When an epic disaster occurs somewhere in the world, images of devastation are conveyed instantly around the world through newspapers, television and the Internet. The result is an outpouring of aid, often in the form of donations to nonprofits like the American Red Cross. But once the dramatic images and headlines begin to fade, donors often disappear as well.
The question for groups like the Red Cross is how to identify and reach out to those one-time givers who are most likely to become regular donors. “The single biggest channel through which we can acquire new donors is in response to a disaster,” says Tony DiPasquale, senior director of market intelligence for the Red Cross. “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.”
Solving that puzzle could be a boon for the Red Cross and nonprofits like it that need to find cost efficient ways to improve their fundraising. The answer may lie in the world of customer analytics, the collection and mining of data on individual consumer behavior that is already revolutionizing how for-profit businesses operate. Through a partnership with the Wharton Customer Analytics Initiative (WCAI), the Red Cross has linked up with six teams of researchers from around the country, including analytics experts from Baylor University, the University of Pittsburgh and the IBM Watson Research Center who will analyze data from the Red Cross database to develop tools for improving the organization’s outreach efforts.
The payoff for nonprofits like the Red Cross may be as valuable as it has been for businesses that have mastered the analytics field. “This could have a huge impact, because efficiency in [the nonprofit] industry really matters,” notes Eric Bradlow, a Wharton marketing professor and co-director of WCAI. “Converting disaster donors to lifetime donors is crucially important.” But nonprofits looking to harness this sort of data will face the same challenges seen in the for-profit world, Bradlow and other experts say, including effectively translating analytical insight into new outreach efforts and finding the right balance between tracking customers and respecting their privacy.
Reduced Gifts and Lapsed Donors
The Red Cross has good reason to be concerned in the current economy. According to the 2011 Fundraising Effectiveness Survey Report by the Association of Fundraising Professionals and the Center on Nonprofits and Philanthropy at the Urban Institute, net giving has not recovered to pre-recession levels. The survey found that for every $5.35 in gift dollars that organizations received from donors in 2010, $5.54 was lost through reduced gifts or lapsed donors. “Giving rates still have a long way to go before we reach pre-recession levels, and it all begins with reducing the number of lapsed donors,” Andrew Watt, CEO of the Association of Fundraising Professionals, said when the report was released in the fall of 2011. “This is one of the biggest challenges charities face — losing nearly 60% of donors every year and relying too heavily on new donors. It’s much less expensive to retain and inspire existing donors than it is to find new donors, so charities should focus on stewarding their current donors and reducing losses there.”
For the Red Cross, retaining disaster donors is critical to achieving its mission. Janet Couperthwaite, account director with marketing and communications firm Russ Reid, which advises the Red Cross on outreach efforts, points out that in addition to disaster relief, the Red Cross delivers aid and services — everything from responding to 60,000 house fires a year in the U.S. to CPR training to babysitting classes — that require stable, year-round funding. According to DiPasquale of the Red Cross, in a year following a major disaster, less than 10% of those who give become repeat donors. “Those folks are less likely to return unless we see another event of that caliber,” he says.
The Red Cross’s project with WCAI is aimed at changing that equation. The six teams selected for the project will be studying a data pool of more than 500,000 donors to the organization who made a contribution between 2006 and 2011. The goal is to come up with new tools to improve fundraising efficiency. The IBM Watson team, for example, will be creating software tools that give the Red Cross new ways to visualize and work with the data. “Data visualization is a hot topic,” notes Kurt Kendall, a leader in the Consumer Marketing Analytics Center at McKinsey. “There is a whole body of research that shows human beings are very bad at looking at a table of numbers and drawing conclusions from it. But the human brain does very well on visual pattern recognition.”
Another group, this one from Baylor, will be studying how communication directed to donors from the organization affects response rates. “They are going to look at [donor] letters to see what kinds of messages they include, and what is the psychology [behind] the message that makes a person engage,” says Elea Feit, a Wharton marketing lecturer and research director of WCAI. (To learn more about WCAI’s collaboration with the Red Cross, see the video above: “Using Data to Help Convert Red Cross ‘Disaster Donors.'”)
No More ‘Shooting from the Hip’
In embracing customer analytics, the Red Cross will be following the lead of corporations that have used analytics to overhaul their marketing operations in recent years. “In marketing, we have largely been making decisions by intuition,” notes Feit. “It was a shoot-from-the-hip type of world. But over the last 10 years, as marketing migrates to platforms like the Internet and direct mail, we can see what is really going on and what ads prompt a purchase. There is an opportunity to leverage that data and add some rigor to the creativity of marketing.”
