Imagine your customers like the leaky bucket; no matter how much you pour in, the leaks will never let it fill. But what if you could seal those leaks? Yes, predicting customer churn is the way to retain your loyal customers silently.  With every user planning to leave your site, they cause a significant loss to your business and overall profit. Building new customers is five times more expensive than retaining existing ones.

Therefore, the only rescue plan is to retain the customers before they leave you. When you get to know who is going to cancel your subscription, you can make a strategy to get them back. But this is not a walk-in-the-park task. For your convenience, this article provides a detailed guide into the churn prediction analysis and practical models to help you provide the best customer service and save your business.

Without further ado, let’s get started with the customer churn.

What is Customer Churn Prediction?

In the business world, the word ‘’churn’’ refers to those customers who are about to cancel the subscription or leave the services of a company. Predicting customer churn is a valuable business strategy that can give you another chance to build a strong relationship with your users. This is because if you don’t know who is going to leave you, how are you supposed to stop them?

Churn prediction gives you a clear description of which customers are at risk of leaving or abandoning your products. This way, you can implement some retention techniques to get them back. You have to use these retention tactics at some point or other, as the average churn rate for SaaS companies is 10 to 14% annually.

Again, you cannot apply similar marketing strategies to individual customers, so the goal is to target the ones who are on the verge of leaving your services. Besides, enlisted below are some key points that show why predicting customer churn is essential:

• The churn rate directly affects your revenue performance. Thus, the ultimate goal is to keep your churn stats as low as possible.
• It helps you determine how many users will leave your services in a given time. Depending on the scenario, you can develop targeted retention strategies to keep customers engaged.
• It is a great metric to keep track of your company’s progress and evaluate the satisfaction rate of your clients.

Why Predicting Customer Churn is Important?

Being unpredictable creatures, humans are set to change their minds anytime, at any point in their lives. That said, your customers can also stop using your products without writing you an email explaining why they planned to do so. Sounds unfair.

Right? This is where churn risk prediction comes to your help. It helps you identify customer churn patterns so you can make a retention plan to retain your valuable users. Let’s look at its importance in detail below:

1. Identify Pain Points

One of the key benefits of predicting customer churn is it helps you identify your customers' pain points. When you understand the cause of churn, you can make a more holistic plan to retain valuable clients. For instance, if you discover that a particular feature is causing customer churn, you can make a targeted plan to improve that feature altogether.

2. Improved User Experience

The churn prediction models can help you get detailed insights about your company or products. This allows businesses to better understand customer behavior and address potential problems. Alternatively, you can build a strong relationship with the user and take a proactive approach to make a long-term connection.

3. Product and Service Improvement

Predicting customer churn pinpoints accounts at risk of leaving your services or products. This can also provide patterns and insights into why customers leave. The information and churn data can be used to improve your services and overall customer experience. This way, you can reduce the churn rate by implementing effective retention strategies.

Building a Churn Prediction Model

Predicting customer churn is just half the battle. The real challenge lies in building a model that can analyze the number of leaving customers. An analytics model can help you in customer retention and achieve business profitability.

This is because the sole purpose is to retain customers. After all, a 5% increase in retention rates can increase profit by 25-95%. To achieve this, you need to set predictive churn modeling as described in the steps below:

1. Define the Problem

You have to start by determining your business case. Knowing your company scenario can help you clearly define what’s causing churn in your business context. Whether it’s subscription cancellation, no activity on the site for a longer period, or stopped purchases. This can provide you with a list of data that could be the main signs of churn.

2. Data Collection

The next step in predicting customer churn is to collect and prepare relevant data. This involves gathering data about product usage, customer behavior, clicks, and interactions. All of these rough data charts can then be organized into structured data for better evaluation. Here are some common customer churn data sources that you can collect when building a model:

• Demographics such as age, gender, and location
• Transaction history, including purchasing date and amount
• Customer service interactions or clicks
• The frequency and duration of use
• Customer feedback via surveys and reviews

This is an easy way to build and prepare data for your churn model. For assistance, you can use softwares such as CRM, web analytics, or sentiment analysis tools to gather a large amount of data in a given timeframe.

3. Customer Segmentation

Once you’ve gathered enough data about your users, it’s time to segment your customers based on the consolidated database. Customer segmentation can help understand different user profiles and then select a predictive model based on the data. It includes the attributes such as demographics, behavioral data, customer interest and company size or business needs. Plus, it helps answer the following questions:

• How often is a customer using your product?
• Which specific feature is mostly used by a user?
• How much revenue do you earn from a customer?
• How long have they been using your services?

All of these queries are highly beneficial in predicting customer churn and building an accurate model. Based on customer segmentation, you can predict who will likely cancel your subscription or stop visiting your services.

