By: Pinaki Sahu, International Center for AI and Cyber Security Research and Innovations (CCRI), Asia University, Taiwan, Taiwan, firstname.lastname@example.org
In the world of banking, retaining customers is a top priority. Customer churn, or the rate at which customers leave a bank’s services, can have a significant impact on a bank’s bottom line. To deal with the customer churn effectively, banks are turning into advance level analytical and machine learning to develop the prediction model. Preventing customer churn is not only crucial for corporate development but also a key component of the client Relationship Management (CRM) strategies. This article explores into world of the customer churn in the banking industry for predicting the churns and the strategies needed to achieve ambitious goals set for high customer retention.
In this competitive world of banking industry, maintaining and building more customer relationship is a primary pursuit. In this venture to the never ending challenge of customer churn is the rate at which the customers are disengaging from a bank services. Customer acquisition is very important as well as retaining the customer is equally vital for the sustainable growth and profitability.
In the banking industry, many banks have reached a critical point where the conventional or old strategies and approaches that bank have traditionally used to retain their customers are no longer effective or suitable. Modern customers are
more decisive and selective than ever before. Their demand is not just financial services but also need a personalised, seamless and continuously satisfactory experience. Banking industries are facing this reality, and finding ways for adaptation and innovating their services to meet the evolving expectation of today’s customers.
The motivation behind is to go deep into the complex problem of customer churn in banking is versatile. Firstly, financial consequences are can’t be ignored or denied. When customers leave from a service , banks not only loose existing revenue streams but also face increasing cost of acquiring new customers to fill the gap. In the highly competitive banking industry, banks are actively seeking to attract their customers and retention of customers becoming a top priority.
Secondly, the advantage of data-driven technologies becomes a wide range of new opportunities they have unlocked. Technologies like big data analytics, machine learning and predictive modelling has become a effective tools for banks to tackle customer churn. This has opened up new opportunities to prevent churn events with greater accuracy and implement of improved retention strategies.
The Churn Challenge
Customer churn is still a problem for banks, with millions of customers leaving each year. This mass departure of users has an effect on both profitability and growth. To address this issue, the banking industry is turning to advanced analytics and machine learning to create predictive models capable of identifying future churners and taking proactive actions to retain them.
According to recent data, a shocking 96% of consumers are likely to switch to another bank if they receive terrible customer service. Customers who want better customer service have shown a strong inclination to switch banks (27.9%). 32.7% of people fall into the very switchable category. 35.5% show some sign of switch ability. These statistics highlight how crucial it is to handle customer churn in the banking sector, with customer service quality playing a big role. The difficult job in customer retention is how successfully the model can identify and interpret unrealistic consumer behaviours for instance, if a customer does several large-funds transactions with other banks. Another challenging task is to identify the cause of the customer churn for example, poor services.
Factors Contributing to Customer Churn in Banking
Customer churn, or the loss of customers, is a significant concern for the banking industry. Several factors contribute to customer churn in banking, as identified by various studies.
One factor is the quality of a bank’s products and services. Lappeman et al.  found that the quality of a bank’s products and services, along with its reputation and credibility, were significant factors influencing customer churn. Similarly, Mavri and Ioannou  examined variables representing characteristics of customers and offered services and products and found that these factors contributed to customers’ switching behavior.
Another factor is customer satisfaction. Asiedu et al.  highlighted that customer satisfaction is a crucial predictor of customer retention. Additionally, customer belongingness, or the sense of connection and attachment to the bank, was identified as a significant contributor to customer retention .
Employee attitudes and customer service also play a role in customer churn. Lappeman et al.  found that employees’ attitudes directly affect customer churn. Jamalian and Foukerdi  defined customer churn in the banking field as customers who close their accounts, indicating that negative experiences with customer service could lead to churn.
Price and denial of services are also factors contributing to customer churn. Lappeman et al.  identified pricing and denial of services as key factors of churn in the retail banking sector. Qin et al.  highlighted that customer churn increases the cost of marketing new customers, making it more expensive to acquire new customers than to retain existing ones.
