Given that predictive analytics software is increasingly easier to use, it’s no surprise the technology is being adopted more and more in the financial services industry. In general, it is applied there in two ways:
- Against customer data
- Against internal and market data for risk management
While both uses are predictive, there are large differences between the results. Using customer data, banks and other financial institutions are applying the technology to predict customers likely to churn and then taking action to prevent the churn from occurring. Predictive analytics identifies customers likely to churn, then segments those customers by profitability, volume, and length of engagement.
Once segmented, banking business analysts, often working in tandem with marketing and sales teams, again apply the technology to optimize marketing campaigns that ensure exactly the correct incentives are offered to each class of customers. This results in higher retention rates at lower costs, and can improve the customer experience by more precisely offering promotions that appeal to them.
Financial services institutions also use predictive analytics to segment customers and predict which ones will react well to cross-selling promotions. Since it is widely reported that it costs credit card issuers, for example, up to $200 to attract each new customer, banks are eager to recoup their costs early in customers’ lifecycles. Cross-selling is a popular strategy for doing so. Mortgage borrowers may be open to opening retirement accounts, while credit card users may be interested in mortgage offerings. Predictive analytics is ideal for classifying which customers are likely to respond to offers for additional products and services, allowing banks to achieve profitability in the near term, and add to the bottom line over time.
Read the full article on Wall Street & Technology.
Ingo Mierswa is an industry-veteran data scientist, beginning with the development of the RapidMiner platform at the Artificial Intelligence Division of the University of Dortmund, Germany. He has authored numerous articles about predictive analytics and big data. ... View Full Bio