4. Feature Engineering

An integral part of customer churn predictive analytics involves feature engineering. It helps you better understand customer’s interaction with a product or service. It aims to create features that capture patterns and behaviors indicating whether a customer will churn. The key aspects of feature engineering involve the following:

• Aggregation feature that aggregates data over time to summarize customer behavior.
• Temporal feature that captures the time-related aspects of customer activity.
• Behavioral feature that reflects customer engagement and satisfaction.
• Text feature for sentiment analysis and topic modeling.

Overall, it’s a great point in predicting customer churn that assigns measurable attributes to data points. Data scientists use feature engineering to get meaningful information depending on their business case.

5. Choosing a Model

Here comes the most crucial step - choosing a predictive model for customer churn. Fortunately, there are different algorithms to predict churn, each involving a unique attribute. To manage large datasets, you can select various models, such as binary classification, decision trees, random forests, and neural networks.

Once you’ve selected a model, it’s time to train, validate, and deploy it for your business needs. Let’s discuss a few key points of each model one by one below:

• Binary classification is a simple and effective model that runs on logistic regression.
• Decision trees provide insights into feature importance and decision rules.
• Random forests improve accuracy by combining multiple decision trees.
• Neural networks help capture non-linear patterns in large datasets.

Implementing Churn Prediction Model for Customer Retention

Now, you’ve acknowledged the basic steps of predicting customer churn. However, one thing that is still questionable is how to implement the churn prediction model into your business. This involves a multi-step approach and cannot be completed in just one single step. Therefore, here are some effective tips and strategies to implement the churn machine learning model:

1. Model Evaluation

The first and most important thing is to assess your model’s performance. This is simply called model evaluation and involves various metrics such as the percentage of correct predictions and the overall score.

You can evaluate the model using performance metrics like accuracy, precision, recall, F1 score, and ROC-AUC. This will help in churn forecasting and achieving better customer retention rates.

2. Model Deployment

After cross-validating your model's performance, you need to integrate it into your business or company. When predicting customer churn, you can deploy the model into productive environments. This can include API integration, batch processing, or real-time processing.

3. Monitoring and Maintenance

Here comes the final step. After successfully evaluating and deploying your model, you need to monitor it and update it for changes regularly. Doing so will determine your churn probability and provide better insights into your customer activities. You can also periodically retrain the model with new data to maintain accuracy.

How to Practically Prevent Customer Churn?

Predicting customer churn is a broader thing that takes into account different factors and attributes. You cannot rely on one simple strategy to prevent churn and retain customers.

There is an unending list of items that you need to implement to reduce your customer churn. Improving your customer experience is just the tip of the iceberg. Here are top tricks and tips that will help you prevent customer churn:

1. Understand your Customers

You might have heard the phrase ‘’satisfied customers are usually loyal customers’’. So, your focus should be on improving your customer experience, and the best way to do this is to maintain good communication with your users. A happy customer will reduce your churn rate and maximize your business revenue.

Plus, it’s an important aspect of predicting customer churn and makes it easy for you to engage with the users before they leave you. For instance, you can improve customer support or promptly reply to their messages. A hot tip is to use AI chatbots to take instant actions and solve user’s problems.

2. Use CRM System

When predicting customer churn and retaining users, you can get help from advanced tools and technologies. This involves using Customer Relationship Management (CRM) Software to manage user relationships and interactions. You can also use analytics tools to gain insights into customer behavior and identify at-risk customers. Enlisted below are some of the benefits of using a CRM system:

• It can improve customer retention by automating tasks.
• It sets reminders and follow-ups for timely customer interaction.
• You can instantly get notified about a user problem and take swift action.

Overall, CRM systems are designed to improve your sales and streamline your business. You can implement them for better customer segmentation and boost your business growth.

3. Incentivize your Customers

Predicting customer churn is all about making the right retention strategies. Thus, what could be better than providing value to your customers through special promotions and discounts? You can introduce loyalty programs for loyal or long-term users to build strong relationships. This can include rewards, exclusive offers, or discounts on your products or services. All of this will reduce your churn probability and increase your sales.

Conclusion

Predicting customer churn is all about making the right choices at the right time. From building a churn prediction model to understanding your customers' pain points, the secret lies in the appropriate retention strategies that work wonders for your business. One way of retaining users is to get help from a well-trained AI Chatbot customer service. It can generate custom prompts and provide AI support to improve customer experience.

Above all, the chatbot is designed to connect data sources and identify which customers will leave your site or stop using your products. What’s best? You can automate your everyday tasks and get valuable insights into your customer’s data. Use Aidbase for free and upscale your business like never before.