A Business Intelligence Approach For Revolutionizing Customer Retention

ABSTRACT—

MATLABSolutions demonstrate In this task we are going to The report is focused on understanding the summarises a complete Business Intelligence (BI) solution for the banking industry's pressing problem of client attrition. The loss of customers, sometimes known as "churn," is a major issue for banks. This business intelligence initiative aims to optimise customer services & sustain corporate growth by proactively identifying and retaining consumers at danger of churning. Beginning with a manager's worry that too many consumers are cancelling their credit card services, the project identifies the business problem that has to be solved. The BI solution takes advantage of a Random Forest model, which is adept at managing complicated datasets and generating reliable churn forecasts, and applies it to the "BankChurners.csv" dataset, which comprises various customer attributes and behaviours over 200 discrete segments. Insights regarding the model's accuracy, precision, recall, as well as feature relevance can be gleaned during the evaluation process. Users may easily navigate client categories and churn patterns with the help of the dashboard's dynamic design. In the final section, reflection on the project's successes, highlight its shortcomings (such as the lack of complete historical data and the unequal distribution of its benefits), and make suggestions for its future growth. To further strengthen client retention efforts, potential future work could involve real-time data integration, improved predictive analytics, and personalised churn avoidance techniques. This BI solution provides a holistic method for reducing banking industry customer attrition. To equip stakeholders with the knowledge necessary to make educated decisions and maintain consumer loyalty, it blends predictive analytics, feature importance analysis, and an interactive dashboard.

Introduction—

The goal of this project delves into the pressing issue of customer churn within the banking sector. Financial companies face particular difficulties as a result of customer attrition because keeping existing customers is more economical than finding new ones. The banking sector has seen an increase in the number of consumers cancelling their accounts, which has made churn-reduction tactics more important. One dataset that is especially pertinent to the banking business is "BankChurners.csv," which includes a range of customer attributes and financial behaviors. It provides a realistic depiction of the data landscape inside the industry. Within this framework, the Business Intelligence (BI) solution offers customized insights specifically for the banking industry in an effort to proactively address the problem of client attrition.

The Corporate Issue

Customer turnover is a rising problem for the financial services industry, notably for credit card providers. In today's cutthroat economy, maintaining consumer loyalty is crucial to success. When a large number of consumers leave a business, it suffers financial losses and must spend money to replace them (Vo et al. 2021). The organisation is working hard to create methods that will reduce client churn despite the difficulties it faces. Finding the customers most likely to defect and developing strategies to keep them as customers are two sides of the same coin.

Case Study and Related Dataset

The dataset used in this research is comprised of several different types of information gleaned from the company's past records, such as client demographics and financial transactions. Personality characteristics of consumers include their ages, sexes, levels of education, marital status, jobs, incomes, and methods of making purchases (Borah et al. 2020). Credit limits, flowing balances, as well as transaction details are just some of the financial information included in the collection. This dataset has been carefully chosen to have the right balance of variety and complexity. This was done on purpose to ensure that the model's efficacy and the breadth of its insights would not be compromised by a reliance on extremely simplified inputs.