AI & ML Churn Prediction. Calculate Customer Churn Prediction ROI

Many companies use statistical models to optimize their activities. An example of this type of models are scoring systems used in banks in assessing creditworthiness, models used to optimize the activities of debt collection companies, models optimizing direct marketing or used in CRM (Customer Relationship Management). Such models are often called predictive models because they allow prediction (forecast) of future customer behavior. In this article, we would like to introduce to you Customer Churn prediction and its ROI calculator.

What is Customer Churn?

Customer churn is one of the most important metrics for evaluating a growing business. Customer churn is the percentage of customers who have stopped using your company’s product or service for a certain period. However, the definition of ” customer churn ” differs for different industries.

For an e-commerce or telecommunications company, this means losing customers, while for a subscription-based business, churn means losing subscribers. And so, the churn rate may be calculated by dividing the number of customers lost over a particular time by the number of customers acquired and then multiplying that number by 100 percent.

“Churn rate is a health indicator for businesses whose customers are subscribers and paying for services on a recurring basis.” – Alex Bekker, Head of data analytics department at ScienceSoft. [1]

Importance of customer charn prediction

Customer churn prediction is a critical aspect of modern business management, especially for companies operating on subscription models. Understanding why predicting customer churn is important involves several key factors:

• Higher Costs of Acquisition
  Acquiring new customers typically incurs higher costs compared to retaining existing ones. Businesses often find that the expenses associated with marketing and onboarding new clients can significantly outweigh the costs of retaining current customers.
• Revenue Protection
  Predicting churn allows businesses to take proactive measures to retain customers before they leave, thereby protecting revenue streams. This is particularly vital for subscription-based services, where losing a customer can mean losing recurring revenue.
• Personalized Engagement
  By identifying customers at risk of churning, companies can tailor their marketing efforts and communications to address specific concerns or needs of these individuals. This targeted approach increases the likelihood of retaining these customers.
• Improved Customer Experience
  Understanding the reasons behind potential churn enables businesses to enhance their offerings and customer service, ultimately leading to a better overall customer experience and increased satisfaction.
• Utilization of Historical Data
  Churn prediction models leverage historical data to identify patterns and behaviors that indicate a risk of churn. This data-driven approach helps businesses make informed decisions about customer engagement strategies and product improvements.
• Dynamic Adjustments
  Advanced churn prediction techniques allow for real-time analysis, enabling businesses to adapt their strategies based on up-to-date customer behavior rather than relying solely on static data.
• Increased Customer Lifetime Value (CLV)
  Retaining customers not only preserves current revenue but also maximizes their lifetime value. A focus on reducing churn can lead to sustained revenue growth over time as loyal customers are more likely to make repeat purchases and recommend the service to others.
• Strategic Resource Allocation
  By understanding churn dynamics, businesses can allocate resources more effectively, focusing on retention strategies that yield the highest returns rather than spreading efforts thin across acquisition alone.

Customer churn analysis is essential for optimizing operational efficiency, enhancing customer satisfaction, and driving sustainable growth in competitive markets. By proactively addressing the factors that contribute to churn, businesses can significantly improve their financial health and market position.

Benefits of using AI and machine learning in churn prediction

AI churn prediction and machine learning churn prediction offer numerous benefits that enhance business operations and customer retention strategies, such as:

Enhanced predictive accuracy

AI and machine learning algorithms can analyze vast amounts of historical data to identify complex patterns and correlations that might not be apparent through traditional analysis. This leads to more accurate predictions of which customers are likely to churn, allowing businesses to take proactive measures.

Automated data processing

Machine learning churn prediction solutions automate the data processing and analysis phases, reducing the time and resources required for manual analysis. This efficiency allows businesses to focus on implementing strategies based on the insights gained rather than spending excessive time on data interpretation.

Identification of key churn drivers

AI churn prediction tools can help identify the specific factors contributing to customer churn, such as dissatisfaction with service or competitive offerings. Understanding these drivers enables businesses to address issues directly and improve their overall service quality.

Scalability

AI churn prediction solutions are scalable, meaning they can handle increasing amounts of data as a business grows. This adaptability ensures that churn prediction models remain effective even as customer bases expand or change over time.

What is Customer Churn Prediction ROI?

