In Using Big Data, Focus On The Right Data

Credit unions are tying existing analytic capabilities to a number of institutional goals.

 
 

Big data doesn’t need to be some ethereal concept that lives off on the horizon. In many cases, advanced data capabilities are already driving predictive analysis, member segmentation strategies, operational efficiencies, and balance sheet growth among the nation’s cooperatives.

The key to making this analysis affordable, effective, and acceptable to membership (i.e., no Big Brother oversight) is to focus on the key issues and improvements that matter to members and the institution as opposed to hoarding fields of knowledge that encapsulate everything but are useful for nothing.

Below are two examples of how cooperatives can put advanced analytics to work today.

Identify And Encourage Profitable Behaviors

“If you ask five people to define Big Data you will probably get five different definitions based on their perspective,” says Charlie Vanderhoof, business intelligence officer for Affinity Federal Credit Union ($2.3B, Basking Ridge, NJ). “For us, it’s essentially using data from multiple sources to assist in decision making via objective insights. It’s what we need to know as opposed to what's nice to know.”

Affinity’s IT staff started collecting its big data by building onto its existing marketing customer information file (MCIF) system. It added various tables for credit union employees to access and integrate, including text-based searches for key words in description strings.

The credit union has also invested in an in-house acquisition of demographic data and information on its debit and credit card activity. Despite these internal advanced capabilities, when it comes to promotions that are more predictive in nature, Affinity still relies on its external vendors for support.

“Our initial steps were simply to obtain a more in-depth view of our members,” Vanderhoof says.

The credit union has used member data to examine how branch depositors compare to multi-channel depositors and identify specific traits among new members that indicate a better chance of increased engagement over a shorter time frame. The credit union is currently using member behavioral data to develop a relationship-based pricing program.

According to Callahan & Associates’ Peer-to-Peer Software, although Affinity is on par with its peers regarding average loan accounts per member — 0.53 versus 0.56 —  in fourth quarter 2012, its average loan balance was $10,000 larger than other credit unions in its asset size. Likewise, Affinity’s average member relationship is $19,682 — more than three times that of other credit unions with $1 billion or more in assets.

AVERAGE LOAN BALANCE
DATA AS OF DECEMBER 31, 2012
© Callahan & Associates | www.creditunions.com

avg-loan-balance

Generated by Callahan & Associates' Peer-to-Peer Software.

Building business intelligence requires an investment that can sometimes be costly. However, as more credit unions begin to see value in big data and data systems become more widespread, the price will adjust, increasing potential ROI even further.

“It’s kind of like HDTVs — what was expensive a short time ago will become more reasonable,” Vanderhoof says. “But the key will always be with the interpretation of the information.”

As data capabilities increase, there is the potential for credit unions to overstep boundaries with their members, particularly when using predictive data, Vanderhoof warns. By embracing only those data opportunities that benefit both the member and the institution, Affinity is making sure it does not risk its reputation for information it doesn’t need.

Put The “Market” Back In Marketing

Credit unions have a wealth of channels to get their messages across, but when it comes to securing the greatest ROI from online marketing, some institutions benefit by thinking smaller about their data.

For example, Community-chartered Mid-Atlantic Federal Credit Union ($276M, Germantown, MD)has started gathering online information needed for geotargeting — i.e., serving up specialized ads and content based on a consumer’s location. By focusing mainly on people who visit its website, the credit union ensures it is more succinctly targeting the current and potential members who can actually respond to its offers, rather than the general population.

“The DC area is an expensive media market,” says Marc Wilensky, vice president of marketing at Mid-Atlantic. “To reach people in Fairfax, Prince George’s County, and Washington, DC, on the outside chance they might work or live here, wasn’t really cost effective.”

Credit unions can choose to generate different messages for different regions and audiences whenever they advertise directly through a website — but that capability usually requires a higher premium, on top of the already high cost of doing business with such companies.

As an alternative, Mid-Atlantic partnered with Long Island-based advertising agency Austin & Williams in 2011 to create its own Google retargeting campaign.  The credit union secured ad space using network buys, which are affordable blocks of leftover ad space on different local websites. The credit union then set up its website to distribute a tracking cookie on visiting PCs, which served up targeted ads from Mid-Atlantic whenever those individuals visited other affiliate sites.

“Ironically, we were actually getting ads on washingtonpost.com through our Google network, and it was a costing us a quarter of what it would cost to advertise directly with them,” Wilensky says.

As part of this strategy, Wilensky tracks the credit union’s website analytics every day to see where people are coming from, how long they’re staying, and what they’re doing when they are there, which helps him understand if the credit union is reaching its desired audience.

To establish ROI, the credit union tracks the number of applications people complete on the website for things such as membership, auto loans, credit cards, and mortgages and checks to see how many of these visitors came from the Google campaign versus other measures like organic search.

This targeted, data-driven marketing strategy helped the credit union achieve 6.96% annual loan growth in fourth quarter 2012, 3% growth in membership, and 4.9% growth in average member relationship.

MEMBERSHIP GROWTH
DATA AS OF DECEMBER 31, 2012
© Callahan & Associates | www.creditunions.com

membership-growth-mid-atlantic

Generated by Callahan & Associates' Peer-to-Peer Software.

Typically, Mid-Atlantic tailors its ads to promote seasonal promotions or specific products, but as financial institutions’ ability to gather and effectively use consumer data continues to evolve, so will the marketing strategies at Mid-Atlantic.

“New membership is one area we’re pushing and obviously loans,” Wilensky says. “But I’d love to get down to the point where we know who those online visitors are and what they do, so we can tag them if we know they have a specific loan or have been shopping for one. That’s where thing are ultimately headed.”

By combining the wealth of online data already available with advanced capabilities in mobile phones, such as GPS, credit unions may soon be able to tailor and improve all types of financial experiences, not just electronic ones.

“The day is going to come when someone walks into the branch with their phone, and every teller screen will say who just walked in, that they’re three days behind on their credit card payment, or that they came in last week about an auto loan,” Wilensky says. “If it’s done properly, it can eliminate the hassle of having your time wasted.”

 

 

 

March 26, 2013


Comments

 
 
 
  • I appreciate this series of articles into the management and use of "Big Data". Thanks
    Russ Dalke