A Leading International Bank Improves New Account Activity and Customer Retention Using Predictive Modeling
Overview The banking and financial services sector has transformed tremendously over the past few years. The recent advances in analytics and predictive modeling techniques have further propelled businesses by offering powerful analytics tools to gain insights into the changing customer needs and behaviors. With the rise in the use of advanced analytics and data visualization [...]
The banking and financial services sector has transformed tremendously over the past few years. The recent advances in analytics and predictive modeling techniques have further propelled businesses by offering powerful analytics tools to gain insights into the changing customer needs and behaviors. With the rise in the use of advanced analytics and data visualization techniques, these analytics advances have begun to accelerate rapidly across industries. The potential benefits of these sweeping new advances and predictive modeling techniques are reflected in a variety of areas such as enhanced anticipation and prediction of possible customer churn, improved effectiveness of cross-selling and marketing activities, and greater efficiency and accuracy in anti-money laundering, and other compliance initiatives.
In such a complex business scenario, satisfying the growing customer base turns out to be a daunting task even for well-established banks. Though banks have been adopting various tools to address these challenges, factors such as ensuring long-term loyalty, customer retention, fraud detection, and credit risk management have always been key areas of concern. Facing similar challenges the client in this study realized that predictive modeling would help them address such issues. The client chose to partner with Quantzig to effectively address their challenges and to expand their knowledge of how modern tools and predictive modeling techniques could improve the efficacy of their existing business models.
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The banking client had been running a new customer acquisition program that focused on new customers relocating near branches with a cash incentive to open new bank accounts. Although the program had generated reasonably good results, the bank was anticipating reductions in available marketing budgets and wanted to lower program management costs while improving overall business outcomes.
Our experts worked closely with the client to develop a new predictive modeling process that makes accurate forecasts to best serve their business budget and operation planning needs. The devised predictive modeling process helped the client to identify influential attributes of new responders by categorizing the prospects into five groups based on their probability of response. The client targeted the top five categories that consisted of 60% of the new and most responsive user groups.
The use of a new predictive modeling approach delivered detailed insights and accurate predictions that helped resolve business uncertainties into profitable probabilities. To take advantage of the insights offered by the new predictive modeling approach and to make better, more profitable decisions, the client wanted to deploy predictive analytics models in their operational systems. A new business plan coupled with a robust predictive modeling platform delivered:
- A collaborative environment and shared framework for problem definition to ensure the analytics is solving the right problem
- A repeatable, industrial-scale predictive model
The solutions offered resulted in a stable predictive model with a performance that exceeded the client’s existing system, despite the considerable effort that had been invested in their existing model. Also, it’s essential to note that by focusing on the most responsive, new targets the client significantly increased customer acquisition rates and associated transactions while cutting down on their marketing costs. The predictive modeling solutions also empowered the client to fine-tune the audience based on various criteria to accurately predict acquisition campaign results. This, in turn, enabled the bank to optimize program performance on a continuous basis.
The adoption of predictive modeling techniques offered the following outcomes:
- Customer acquisition rates increased by 25%
- New account activity improved by 30%
- Significant reduction in marketing costs
What are the different types of predictive models?
Predictive modeling is an analytics discipline that leverages data and statistical techniques to predict future outcomes through data models. Predictive models can be broadly categorized into two categories such as- parametric and non-parametric. Though they sound like technical jargons, the basic difference between these models is that parametric models make specific assumptions about the characteristics of the focus groups used in developing them.
Specifically, some of the different types of predictive models include:
- Decision Trees
- Generalized Linear Models (GLM)
- Logistic Regression
- Random Forests
- Neural Networks
- Multivariate Adaptive Regression Splines (MARS)
- Ordinary Least Squares
Each of these predictive models differs in their specificity and the adopted approach. However, the overall goal revolves around effectively analyzing data to predict future outcomes based on past outcomes.
About the Client
A leading banking firm, with retail branches spread throughout North America. Being a Fortune 500 organization the client is well-known for offering a broad array of financial products and services to a diversified set of individual consumers and commercial customer groups.
Quantzig’s predictive modeling solutions that leverage advanced analytics and data mining approaches have helped solve real-world problems across diverse industries. Our areas of expertise include data science, data mining, data visualization, and predictive modeling. With experience in diverse projects and predictive modeling algorithms, and advanced validation techniques we can help you maximize project success to ensure a continued return on your analytics investments.
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