Customer churn prediction ROI means determining which customers can opt-out of a product or subscription to a service, depending on how they use it. This is an important forecast for many businesses because attracting new customers is much more expensive than retaining existing ones. Therefore, using advanced artificial intelligence techniques such as machine learning (ML), you will be able to predict potential outflows that are going to abandon your services.

How can AI and machine learning help with churn prediction?

AI and machine learning significantly enhance customer churn prediction by leveraging vast amounts of data to identify patterns and behaviors that indicate potential churn.

Here are the key ways these technologies contribute to effective customer churn analysis:

Data processing and analysis

AI and machine learning churn prediction solutions excel at processing large datasets from various sources, including customer interactions, transaction histories, and demographic information. This comprehensive analysis allows businesses to gain insights into customer behavior and churn patterns, enabling them to identify at-risk customers more effectively.

Predictive modeling

Machine learning algorithms, such as logistic regression, decision trees, and random forests, are used to build predictive models that classify customers based on their likelihood of churning. These models are trained on historical data to recognize patterns associated with customer attrition, allowing businesses to anticipate which customers may leave.

Real-time predictions

AI churn prediction systems can provide real-time churn predictions by continuously monitoring customer behavior. This capability allows businesses to detect early warning signs of churn and implement immediate interventions, such as targeted marketing campaigns or personalized offers, to retain those customers before they decide to leave.

Feature extraction and selection

Machine learning and AI churn prediction algorithms automatically identify the most relevant features that contribute to customer churn. By focusing on these key predictors, businesses can streamline their churn prediction processes and enhance the accuracy of their models.

Handling complex patterns

Deep learning techniques can uncover intricate relationships within the data that traditional methods might miss. By utilizing neural networks, businesses can improve the accuracy of their churn predictions by recognizing complex dependencies between various customer attributes and churn indicators.

Customer Churn Prediction ROI with Machine Learning

The basic feature of machine learning is the creation of systems capable of finding patterns in data and learning from them without explicit programming. In the context of customer churn prediction ROI, these are characteristics of online behavior that show a decrease in customer satisfaction with the use of the company’s services/products. The total amount of work done by data scientists to develop machine-learning-based algorithms that can predict customer churn ROI may look like this:

Defining the problem and the final goal

Firstly, it is important to understand what ideas you need to get as a result of the customer churn prediction. At the very beginning, data scientists should determine which questions to ask and, as a result, which type of machine learning problem to solve: for instance, classification or regression.

Classification is used to identify which class or category a data point belongs to (in our case, the client). Customer churn prediction ROI, on the other hand, can be represented as a regression problem. On the other hand, regression analysis is a statistical method for determining the connection between a target variable and other data values that impact the target variable, which is expressed as continuous values.

Creating a data collection

The next step of customer churn prediction ROI using machine learning is to define the data sources that will be used in the next stage of the simulation. In most cases, you get data from: [1]

• CRM platforms (Salesforce, Pipedrive, Microsoft Dynamics 365)
• Marketing/Analytics services (Google Analytics, AWStats)
• Comments on social media/reviews/blog pages
• Feedback provided on demand

Data preparation and preprocessing

After that, the historical data must be converted to machine-learning friendly format. The main goal here is to verify that all discrete units of information are collected using the same logic, and the overall data collection is consistent.

Modeling and testing

This is when a churn prediction ROI machine learning model is created. Several churn prediction models are often trained, configured, and tested to determine which is the best in terms of speed and accuracy. Moreover, they can represent logistic regression, decision trees, random forests, or any other algorithms that are acceptable.

Deployment and monitoring

This is the final stage of the churn prediction ROI using machine learning. The chosen model must be put into production. Finally, the model can be integrated into existing software or become the foundation for a new application.

Customer churn and retention using predictive modeling and AI

Predictive models can predict the chance of occurrence of any phenomenon or the fact that it occurs: failure to repay, accident, customer departure or purchase. In fact, the use of the predictive model to support decisions in comparison to the application of common sense rules or those proposed by the expert gives a profit of 10-30% greater.

