It seems like just yesterday when credit card companies were relying on manual processes to predict customer churn and make decisions about their customer base. Now, a new revolution is taking place, as companies are increasingly turning to data science and machine learning to make more accurate predictions and more informed decisions.
Churn, which in this case refers to when customers cancel or stop using their credit cards, is a major issue for credit card companies, as it leads to lost revenue and decreased customer loyalty. In this article, we’ll explore the factors that cause credit card customers to leave, how to predict when they will leave, and the analysis involved in credit card customer churn.
Understanding the value of customer retention is essential for credit card companies to understand their customer base and make informed decisions. Credit card churn is the loss of a credit card customer, either through cancellation or non-renewal of their account.
For instance, when your credit card reaches its expiration date and you don’t renew it, you are considered a churned customer. However, churn could also be if you simply don't use your card for a period of time, or if you switch to another card issuer. Finally, if you choose to actively cancel your account, you are also considered to be a churned customer.
Understanding why customers fall into any of these categories is key to predicting and preventing churn. If customers are canceling their accounts, it may be due to poor customer service, a lack of rewards, high interest rates, or a number of other factors.
On the other hand, if they're not using newly issued cards after a certain period of time, it could be due to a lack of awareness of the product, or they may not have received an adequate offer to use the card.
Credit card companies that fail to understand the causes of customer churn are at risk of losing customers and not being able to capture new ones.
Many credit card firms see customers ditching their cards in alarming numbers. With high fees, policies becoming more complicated and customer service being often unhelpful, it's becoming harder to justify keeping a card in your wallet.
It's estimated that more than 6 in 10 Americans have closed a credit card. That's a staggering statistic considering the convenience and powerful purchasing power that credit cards offer.
So why are people leaving their plastic behind? Many of them cite high costs as the number one reason for dropping their cards. With late fees, annual fees, foreign transaction fees, and other hidden costs, it's getting harder and harder to make credit cards worth the cost.
Credit card firms aren't unique in facing a pricing dilemma. The world is increasingly mired in a cost of living crisis, and people are taking more of a hard line against any spending that can't be justified. Some credit cards cost hundreds of dollars per year and can make very little sense for those on tight budgets.
Even "free" credit cards come with a price tag. Many of them come with steep interest rates, and customers are often unable to take full advantage of the rewards offered. Customers often find themselves maxing out their cards and unable to pay off the balance, leaving them in a cycle of debt that can be hard to escape.
Customer service is also a major issue for credit cards. Many customers are stuck in a long wait time loop or find themselves unable to get clear answers to their questions. Poor customer service can lead to a feeling of helplessness and frustration and can lead to customers looking elsewhere for a better experience.
Finally, personal circumstances also play a role. Identity theft is a major concern for many, leaving them feeling vulnerable and exposed. Others have simply had enough of credit cards, and feel the need to distance themselves from the cycle of debt.
It's clear that credit card companies need to take action if they want to keep their customers. They need to reduce costs, simplify policies, and offer better customer service if they want to remain competitive. Otherwise, it's only a matter of time before people start leaving in droves.
However, randomly picking features or slashing prices isn't the answer. Credit card companies need to take a data-driven approach to understand why customers are leaving and what they can do to retain them. Data science is the single most important tool in understanding customer behavior and designing effective solutions.
If they can get the data right and use it to find solutions, credit card firms can ensure they remain a trusted part of the consumer landscape.
Churn management is a challenge across industries, from the telecommunications industry to the financial services sector. A variety of data science techniques are used for credit card churn prediction.
Historically, firms looking to improve their customer attrition would hire computer science experts to build machine learning models, such as logistic regression and random forest. This was common even decades ago, as seen, for instance by 2004 research from Dirk Van den Poel which looked at demographic characteristics of existing customers to predict which new customers will be churners.
In reality, many features are selected during the process of feature selection, such as the transaction amount, transaction count, education level, marital status, and card category, or if the holder is a VIP or in some other atypical category. For instance, research by Nie, Tian, and Shi et al. used decision trees to predict churn at a real Chinese bank, based on feature selection from 135 variables. Preprocessing is also important, as is data engineering and hyperparameters optimization.
By conducting data mining on your data of credit card holders, or more broadly on bank customers, you can build an original dataset to create a classifier that predicts churn. These learning techniques require validation with metrics such as ROC and AUC, or the Receiver Operating Characteristic and Area Under Curve. These error metrics are discussed in detail in the literature over the years, such as in this IEEE international conference paper of 2005, by Huang et al.
The research on customer churn analysis is ongoing. Recent papers, such as a 2023 paper published in Springer, uses training data from Kaggle to build a highly accurate churn forecasting model.
Several different data types can be used in these models. For instance, structured data analysis involves looking at organized records such as sales transactions, customer profiles, and card usage data. This type of analysis is used to identify patterns and correlations in customer behavior.
