Predictive analytics is widely used to solve real-world problems in business, government, economics and even science—from meteorology to genetics. Here are some examples of predictive analytics applied to customer decisions.
- Financial services: A large credit card issuer saw a $6 million profit boost for every million active accounts by using predictive analytics to assign an optimal credit line for each customer.
- Insurance: A large Brazilian insurer grew net profits by 130% using predictive analytics in its underwriting to reduce risk and grow revenue from profitable customers.
- Telecommunications: A major global carrier saved $70 million and decreased net bad debt by 25% in its first year of using an analytics-based collections solution. Collectors can pinpoint which accounts will repay the most.
- Retail: A mid-size specialty retailer generated an additional $250,000 in revenue per campaign and increased retention, using an analytics-based marketing solution to target customers and find the right marketing mix.
- Healthcare: A major commercial payer saw more than $20 million in annual savings using an analytics-based fraud solution to detect provider fraud and abuse, overpayment, and policy and system errors.
One industry that has clearly benefited from predictive analytics is the credit card industry. Over the past 40 years, predictive analytics revolutionized the credit card industry, changing the speed, consistency and objectivity of all types of customer decisions. The technology is now used in every phase of the customer relationship— from marketing and pre-screening, to approving applications, to managing relationships with existing customers, to collections and fraud, to the securitization of loans. Because analytics works almost instantaneously, lenders now make much faster—and sometimes instant—decisions on revolving credit. And decisions are more objective. Issuers’ systems base decisions on facts that have a proven relationship to credit risk, rather than human subjectivity. Similar groundbreaking transformations in the airline, transportation and other industries show that predictive analytics can not only improve a single company’s bottom line, it can make an entire industry more efficient.
