Chris Catliff has seen the future for credit unions and it looks more like a Four Seasons Hotel or an old-fashioned village butcher than it does a traditional financial institution.
Catliff, president and CEO of B.C.-based BlueShore Financial (40,000 members, $3.5 billion in assets) envisions a world where as members approach a branch, their iPhones ping and they receive a message telling them they have been virtually “checked in.” As members enter, their pictures and customer information profiles pop up on a concierge’s screen, including any reference to the reason for their visit. They are greeted by name and ushered to an appropriate employee who handles their transaction. Or, they may walk over to an ATM that uses biometrics from their iPhone to authenticate them. The instant teller then offers them their usual transaction based on past records, such as a $100 withdrawal.
Catliff likens the experience to visiting the village butcher of past days, who often knew which cut of meat a client wanted. “Our strategy is to use our ‘bluegorithms’ to make the person-to-person experience that much more dramatic and helpful,” he says, creating a branded play on the word algorithm.
He further predicts that within three years, simply telling members to come into the branch to sign their loan documents is “really not going to cut it.” Service, not price point, will be the key competitive differentiator, he says – and success will lie in defining what constitutes service. “It has to be electronic transactions with the introduction of superior, human touch at various critical points. It’s high tech and high touch.”
Getting to Catliff’s final destination isn’t just about technology or a well-trained customer service staff. It’s about accessing information buried within a credit union’s legacy banking systems and combining that with externally available information from credit score companies, StatsCan and other resources, either purchased or free. Many believe that’s how to grow wallet share and attract members who are a great fit.
The data deluge
Welcome to the blossoming world of Big Data. It’s a place where machine learning is used to analyze and identify relationships and patterns – so-called predictive analytics – from evermore rapidly gathering mounds of digits. These analyses might reveal who needs a new loan, which member is likely to leave the credit union and how to find best-in-class products. The goals are to sell more financial products, underwrite more business and keep clients content. The information derived might also be used to find ways to reduce credit defaults and risk.
The data explosion is very real. Experts used to talk in terms of gigabytes and terabytes (one trillion bytes). Now they refer to petabytes, exabytes and zettabytes, each category exponentially larger than the previous. To put it in context, the amount of data we create and copy, including bank card-swiping information, electronic highway tolls, airport security video, YouTube videos, photos, voice calls and texts – is doubling every two years, according to the research firm International Data Corporation (IDC). It will grow by a factor of 10 to 44 trillion gigabytes in 2020 from 4.4 trillion gigabytes in 2013. That is more than 5,200 gigabytes for every man, woman and child expected to be living on the Earth.
In 2013, about 22 per cent of the digital universe was considered useful for analysis, yet only five per cent was actually looked at. By 2020, useful data will climb to 35 per cent. Managing that information explosion will be a game changer in how companies market to consumers.
Big Data is Moneyball
Darren Meister, an associate professor in innovation and entrepreneurship at the Ivey School of Business at Western University in London, Ontario, says “this is Moneyball” – a reference to the popular Brad Pitt movie, based on a Michael Lewis non-fiction book. The story covered how the Oakland Athletics, which had a limited salary budget, revolutionized baseball and won games by using data analysis to determine which players to sign and draft. The method has since been expanded to other sports.
The retail industry has long been at the forefront of Big Data and analytics, improving sales by using a range of available digital information to determine product placement and pairings. The healthcare and insurance industries are using Big Data to detect fraud and big banks are using it to get a deeper understanding of customer needs.
Volume, variety and velocity
Big Data has more definitions than most big banks have branches, prompting many to question the usefulness of the phrase. But Meister says common acceptance is that Big Data has three “Vs” – volume, variety and velocity. Volume is the quantity of data, variety represents the types of disparate data sets to be analyzed and velocity, of course, is speed.
“We can track data a lot faster than we used to,” he says, referring to real-time transaction monitoring. Still, a company can’t expect to start analyzing information simply by plugging in some new servers, Meister warns. It can take three to five years before Big Data projects bear fruit, he says, adding that a company needs a body of historical data in order to apply predictive analytics. “Past performance is a useful proxy for future performance,” he points out.
Challenges for credit unions
There’s the rub for credit unions. Many have recently gone through a round of consolidation and are still dealing with technology integration. In fact, some credit unions Enterprise contacted declined to comment because they were enhancing their systems.
There’s also the challenge of using banking platforms as a data repository. Experts say they are simply not designed to allow for the type of slicing and dicing that needs to be done.
Nonetheless, some credit unions are making progress. For BlueShore, its data journey began in 2000, when the company added a customer relations management (CRM) system, which allowed better tracking of member information. Since then, BlueShore’s assets under management have grown to $3.5 billion from $800 million, without enlarging t~e membership base. Instead, the focus has been on expanding wallet share among existing members and working to reduce attrition.