Unstructured data analysis, on the other hand, is used to analyze unstructured data such as emails, social media posts, and customer feedback. This type of analysis helps identify customer sentiment and feelings toward a particular product or service.
In addition, banks are also using natural language processing (NLP) to better understand customer conversations. By leveraging NLP techniques, financial institutions can gain insights into customer conversations and make more personalized offers. This can help banks better tailor their products and services to customers, increasing customer loyalty and generating more revenue.
These techniques use a number of different machine learning algorithms such as decision trees, neural networks, and support vector machines. Decision trees, named for their branch-like structure, split data into smaller groups and can be used to identify patterns and trends in customer data. For instance, one "split" could be determining whether customers who spend a certain amount of money are more likely to switch cards, while another split could look at the types of purchases customers make.
Neural networks are another type of machine learning algorithm that can be used for credit card churn prediction. Neural networks are based on the structure of the human brain and can learn from data and make predictions. For instance, a neural network can learn from customer data to identify patterns in spending habits and help financial institutions better design credit card offers.
Finally, support vector machines are used for classification problems such as determining whether a customer is likely to switch cards. Support vector machines create a hyperplane to separate data into two groups and can be used to identify patterns and trends in customer data.
Building and deploying accurate and scalable AI models is no easy task. From data wrangling, feature engineering, and model selection, the process requires deep technical know-how and considerable development resources.
Akkio is a cloud-based ML platform that allows users to quickly build and deploy ML models without any coding. Our platform is designed to work with the most popular big data sources, such as Snowflake and BigQuery, and can be integrated with existing applications.
Akkio is designed to make building and deploying ML models for credit card operations easy. The platform’s visual interface allows users to quickly connect to their existing data sources, select columns for specific ML tasks, and deploy the models to wherever they need them.
For example, if a credit card business wants to create a model to predict customer churn, they can use Akkio to connect their data sources and select the ‘churn’ column. Akkio then automatically builds and deploys the model to the desired destination.
Akkio is also designed to be used for many other applications, such as fraud detection and customer segmentation. The platform’s point-and-click interface makes it easy to create, deploy, and monitor ML models for a wide range of tasks.
Simply put, Akkio is a powerful ML platform that makes it easy for credit card businesses to apply machine learning to their operations. It provides an intuitive drag-and-drop interface that allows users to quickly connect to their data sources, select columns for specific tasks, and deploy models wherever they need them.
Financial services firms also use Akkio for credit card fraud detection, building credit approval bots, and more.
To demonstrate the ease of use of Akkio, you can try out a credit card churn prediction model below. This model will allow you to input your data and receive a prediction of how likely customers are to churn.
You can either manually enter sample data into the form, or connect your own data source to get a prediction for your own customers. Such an embeddable model is one of the many deployment options you'll have when using Akkio. Try it out and see just how easy it is to apply ML to your credit card operations.
Customer churn is one of the biggest challenges facing businesses today. It can be incredibly difficult to predict when a customer will decide to leave, and even more difficult to figure out how to prevent it.
However, there are a few essential steps businesses can take to reduce customer churn and keep customers coming back for more.
Not all credit cards are created equal, and customers don't always have the same payment preferences. For instance, some customers prefer automatic credit card payments, while others may prefer manual payment options.
By offering flexible payment options, businesses can make it easier for customers to pay and reduce the chances of them abandoning their orders.
Customers want reliable and fast support when they need it most. If they're having trouble with a service or facing issues while using it, they'll look elsewhere for something better. Make sure you have easy access channels like live chat and phone support available 24/7 so that you can respond quickly when needed.
And of course, customers want to be treated like the individuals that they are. Sending personalized emails and messages will help build rapport with them which in turn will help retain them better.
No matter how great your product or service is, if users struggle to use it, they'll be unlikely to stick around. Make sure you spend time optimizing user experience so that customers can easily find what they're looking for.
By focusing on these key areas, businesses can reduce customer churn and build a loyal customer base. However, it's important to remember that customer churn prevention is an ongoing process, and businesses should continuously monitor and adjust their approaches as needed.
Credit card companies face a volatile market, with high churn rates and plenty of competition. From Revolut to Apple Card, customers have more options than ever before, and it’s becoming increasingly difficult for credit card companies to retain them.
Data science and machine learning are the key to understanding customer behavior and predicting churn. Akkio is an easy ML platform that makes it easy for businesses to apply machine learning to their credit card operations. By leveraging the platform’s drag-and-drop interface, users can quickly connect to their data sources, select columns for specific tasks, and deploy ML models wherever they need them.
In one case, Sterling Strategies, a campaigning firm, used Akkio to save 6 months of development time, achieving a 2.2X return compared to previous methods and supporting 5X annual revenue growth.
Akkio is the perfect tool to get started with AI-powered credit card customer churn prediction. Try Akkio today and see how easy it is to apply machine learning to your credit card operations!