Customer churn and retention decrease are a very important problems in most of the industries nowadays. To solve these issues, it’s very important to analyze each customer and predict their future behaviour. Therefore, machine learning techniques are able to discover patterns in your customer data and apply those insights to predict churn, which well helps to retain more customers. More and more companies are starting to implment machine learning and AI for customer retention management.

At Addepto we have prepared ROI calculator which will show how much return on AI investment you could get. Use below Customer Churn Prediction ROI calculator to estimate how much you could save by leveraging Artificial Intelligence with use of machine learning and predictive analytics.

Customer churn prediction software and it’s ROI

Churn prediction software and solutions are used in many industries, such as e-commerce, mobile gaming, telecom, fin-tech (finance) , healthcare, insurtech (insurance), fitness, retail, banking and many more businesses. While calculating investment on customer churn prediction costs you should understand all other financial and business benefit aspects.

In addition, statistics show that it is 5 times cheaper to retain existing customers rather than finding and acquiring a new ones. Churn prediction allows you to not only keep your clients active and loyal but also give you additional opportunity for up-sell and cross-sell on retained customers. So, all of these factors give you an amazing opportunity to grow your business faster.

Regardless of the industry, the above customer churn prediction ROI calculator will help you pre-determine the potential advantages of implementing the customer churn analysis AI model into your system. It also helps to estimate how much you could save by implementing machine learning models for customer churn prediction.

Use our churn prediction software to increase your customer retention and churn prediction ROI. Get started applying machine learning for marketing and AI models to predict customer churn and stop your customers from leaving your company.

Discover patterns in your customer database and apply that information to retain more customers and keep the loyal. Furthermore, our customer churn software will provide you with additional recommendations on why your customers are likely to leave.

Customer churn prediction using machine learning in various industries

Customer churn prediction in telecom industry

In recent decades, the development of information and communication technologies (ICTs) has grown rapidly. However, commercial companies in general and telecommunications companies in particular are considered one of the leading sectors that suffer from customer churn. This means that such companies can lose about half of their clients and can lead to decrease in profitability.

Therefore, telecommunications companies use different strategies to manage churn and retention. [2] Machine learning has already been used to reduce customer churn by well-known giants such as AT&T, Sprint, Vodafone and T-Mobile.Today, even small companies and startups are trying to implement artificial intelligence applications as soon as their services enter the market. [3]

Customer churn prediction in the retail industry

Even very simple machine learning algorithms can collect, analyze, and show data while providing precise predictions based on conversion trends, repeat visits, and transactions made by a certain consumer. For example, e-commerce website Showroomprive.com uses predictive modeling to manage return on investment(ROI). The solution they use sets the rules for identifying “potential outflows” and determines the value of each such client.

Moreover, due to machine learning algorithms, they use customer data to effectively target personalized marketing campaigns to those clients. This is how customer churn prediction ROI using machine learning works. [4]

To sum up, it is important to predict customer churn ROI. Predicting and preventing customer churn ROI will not only save your company a lot of money on attracting new customers, but it will also provide a significant additional potential revenue stream for your business.

If you have any questions about machine learning consulting or you need assistance in measuring ROI on predictive analytics implementation for customer churn, just drop us a line. We will provide you with free consultation and analysis of your data for potential machine learning and AI model implementation for customer churn prediction.

This article is an updated version of the publication from Jun 22, 2021.

References

1. Kdnuggets.com. Customer Churn Prediction Using Machine Learning: Main Approaches and Models. URL: https://www.kdnuggets.com/2019/05/churn-prediction-machine-learning.html. Accessed June 22, 2021.
2. I.AlShourbaji, N.Helian, Y.Sun, M.Alhameed. Customer Churn Prediction in Telecom Sector: A Survey and way ahead. (2021). URL: http://www.ijstr.org/final-print/jan2021/Customer-Churn-Prediction-In-Telecom-Sector-A-Survey-And-Way-A-Head.pdf.
3. Fayrix.com. Benefits of Customer Churn Prediction Using Machine Learning. URL: https://fayrix.com/blog/customer-churn-prediction-benefits#benefits. Accessed June 22, 2021.
4. Comtecinfo.com. How Predictive Analytics helps in reducing churn for e-retailers. URL: https://www.comtecinfo.com/rpa/predictive-analytics-helps-reducing-churn-e-retailers/. Accessed June 22, 2021.