daily, while others may prefer the solo route and like to work with online documentation or knowledge bases.

Key parameters you need to consider for this step include – demographics (age, gender, etc.), behavioral data (for example, how many tickets are opened on average?), revenue information, and subscription contract terms.

Constantly track and monitor product usage patterns

The traditional method was waiting for customer usage to dip to dangerous levels before getting involved, but this approach is no longer useful. You need to set KPI thresholds for product usage and use the right solutions to make sure they are not crossed. Furthermore, your teams should be looking out for trends. For example, if a new feature has not been received well by multiple customers, this might be a sign of trouble.

Real-time sentiment score tracking

Customer sentiment is a fusion of perception, opinions, and also the overall user experience. This can be related to the onboarding process, unresolved issues, in-app friction points, the performance of the support team, and more. Customer success executives need to have a system in place to feel the pulse and detect sudden drops in customer sentiment. Failing to do so can result in unexpected churn and brand damage that’s hard to fix.

Stakeholder mapping and ongoing monitoring

Stakeholders, such as brand advocates and champions, must be taken into account in churn modeling. Any change in their position or status has a direct impact on accounts, and might have a dramatic effect on churn rates. You need to conduct a comprehensive stakeholder analysis and mapping, after which they must be nurtured and contacted to convert new engagement and growth opportunities.

Taking churn prediction to the next level with AI

Predicting churn comes down to continuously feeling the customers’ pulse. But unlike medical tests where the doctor just needs to touch your wrist for 30 seconds, customer success executives need to touch multiple points – the product, the service being provided, and support levels, all of which add up to the overall value that your business is creating in the eyes of the customer.

Churn prediction can be done efficiently using traditional methods when businesses are just getting started, but things get complicated at scale. This is when companies have to increase the number of accounts per customer success manager and cut corners with their support operations. Add in the multiplying use cases with tough customer requirements and you have a situation that can go downhill pretty quickly. AI is helping businesses keep their service levels high to unlock more NRR.

AI scans all vendor-customer communications automatically to track relationships and sentiment fluctuations to highlight risks and point at new growth opportunities. Not only are these activities more accurate and data-driven, but customer success teams have more time on their hands. They can proactively act upon these unbiased insights and put more effort into growing and nurturing accounts in real-time.

You can now scale up your business even with limited post-sales resources without overwhelming your customer success managers with manual playbook-related tasks and with no impact on the quality of your service or support. AI is a true gamechanger to customer-facing teams.

Staircase: The Leading AI-Based Churn Prediction Platform

Detect and Prevent Customer Churn with Staircase AI

In a perfect world, you would know exactly why customers churn and could prevent it using all available historical data. Staircase AI makes this possible with our advanced AI churn prediction scores. With Staircase AI, you can:

• Predict Churn Before It Happens – Leverage AI to predict your customers’ churn signals before it’s too late. Our churn prediction scores are designed to detect customer risks based on your accounts’ historical data. This proactive approach allows you to take action one step before the fall.
• Understand the Reasons Behind Churn – Churn prediction starts with identifying churn signals. With Staircase AI, you can clearly see why customers churn by using live customer journey mapping. Investigate churn reasons, track engagement and sentiment trends, and gain comprehensive insights into your customers’ behaviors.
• Real-Time Alerts for Timely Actions – Customers send signals all the time, but we’re often too busy or optimistic to detect them. Staircase AI turns these insights into real-time alerts via Slack, ensuring that you, your team, and executive management are always informed and ready to act.

Staircase AI provides the tools you need to understand and prevent customer churn, helping you maintain a healthy and loyal customer base.

Learn More About the Staircase AI Platform