November 14, 2013 1 Comment
Bankers seen sipping away the hours over client martini lunches at upscale restaurants and posh clubs are rare these days. The slump in credit demand from the global economic crisis is part to blame but so to is the absence of ‘live’ clients. Branch offices that were once community hangouts on payday look more like empty office spaces for lease. Today bank clients ‘hangout’ virtually, while doing most of their banking online. They lurk in and out web-based services unwittingly leaving behind hundreds of data points (like footprints) that when reconstructed using data analytics algorithms can accurately reveal the client’s real identity.
At a first-of-its-kind event in Atlanta,Georgia titled, Customer Insights & Analytics in Banking Summit 2013, representatives from various forward-thinking banks and data-analytics service companies presented their combined views to a packed room of financial professionals. Organized by Data-Driven Business (datadrivenbiz.com), a US arm of FC Business Intelligence (a London-based events company), the Summit personified the past, present, and future of banking. First, it exposed the ugly truths characteristic of a complacent banking culture mindset. Then it highlighted the extraordinary accomplishments from early-adaptor banks, and, finally, it unveiled a fantastic prediction on how banking could potentially hold the keys to unlocking the value of social media feeds from Twitter, Facebook, and other similar web-based services.
With off-the-shelf, data analytics, software tools, bankers can gain an accurate 360 degree view of their customers on an individual basis just by matching a customer’s banking data (i.e. loans, credit card purchases, investments) with their behavioral patterns online. The technology used to integrate data sets to match behaviors with individual names has advanced remarkably, so much so, that bankers can calculate with reasonable accuracy the ‘lifetime value’ of each customer. This magical step has been demystified by over 150 vendors who specialize in the science of Digital Data Integration or DDI. DDI connects numerous disparate data sets both structured and unstructured using assigned ID numbers. Expert companies in this area include Aster (asterdata.com, a TeraData Company), Actian (actian.com), PrecisionDemand (precisiondemand.com), Convergence Consulting Group (convergenceconsultinggroup.com) and Actuate (actuate.com, a BIRT company). The principle reason bankers want to segment their customers by their future income potential is to allocate their limited resources more efficiently.
Banks that fully integrate their operational data with unstructured social media streams will become the game-changers to watch. Already the Old Florida National Bank boasts of their younger and more agile management team (under 43 years of age) who credit their surging asset growth in the past four years to their data analytics initiatives – (from USD$100m to $1.4b). Their team has the proper bank culture, mindset, and know-how to implement data analytics tools that fully capture a digitally-holistic view of their customers. By mapping where their customers spend most of their time and money, management can target more relevant and timely offerings. Targeted customers unwittingly respond with not only a buying interest but also a willingness to refer the bank to a friend or colleague. …truly a win-win for all.
SunTrust Bank, also based in Florida, uses data analytics to determine not only the location of their next branch office, but also the optimal management qualifications required to operate one of their branches. Another interesting case study came from Wells Fargo. Their data analytics team integrates thirty-two data sets (from both internal and external sources) and presents the results in a customized dashboard format to their managers company-wide. Managers use the service to make better decisions, present data on an ad-hoc basis at meetings, and self-serve their specific research interests using a number of additional data visualization tools for non-techies. The tools they use are off-the-shelf Business Intelligence or BI software packages provided by companies such as Oracle (oracle.com/BI), MicroStrategy (microstrategy.com) and Tableau (tableausoftware.com).
Servicing a more digital client-base has come with its many challenges as well as with its unexpected opportunities. For example, credit bureaus that traditionally deny 96% of consumer credit requests often reject qualified candidates. Using data analytics tools, however, banks can integrate comparative behavioral data with a candidate’s payment history and reassess their risk profile accordingly. The results would qualify more loans that would otherwise have been turned down. Other exciting ways for banks to grow revenues include working with real estate brokers. Banks can determine which of their clients is most prone to purchase a new home and pass the list on to an agent. Agents seeking better leads will more than likely recommend mortgage business back to the bank that shared their intel.
One can just imagine how many more ways bank data can play an integral part in helping companies find their most likely customers and future business. Banks already manage the transactional data in-house and are rapidly gaining the business intelligence experience needed to integrate their customer’s behavioral data and compare their profile with their peers. Under this scenario, one might wonder why any business would not want to work with a bank that not only understands their business but also delivers buying customers.
With this much real-time intel available on customers in one central location, could banks one day become the primary lead source for their business clients? Could this new normal become a significant game-changer in the banking industry?
Despite a rosy future, the business world is not waiting for banks to embrace data analytics any time soon. Competitive trends point to a number of threats including retailers such as WalMart who will be offering banking services directly to their customers at their retail outlets.
There is also the emergence of the ‘digital wallet’, which for the time being focuses on reducing the clutter of credit cards using available smartphone technology. Eventually one company will umbrella all credit card transactions and offer global behavioral tracking intel. Pioneers on the forefront include Protean Payments (getprotean.com), a recent startup that plans to use bluetooth technology to replace card swiping at terminals and Wallaby (https://walla.by), a company that helps cardholders maximize points earned prior to making a purchase. There’s also Ebay’s PayPal (paypal.com), which has released a debit card concept, which it hopes will entice developers worldwide to promote their data analytics services to SMEs.
In online banking, Simple.com does not have a physical presence nor charges the customary fees that traditional banks do. In fact, they offer plenty of financial management reports and suggestions at no charge. …all online, of course. How they make money is best understood when opening an account. Simple.com new accounts cannot be opened unless one is willing to accept ‘cookies’ on their computer, a permission which releases away a user’s complete web history to a third party. Their insistence suggests that they place a greater value in a customer’s behavioral online data than they do in their banking business.
If Simple.com succeeds, could their new business model significantly change the way consumers perceive a bank’s value proposition? Will consumers demand additional compensation for allowing access to their behavioral online data, since the data is worth more than the interest paid on deposits?
For now, banks who are looking at data analytics for the first time and wondering how and when to take the plunge should heed practical advice from experts who spoke at the event. One individual concluded that for now, those new to data analytics should start with the data they already have and use predictive findings from data analytic tools to start a conversation rather than formulate targeted recommendations. This advice and the rapidly evolving changes in both consumer and commercial banking remind me of the famous Aesop’s Fable about the race between the tortoise and the hare. This time, however, the winner may be a third and invisible participant called ‘Big Foot’ representing Big Data and Data Analytics.
© 2013 Tom Kadala