February 1, 2012; Source: [email protected] | The American Red Cross is partnering with the Wharton Customer Analytics Initiative (WCAI) to study a pool of more than 500,000 donors who made a contribution to the Red Cross in the last five years.
The goal is to improve the Red Cross’ fundraising efficiency by examining customer data and then creating tools that can look for trends in the outcomes of different types of messaging on donor response rates. For example, analytics may provide messaging solutions for turning one-time disaster-response donors into those ongoing donors who support the organization’s core mission, a widely-acknowledged issue for the Red Cross.
Tony DiPasquale, senior director of market intelligence for the Red Cross, says that only 10 percent of those that give in response to a disaster return the next year. “The single biggest channel through which we can acquire new donors is in response to a disaster,” says DiPasquale. “What we have long had difficulty doing is moving these donors from being disaster-response donors to ones who support [our organization’s] core mission.”
This would be important to the Red Cross, which has run into major problems with “donor intent” by using the spikes of donations made to relief of one disaster for other efforts—most notably with regards to the Liberty Fund established after 9/11. In this effort to strategically target donors for effective dollar outcomes, the Red Cross and WCAI partnership will also work with six teams of researchers across the nation, including experts from Baylor University, the University of Pittsburgh and the IBM Watson Research Center.
Sign up for our free newsletters
Subscribe to NPQ's newsletters to have our top stories delivered directly to your inbox.
By signing up, you agree to our privacy policy and terms of use, and to receive messages from NPQ and our partners.
Peter Fader, a Wharton marketing professor and co-director of WCAI, says that analytics tools also come with risks and new responsibilities. Organizations that utilize such data must undergo a cultural shift if the information that comes from such models is to create new programs rather than merely justifying those programs or decisions already in place.
Kurt Kendall of the Consumer Marketing Analytics Center at McKinsey, says, “Today, if you look at all the contact touch points companies have with their customers, it is easily in the double digits…You have your website, related Internet sites, social networking sites and mobile devices. And the amount of data these channels create has expanded significantly, too. The technology has developed to combine all this data so that you have a 360-degree view of that customer,” he says. “That includes not only when customers interact with you, but also when they interact with someone else. That becomes a tremendous asset—but it can also be massively overwhelming. You can capture greater and greater amounts of information, but that doesn’t mean you are ready to use it.”
This, of course, may raise some privacy issues, but Fader argues that a lot of personal information is less than useful. “Demographics like race, income and gender tend to be very poor in terms of predictive power,” he says. Instead, more straightforward data—including the frequency of someone’s donations and the average size of their past transactions—are better indicators of their future behavior.”–Saras Chung