Catharine Downes, vice-president of marketing, and Richard Burg, senior manager of business process solutions, say the strides BlueShore has made point back to having been an early adopter of the CRM and having management buy in. Downes says the system provided a “treasure trove of information,” which helped BlueShore to segment clients.
Making use of these new insights required putting faith in the numbers. Burg says that applying analytics to customer transaction data “removes as much emotion from decisions as possible.” The benefit: If analysis suggests a client is ready to be pitched on a new mortgage product or to entertain a call from a financial advisor, it makes it easier for staff to make that call.
Burg says that applying analytics to customer transaction data “removes as much emotion from decisions as possible.”
Servus Credit Union (377,000 members, $14 billion in assets), based in Edmonton, is also taking a bigger step into the data game. Gail Stepanik-Keber, chief brand and corporate social responsibility officer, says “we have a business intelligence project underway where we are warehousing all of our data.” She notes her organization “just came out of a five-to-six year integration project” stemming from its 2009 merger with Apex Credit Union.
“In order to get great data . you need to be one banking system,” she says. Servus has collected a year’s worth of historical data regarding its members, allowing the credit union to do some predictive modelling. Stepanik-Keber is reluctant to call it a Big Data project, because it isn’t integrating internal data with external information sources. Rather, it’s segmenting business lines and building out member personas. “We need to be very targeted. We can’t be all things to all people. [But) we have refreshed our brand as a result of all this research [and) are now louder in the marketplace,” she says.
Tackling the big, hairy monster
Meanwhile, Ontario’s Meridian Credit Union (266,000 members, $10 billion in assets) is well down the path of working with Big Data. It’s all in a bid to garner a larger wallet share from existing members and identify the traits and habits of coveted members.
Aimee Talbot, senior manager, member insights, says “the idea of segmentation and using data to understand and attract new members seems like this big, sort of hairy monster.” She says you can slay the monster, however, provided you break the data down and keep it “simplistic, actionable and understandable.”
One area Meridian is canvassing is “silent attrition” – analyzing and examining the trigger points that indicate when a member will leave – so that the credit union can take steps to prevent t hat before it happens. “Our immediate goal is to develop a deeper and more intimate understanding of what we can offer our members at the right time,” Talbot says.
The power of predictive analytics
It’s the predictive modelling aspect of Big Data that is most attractive for many credit unions. Catliff says BlueShore has crunched numbers that suggest it has as much as $120 million in potential new residential mortgages waiting to be tapped. Yet experts acknowledge that working with large data sets can be a challenge. First, you have to make sure your data is “clean” and can be trusted, says lvey’s Meister. As well, there’s the issue of how to combine structured data – information in a defined field, such as in a contact book – with unstructured text-heavy data, such as Word documents. Ensuring everyone in the organization records information the same way to allow for proper comparison can be tricky, too.
“The idea of segmentation and using data to understand and attract new members seems like this big, sort of hairy monster.” – Aimee Talbot, senior manager, Meridian Credit Union
Protecting privacy remains one of the biggest concerns. The IDC study found that only half the data that needs protection is actually safeguarded. Moreover, some of the secured-information categories of the digital universe are actually growing at a faster clip than the digital universe itself, spawning the need for more attention on privacy protection. That’s not lost on credit unions. Servus’s Stepanik-Keber says “one thing we always keep in mind is member privacy.” As Servus aggregates data, her team is always asking the question, “Are we infringing on personal privacy?” Meister adds it’s essential to get privacy protection right or you will be answering to a provincial privacy commissioner.
Making sense of all the new outcomes is time consuming, too. Says Stepanik-Keber “you don’t do this overnight. It’s an evolution and it’s a build. It’s like building layers of onion from the inside out.”
Big Data, big chills
Big Data can also be expensive. Meister says storage is cheap: it’s the management of the data that’s costly. Proficiency in analyzing data often resides outside credit unions. That can mean engaging with consultants to provide much-needed expertise. “We partnered with a lot of experts in the information services world in order to ensure that what we are building is to best practices. It’s worked very well,” Stepanik-Keber says, adding that going slow is a good policy. “A lot of companies fail at these projects because they try to do it all at once. Don’t bite off more than you can chew.” Finding such experts isn’t always easy, however. “There’s a war for talent,” Catliff notes.
Most importantly, say those at the vanguard, be careful not to approach Big Data as simply another technology upgrade. Properly used, new ways of looking at information should alter an organization’s culture. “It has to be [handled) as a changemanagement project, not as a technology-implementation project,” says Meister. “Big bang tech projects tend not to work.” That means training people and getting them comfortable with how information will be used on a go-forward basis.
BlueShore’s Catliff says that in a world where a typical consumer has 13 financial relationships, but only two that really matter, the stakes are high for retaining existing members and winning over new ones. Data and technology will be critical to success. “I believe that financial service is technology,” he says. “Our philosophy here is that the best ideas win, but bring your data.” ◊