The Power of the Prediction Model
There are many different types of prediction models for overcoming the customer churn. The first step in building the algorithms to estimate client attrition involved using data mining techniques like Logistic Regression, Decision tree , SVM , KNN, Gradient Boost, Random Forest and so on. These models can predict the likelihood of a client leaving in the near future by using historical data, consumer behaviour patterns, and a variety of factors.
Here is an example for Customer Churn Prediction Using Various Machine Learning Techniques (Figure 1).
Here are two popular customer churn datasets for performance evaluation of the prediction models.
- Telco Customer Churn: This dataset is concerned with initiatives to keep customers.
- Predicting customer churn for banks: This dataset is primarily relevant to the banking industry.
- The fact that these datasets include details and factors like the number of features, the volume of data, and the time period covered makes it possible for researchers to create and test prediction models successfully.
Table 1: Accuracy Table
Effective Predictive Strategies for Banking Success
Customer segmentation is a vital component of predictive strategies for banking success. By breaking down your customer base into distinct segments, you can better understand their needs and behaviors. Here’s how it can benefit your bank -:
- Explain how identifying high-risk customer segments can be beneficial: High-risk customer segments are those that are more likely to churn. These could be customers with declining account activity, negative feedback, or other warning signs. Identifying them early allows you to allocate resources where they’re needed most.
- Discuss tailoring communication and offers to specific customer segments: Once you’ve identified high-risk segments, tailor your communication and offers to address their specific pain points. For instance, if a segment is sensitive to fees, consider waiving certain fees temporarily or offering fee-free services to retain them.
Personalized Customer Engagement
Personalized customer engagement is all about making your customers feel valued and understood. Predictive analytics plays a crucial role in achieving this.
- Describe how predictive analytics can enable personalized interactions: Predictive analytics uses historical data and machine learning algorithms to predict what each customer might need or want. For instance, it can predict when a customer is likely to need a loan or when they might be interested in a new savings account. This allows you to offer tailored product recommendations and communication.
- Emphasize the importance of proactive issue resolution: Predictive analytics can also identify potential issues that might lead to churn, such as a pattern of unresolved complaints. By addressing these issues proactively, you can show your commitment to customer satisfaction and prevent churn.
Early Warning Systems
Early warning systems are like the “canaries in the coal mine” for customer churn. They help you spot signs of trouble before it’s too late.
- Discuss the concept of identifying churn signals in advance: Churn signals can be subtle, like a decrease in login frequency or a drop in account balance. Explain how predictive models can recognize these signals by analyzing historical data and patterns.
- Share strategies for timely intervention: Once you’ve identified churn signals, outline strategies for timely intervention. This might involve reaching out to the customer with a personalized offer, a call from a customer service representative, or a targeted email campaign. The key is to address the issues before they escalate.
Loyalty Programs and Incentives
Loyalty programs and incentives are classic tools for retaining customers. When used strategically, they can significantly reduce churn.
- Explain the design of effective loyalty programs: Loyalty programs should offer tangible benefits that align with the needs and preferences of your customers. Describe how your bank’s loyalty program works and how customers can benefit from it.
- Discuss how rewarding customer loyalty can reduce churn: Rewarding loyal customers not only incentivizes them to stay but also encourages them to deepen their relationship with your bank. Share success stories or statistics that demonstrate how loyalty programs have reduced churn in the banking industry.
Finally, reducing the customer loss is top priority for banking industry, and predictive analytics is great tool in this study. Therefore, it is important to explore and compare various data mining strategies as we go through complex world of customer loss. By analysing the performance of various algorithms, we can conclude the dataset performs well with the random forest model, with an accuracy of about 85.8%, and that, following customer segmentation, the mean accuracy of each model performed well, with logistic regression having the lowest accuracy (78.56%) and random forest having the best (85.8%). Predictive analytics will continue to be essential for banks to succeed in the long run as they work to increase customer retention rates.
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Sahu P. (2023) Unlocking the code of Customer Churn: Predictive Strategies for Banking Success, Insights2Techinfo, pp.1