Monday, January 14, 2013

Finding Serendipity in Big Data

Unexpected pleasantries always have a deeper impact. Like moments that surprise you as if a higher power had designed them just for you. The luck of making exciting discoveries by accident, love at first sight, coming across a childhood treasure at a yard sale, unintentionally coming across a precious memory or connecting with an insight that answers your dreams. These are moments that create internal warmth that can only come from unexpected joy.

Take, for example, a concert by your favorite childhood band, The Rolling Stones. Such an event has expectation, build up, and the experience of the moment. The joy is foreseeable. Now, imagine that you head to a local bar for a drink, and on that night a special guest is making an appearance. Without any prior notice, The Rolling Stones come on stage. Previously, such magical moments were only possible by two means. Organized by someone that knows you, or by fate. 

Guest Post By Scott Bales, Chief Mobile Officer, Movenbank

In today's digital world, it is possible for someone to know you well enough to create such experiences. This is because there has been an accelerated growth of data over the past five years, where every minute massive amounts of insight are being generated from every phone, website and application across the Internet.

In his post, ‘How Much Data Is Created Every MinuteJosh James of Domo, dissects the world’s data creation in a unique infographic. Many innovative organizations have recognized the potential of this data, such as ESPN, which drives through Facebook open graph data to optimize the content a user experiences. Some financial organizations have also begun to tap into the potential of ‘big data’. In a world full of data to drive insight, however, there are still very few organizations that use all of the data at their disposal to enhance their offerings. 

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Going forward, big data will be an essential tool in the modern marketer’s toolkit. As Brad Peters explains in Forbes, “The extraordinary richness of modern life—especially as it has reached out to include 3 billion of the world’s people—can be largely credited to the mass customization revolution. But now, big data … promises to take this relationship to the next level: mass personalization.”

Simply collecting huge amounts of data doesn’t have value in isolation, however. If big data (or any data for that matter) can’t be used to improve brand interaction and directly impact revenue, it’s nothing more than a buzzword. Modern consumers are demanding an optimized experience, and that demand can’t be overlooked. Marketers that thrive in “The Age of Big Data” will be those that can find insights and adapt quickly to large amounts of information—not simply collecting it—to deliver the interaction customers want.

In the past, companies relied too much on data at the expense of experience; trusting aged statistical patterns, Excel spreadsheets and batch-based information warehouses, where human insight or intuition was required to create actionable information. Today, the technologists and algorithms in industry have created a new breed of analyst called the data scientist. Their role is less about replacing human intuition than it is about augmenting the human experience by making it easier, faster and more efficient to analyze data.

As data-driven insights become an increasingly vital competitive differentiator, companies will use them to drive and optimize business decisions across industries, products and businesses. In the past, this power was reserved for those with abundant resources, but today, almost any company or individual with access to a significant customer database can potentially become an influential player in the new information-driven economy.

Data Use in Traditional Banking

Personalization and analytics are not new to financial services. Ever since the mainframe computer took over the banks core, banks have continually tried to extract insights from one of the richest sources of data on the planet . . . how people use their money. Historically, however, the only outputs from these initiatives have been internally focused. Transactional analysis for fraud detection, behavior analysis for cross-selling, position analysis for credit risk management and Monte Carlo simulation for exposure forecasting were all internal metrics.

When was the last time you heard about a bank analyzing your financial behavior to provide insight on spending habits, or to encourage sustainable use of your cash flow? Most likely, never.

Simple’s ‘Safe to Spend’, is one of the first data analysis initiatives by a financial organization that delivers valuable insights for the customer based on behavior. The bank provides the customer a simple indication of sustainable spending. Such an insight would be contrary to the economics of most transactional products in banks, where fees are generated through the nativity of the consumer such as with overdrafts, late fees and impulse spending. The message from banks tends to lean the way of enabling unsustainable cash flows so you have to get into more debt.

Within the traditional structure and operation of the financial services industry, consumers have little choice in terms of selecting financial instruments and delivery channels. The rigid structure of the industry, combined with the operation of monolithic powerhouses, meant that consumers had to accept the form and price of both financial instruments and delivery channels. Switching between banking providers generated little benefit, forcing the consumer to experience disruption and financial cost. Consumers were essentially locked into buying patterns and had little incentive to change.

Big Data in Banking Today

Recently, however, deregulation and the emergence of new forms of technology have created significantly more competitive market conditions which have had a large scale impact on consumer behavior, consumer empowerment, and informative comparison. Consumers now have access to greater tools and are more informed to change their behavior or even choice of products or banks. As a consequence, bank providers are less certain that their customers will continue to bank with them, or that they will be able to rely upon the traditional banker/customer relationship to cross-sell high value, so-called ancillary products.

Could a bank change its ways? Potentially, but the odds are stacked against them. Current internal metrics and KPIs would show massive shifts against P & L owners. But there have been glimmers of hope. Capital One came to the market with the very intent to be data driven and have made this a differentiator for customer service and product development. Plus ventures under Citi Group have also seen some insight driven customer value propositions.

Large industry influencers like MasterCard have been analyzing transaction data to help marketers direct targeted efforts at consumers. Although creating large amounts of controversy, the initiative was to leverage one of the richest data stores on the planet. Processing some 34 billion transactions each year, the analysis aimed to help marketers in issuance and acquiring partners target customers who are more likely to buy their products and services. MasterCard first explored the possibility of using customer data for targeted advertising in 2011, but delayed those plans because of legal and regulatory concerns over how financial services companies use the customer data they have collected.