The customer analytics market is large and growing rapidly. While it is difficult to put a dollar figure on the entire field of customer data — which includes everything from social networking sites to search engine tracking businesses — the market for business intelligence, analytics and performance management software alone in 2010 exceeded $10 billion, according to research firm Gartner. “Individual level data has become ubiquitous,” says Bradlow. “Pharmaceutical companies capture the prescription writing activities of a doctor; credit card companies keep information on purchases; nonprofits keep records of donations — most industries today are collecting data at the individual customer level.”
In fact, whether it is information that companies and nonprofits collect themselves or data pulled together by third-party aggregators, the amount of information that is accessible is staggering. Unlike the days of catalogs and call centers, “today, if you look at all the contact touch points companies have with their customers, it is easily in the double digits,” notes McKinsey’s Kendall. “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.”
For corporations looking to strengthen the bottom line, the potential payoff of making sense of that data can be tremendous, so the demand for improved analytics is high. Recently, WCAI led a project for online travel giant Expedia to help the site figure out which hotel options to show to consumers visiting the site. And online ticket operator StubHub paired up with WCAI to figure out how to tailor their marketing — both the type of pitches and the frequency of the messages — to improve their level of repeat business. Doing that effectively “will save money because it gets to the efficiency of advertising,” says Bradlow. “And on the revenue side, it is about maximizing customer lifetime value.”
Making Sense of an Avalanche
But for every example of a firm that has mastered analytics, there are numerous organizations struggling with how to make sense of the avalanche of available customer data. Mark Jeffery, senior lecturer of technology information management at Northwestern University’s Kellogg School of Management, studied 250 large firms in 2007, in part to assess how well they were managing customer data in their marketing operations. Looking at metrics including sales growth, return on assets and figures measuring their marketing efficiency, Jeffery found that less than 18% were using their customer data effectively. “The vast majority of firms are not doing it well,” he says. “It is not enough to buy the data warehouse and the analytics. You have to change your organization and your marketing processes to take advantage of that information.”
Bradlow sees the same struggles going on at many firms. “The data capture side is fairly well mature,” he says. “The part that is in the nascent stages is what to do with that information. I think [most] companies are still trying to get their head around the level of customer analytics they need and how it will change their business model.”
A wave of new tools introduced over the last two years aim to make all this information accessible and useful to managers in charge the business decisions. “In the past, when you did analytics you had very smart people working with a small amount of data,” notes Rod Smith, IBM’s vice president of emerging technology. “Now you have huge amounts of data, and the question is, how do you sort through that quickly?” More solutions are likely forthcoming, he predicts, and they will lead to a revolution in how data is used. “Years ago, we had accountants who did ledgers. Then spreadsheets changed that. I think we are going through that transformation now. We will still need business intelligence experts. But now we are seeing [others] who want to be empowered to go off and work with the data and drive the business.”
Fancy new tools, however, don’t guarantee a payoff from analytics. Organizations need to undergo cultural change as well. Peter Fader, a Wharton marketing professor and co-director of WCAI, says organizations too often use data to justify decisions or programs already under way, as opposed to using the information to create entirely new initiatives. “There are a lot of companies that would call themselves ‘data-driven’ that are using this in a passive way,” Fader notes. “People are afraid to trust data too much. They often trust their gut more.” Gartner research director Gareth Herschel agrees: “If the analysis finds something surprising, then do I trust it? Am I willing to change what I’m doing? There is a lot of organizational inertia that has to be overcome.”
Looking for a Clear Line
If the practical hurdles in the analytics field are daunting, the privacy stumbling blocks are just as challenging. As companies can access more detailed information on individual customer behavior, the rules for using that information responsibly are evolving. “Firms are trying to balance the consumer privacy issue with making a customer’s experience better,” says McKinsey’s Kendall. “It is a hard balance. Plus, some people could care less [about the sharing of their information], while some are very opinionated. There is not a clear, bright line between what is acceptable and what is not.”
The privacy issues are just as — if not more — critical for nonprofits that rely on an altruistic reputation to attract donors. In the case of the Red Cross/WCAI collaboration, the database that is being studied contains no demographic information. “We want to avoid anything that would lead to personal identification — any opportunity where you can, through reverse engineering, figure out who’s who,” says Fader.
Moreover, Fader argues that having personal information about donors would not add much, anyway. “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. “Firms are wasting their time chasing some of this data.”