Author: Linda Rosencrance
Spotfire Blogging Team
Did you know those stuffy old financial institutions steeped in tradition have joined the ranks of 21st century companies by employing predictive analytics , these to improve customer service? Yup, using predictive analytics, banks are devising more effective ways to manage their relationships with customers including developing better advertising and marketing campaigns; determining customer buying habits (up selling and cross-selling initiatives); and creating long-term customer loyalty, retention, customer screening and rewards programs.
For example, Chase “has harnessed the power of predictive analytics to make data-driven decisions about its consumer loan and myriad other lines of business,” according to this blog post by Beth Schultz. The bank is analyzing its data to target customers with products of interest to them. And one customer, at least, seems to be all for it.
This customer was shocked—pleasantly so—to get a letter from Chase suggesting she consider refinancing her mortgage. Chase hit her with that idea because she was such a great customer.
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And how do you think Chase knew she was such a super customer and that she qualified for its “lower your mortgage” program for valued customers? Well, predictive analytics, of course.
There are a number of ways banks use predictive analytics to improve customer service, according to Schultz colleague’s, Cordell Wise.
Wise says, “Believe it or not banks have developed fairly sophisticated processes for optimizing customer value that are effective, practicable, and adaptable.”
Wise gave Schultz some examples of how financial institutions use predictive analytics:
- Credit card companies use predictive analytics to manage credit lines and collections as well as to target customers with exactly the right direct mail campaigns
- Insurance companies use predictive analytics to set premiums
- Banks, insurance companies and even government agencies have turned to analytics to root out fraud.
In fact, Wise says any company can use predictive analytics to anticipate risk, including interest rate risk in financials or price (market) risk in commodities or exposure in energy markets.
Banks can also use analytics as interactive tools to help them plan their strategies and make better decisions by creating interactive maps of the problems they’re trying to solve “complete with decision levers, reactions to decisions and ultimate objectives,” according to this article.
By mapping the connections among them, banks—or any company for that matter—can see the assumptions they’ve made, then use those assumptions as well as the pertinent data to “see what happens” when they change some of their assumptions.
Bottom line—banks will make better decisions if they use predictive analytics, than if they don’t.
Next Steps
Want to learn how to make better decisions with predictive analytics? Register for our live webcast Predictive Analytics with Spotfire tomorrow, November 4th, at 11:00 AM EDT.
Linda Rosencrance
Spotfire Blogging Team
Photo Courtesy of JohnLund.com – “The Stock Photo Guy”