According to an online sales pitch titled “Leveraging MasterCard Data Insights to Reach Holiday Shoppers”, MasterCard analyses billions of transactions in search of insights such as consumers that are more likely to purchase consumer electronics or luxury goods. “The foundation of all of our solutions is transaction data,” Susan Grossman, MasterCard’s senior vice-president of media solutions, said during the programs launch.

As people spend more time in front of computers and mobile phones, both financial and non-financial companies are amassing vast profiles about people’s activities both online and away from a screen. Facebook, for example, is working with Datalogix, a data company, to track whether people buy products after viewing an ad on the social networking site. Many banks are also beginning to use retargeting strategies to position online and offline sales communications after shopping on financial or bank sites.

Other credit card companies have explored using data for marketing. Visa sells retailers the ability to send text messages to consumers based on their previous credit card transactions – as long as those targeted agree to receive the ads in return for discounts and other incentives. American Express also conducts custom research for marketers based on aggregated, anonymous credit-card transaction data.

Banks have for some time been deriving value through analysis from diverse sociocultural factors influence beliefs, behavior and decision-making in both commercial institutions in the formal sector, and offline insights in the informal sector. What banks hope to gain are insights that some transactions add value to lives of people by providing them with financial security, wealth, convenience, and the means to satisfy immediate needs. Negative behavioral indicators can also be used. Insights that suggest, for instance, a lack of ‘financial smarts’, problems with credit and loan repayments, escalating debt can also provide potential for outreach and marketing by innovative financial organizations.

So, with such deep historical industry precedence, why can't personalization algorithms be used to help achieve serendipity in banking? Such models could do a strong of a job automating the discovery of stuff we’re interested in, opening the door for services that deliver personalization in part by identifying broad patterns in user behavior. Unfortunately, with traditional banks, it’s just not what they’re designed to do.

Big Data Challenges

Infamously John Rockefeller, chairman of the Senate Commerce Committee into data brokers, was concerned that an “unprecedented amount” of personal, medical and financial information about people could be collected, mined and sold, to the potential detriment of consumers. “An ever-increasing percentage of their lives will be available for download, and the digital footprint they will inevitably leave behind will become more specific and potentially damaging, if used improperly.”

But is this a generational thing? Statistics suggest that Gen Y are increasingly open with their data if their data being used for their own gain in what is known as a ‘value exchange’. It’s this comfort that powers the buzz around platforms like Facebook, Twitter, LinkedIn, and the large majority of the viral networks that have become a part of daily life.

This ‘unprecedented amount’ of personal, medical and financial data does create a digital footprint, and there are some risks that need to be managed. But, we also have to realize that this ‘footprint’ opens the door for the ability to do something never before possible.

There is the possibility of a 'data-driven serendipity'; where an organization knows you well enough to design experiences that delight, using highly intelligent algorithms leveraging the insight of your digital footprint. 

Data opens the door to this entirely new standard in experience design, even in the banking industry. Perhaps this could be the eventual expression of Steve Jobs’ vision, “that technology alone is not enough—it’s technology married with liberal arts, married with the humanities, that yields us the results that make our heart sing.”

Scott Bales is the Chief Mobile Officer of Movenbank, the world's first ever card-less bank. Scott is a self-proclaimed extrovert, who has meshed his fascination with people and what motivates them, with his enthusiasm for technology. An Australian, who currently runs the Asia Pacific sector of User Strategy in Singapore, Bales is 'the most influential in financial services and mobility', with over a decade of international experience in innovation, thought leadership, implementation planning and strategy. He is an avid blogger and can be found often on Twitter.


  1. Thanks to Scott for a great post about the potential within a bank's database to make a difference. Some organizations are delving into this opportunity while others are waiting on the sidelines.

    Scott, do you think traditional banks are structured in a way to take advantage of big data, or will they leave it to the new start-ups and online banks?

    1. It's an interesting question Jim, as all the enablers have been in place for some time. Open data sources, social media, Big Data and Data Sciences. But banks tend to lag. I'm seeing trends that suggest major banks are looking to partnership or acquisition of leading edge technologies, but haven't seen to many that go as far as creating 'serendipity.' But one thing is for certain, traditional banks really struggle with understanding the value of leverage other party and open data.

      There is however a strong trend amongst startups that embraces open data. Services like Movenbank, MINT, Klout, PeerIndex and Banjo, take third party and open data to able enrichment, context and insight to their services. So while serendipity is technically and commercially possible, banks still through up naive concerns about compliance, security, privacy and ROI.

      Contextualised and insightful experiences are no longer fads, or simply liked by consumers. They are now demanded as a means to managing the highly noisy information age.

  2. Data management impacts the customer experience. That is understood. And in the financial services industry, you have the added challenge of being in compliance with critical regulatory mandates. Now, the quality of your data is even more important. Poor data quality will negatively impact your business, your business processes, and whether or not your bank can survive...or your top executives stay out of prison. The fines and consequences of data quality are too pressing to ignore. And, now that FATCA regulation guidelines are out and CCAR mandates are in, banks must assess their data now in order to be in compliance in the near future. These deadlines will come quickly. Jim, there is so much "Big Data" to assess, institutions must act quickly....