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Dave Brailsford basked in the glow of the Olympics. The Great Britain cycling team just completed their participation at the 2012 Olympics in London, England, winning 70 percent of the medals in men’s cycling. Reporters probed with aggressive questioning, wanting to understand the silver bullet that led to this success. The irony: There was no silver bullet. In fact, it was the opposite of a silver bullet.
“The big data revolution is about accomplishing feats with data that no one believes is possible”
When the Great Britain cycling team, Team Sky, hired coach Dave Brailsford in 2010, the country had never won a Tour De France. In fact, the history of the sport in the country was filled with errors, mishaps, and minimal success. Historically, the team chased fads of success: new equipment, new uniforms, and new techniques. But nothing changed the trajectory. Then, Dave Brailsford arrived.
Dave Brailsford fanatically talks about the aggregation of marginal gains. This concept means that by marginally improving each and every aspect of a process, the aggregation of those small gains will lead to large improvements. Brailsford’s goal was simple: one percent. He sought a one-percent improvement in every aspect of the cycling team.
Setting out to improve all aspects of a cycling team, the obvious places to start are in areas like nutrition, bike performance, and physical conditioning. However, for Brailsford, those enhancements merely scratched the surface. He set out to improve every aspect by one percent. Not only sports massage, but the gels used for sports massage. Not only the bikes, but the grips on the bikes and more specifically, the tackiness of the grips. He focused on every aspect: one-percent improvement. It’s that simple.
In 2012, a short two years after Brailsford joined the team, Great Britain won its first Tour De France. Shortly thereafter, the triumph at the Olympics in London occurred. The aggregation of the one-percent gains created superior outcomes.
The big data revolution is about accomplishing feats with data that no one believes is possible. The leaders of the big data revolution will embody three characteristics:
1) The ability to suspend disbelief of what is possible, and to create their own definition of possible
2) An inherent knowledge of pattern recognition and the insight to apply patterns from one industry or dimension to another that may be seemingly unrelated
3) Commitment to one-percent improvement in every aspect related to data
This revolution is about finding your possible
In banking, the big data revolution will have its greatest impact on customer service. Customer service is increasingly moving to online and remote support, which puts an emphasis on call centers (not the traditional definition perhaps, but certainly a focus on serving clients over web and mobile). There was an era when customer support and service was dictated by what you told the person in front of you, whether that person was a storeowner, lender, or even an automotive dealer. That simpler time created a higher level of personal touch in the process, but then the telephone came along. The phone led to the emergence of call centers, which led to phone tree technology, which resulted in the decline in customer service.
While technology has advanced exponentially since the 1800’s, customer experience has not advanced as dramatically. While customer interaction has been streamlined and automated in many cases, it is debatable whether or not those cost-focused activities have engendered customer loyalty, which should be the ultimate goal.
There are a multitude of reasons why a financial services firm would want to invest in improving customer service through remote support: lower costs and consolidation; improved customer service, cross-selling, and extended geographical reach.
Banking institutions have a unique need for call centers and expertise in customer service, given that customer relationships are ultimately what they sell (the money is just a vehicle towards achieving the customer relationship). Five of the most prominent areas of financial services for call centers are:
• Retail banking: Supporting savings and checking accounts, along with multiple channels (online, branch, and ATM)
• Retail brokerage: Advising and supporting clients on securities purchases, funds transfer, asset allocation, etc.
• Credit cards: Managing credit card balances, including disputes, limits, and payments
• Insurance: Claims underwriting and processing, and related status inquiries
• Lending: Advising and supporting clients on security purchases, funds transfer, asset allocation, etc.
• Consumer lending: A secured or unsecured loan with fixed terms issued by a bank or financing company. This includes mortgages, automobile loans, etc.
Consumer lending is perhaps the most interesting financialservices area to explore from the perspective of big data, as it involves more than just responding to customer inquiries. It involves the decision to lend in the first place, which sets off all future interactions with the consumer.
There are many types of lending that fall into the domain of consumer lending, including credit cards, home equity loans, mortgages, and financing for cars, appliances, and boats, among many other possible items, many of which are deemed to have a finite life.
Ultimately, from the lender’s perspective, the decision to lend or not to lend will be based on the lender’s belief that he/ she will get paid back, with the appropriate amount of interest.
A consumer-lending operation, and the customer service required to manage the relationships, is extensive. Setting it up requires the consideration of many factors ranging from call volumes, to staffing, to performance management, and even location. Data will make this better.
Data will transform customer service, as data can be the key ingredient in each of the aspects of successful customer service. The lack of data or lack of use of data is preventing the personalization of customer service, which is the reason that it is not meeting expectations.
In the report, titled “Navigate The Future Of Customer Service” (Forrester, 2012), Kate Leggett highlights key areas that depend on the successful utilization of big data. These include: auditing the customer service ecosystem (technologies and processes supported across different communication channels); using surveys to better understand the needs of customers; and incorporating feedback loops by measuring the success of customer service interactions against cost and satisfaction goals. Each of these areas is fertile hunting ground to find your 1% improvements.