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Turning “Big Data” into aCompetitive Differentiator     Dr Aaron Sum     Senior Vice President, Head of Strategy & Analyti...
Agenda   “Big Data” in Banking: Opportunities and Challenges   Recent Trends in “Big Data” Analytics   Turning Insights...
The amount of global data is projected tomore than double every 2 years                 “Data, data, everywhere ...”      ...
In financial services, we are now seeingnew waves of data growth                                               Recent Head...
The Banking sector is poised forsubstantial gains from the use of big data                            Data intensity by se...
However, banks must navigate thecomplexity, variety and velocity of “big data”                                         Big...
“Big Data” Analytics, when harnessedcorrectly, can be a substantial competitive edge   From Traditional Sources of        ...
Given the current climate of protracted “slowgrowth” and “hyper competition”, analytics is keyto uncover growth / optimiza...
Agenda   “Big Data” in Banking: Opportunities and Challenges   Recent Trends in “Big Data” Analytics   Turning Insights...
Recent Trends:“Big Data” Analytics                                         •   Sales effectiveness                        ...
Case example in “Big data” analytics                                                 Example: Large Spanish Bank          ...
Hundreds of automated algorithms (continually tested &refined), generate thousands of personalized campaignsevery month, p...
Case example in “Big data” analytics                                                        Example: Progressive Insurance...
Case example in “Big data” analytics                                                            Example: Large Australian ...
Leading banks are embracing online & social mediaanalytics of customer sentiment and opinions to gaugeresponse to new prod...
Case example in “Big data” analytics                                                          Example: UBS Investment Bank...
UBS found greater correlation from its satellitedata projections than its traditional statisticalmethods                  ...
Case example in “Big data” analytics                                                               Example: Bank of Americ...
Case example in “Big data” analytics                                                           Example: Bangor Savings Ban...
Case example in “Big data” analytics                                                  Example: Cardlytics            Partn...
The rise of niche analytics firms such as Cardlytics thatcan be valuable partners to banks seeking to enhancetheir custome...
Agenda   “Big Data” in Banking: Opportunities and Challenges   Recent Trends in “Big Data” Analytics   Turning Insights...
Developing the “Moneyball” Advantage:Analytics-driven strategies vs. Conventional wisdom                                  ...
Building Competitive Advantage:Moving up the Analytical Capability Curve                                                  ...
The Path from Insights to Business Value    6 Guiding Principles 1. Focus on the highest value opportunities 2. Start with...
Contact InformationDr Aaron SumSenior Vice President, Head of Strategy & Analytics(SME Banking)aaronsww@alliancefg.com    ...
Afsc2012 Turning Big Data Into A Competitive Differentiator V Final
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Afsc2012 Turning Big Data Into A Competitive Differentiator V Final

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Developing the “Moneyball Advantage” in Banking
Turning Big Data into a Competitive Differentiator
By Dr Aaron Sum, Alliance Bank Malaysia Berhad
(Presented at the Asian Financial Services Congress, Feb 23 - 24 2012, Marina Bay Sands, Singapore, Organized by IDC Financial Insights)
Introduction and Synopsis
The rapid growth of data in recent years, commonly referred to by industry observers as the “Big Data” phenomenon, presents both opportunities and challenges. As companies seek to manage, analyze and harness the insights from these substantially larger sets of data, they are not only confronted with the growing volume of data from their existing applications, but are also faced with new varieties of unstructured data such as those from social networking tools and mobile technologies. Furthermore, the rate of change of new data formats over the past 5 years has been unprecedented. The amount of global data is now projected to more than double every two years, with new sources such as Facebook generating 30 billion pieces of content every month (1). In financial services, a similar trend prevails; there are now 10,000 payment card transactions per second globally. In 2010 alone, 210 billion electronic payments were generated worldwide, and this is projected to double by the end of the decade (2).
In the book “Moneyball” (now popularized via the movie of the same name), the author Michael Lewis recounts how the general manager of Oakland Athletics (a baseball team in California) used statistical analytics to find undervalued talent to take on teams like the New York Yankees. Lewis details how statistician Bill James showed that people overlooked the information that would reveal which strategies would be most effective to compete and win in baseball. The central premise of Moneyball is that the collected wisdom of baseball insiders (including players, managers, coaches, scouts, and the front office) over the past century is subjective and often flawed.
As seen from “Moneyball”, analytics, when harnessed to its full potential, can serve to ‘level the playing field’ and enable even the smallest industry players to take new ground and win market share.
Given the current climate of protracted slow growth and “hyper competition”, analytics is a key to uncover growth and optimization opportunities. In this context:
• How can financial institutions effectively leverage big data analytics to develop the “Moneyball” advantage?
• How would banks use big data to find new revenue streams, maximize sales effectiveness, optimize costs and even forecast market trends?
• What key steps should banks take to build their analytical capabilities?

The conference paper addresses the key issues above and shows the path that banks can take to generate competitive advantage and tangible business value via the application of hypotheses-driven analytics.

Afsc2012 Turning Big Data Into A Competitive Differentiator V Final

  1. 1. Turning “Big Data” into aCompetitive Differentiator Dr Aaron Sum Senior Vice President, Head of Strategy & Analytics (SME Banking)
  2. 2. Agenda “Big Data” in Banking: Opportunities and Challenges Recent Trends in “Big Data” Analytics Turning Insights into Business Value: The “Moneyball” Advantage 3
  3. 3. The amount of global data is projected tomore than double every 2 years “Data, data, everywhere ...” (1,2) Acceleration of Data Growth • Companies capture trillions of bytes of information about their customers, suppliers, and operations • Millions of networked sensors are being embedded in the physical world in devices such as mobile phones and automobiles, sensing, creating, and communicating data • Multimedia and individuals with smart phones and on social network sites will continue to fuel exponential growth(1) “Big Data: The next frontier for innovation, competition & productivity”, McKinsey Global Institute, June 2011 4(2) “Data, data, everywhere”, The Economist, Feb 2010
  4. 4. In financial services, we are now seeingnew waves of data growth Recent Headlines: Data Growth in Financial Services  The New York Stock Exchange creates 1 terabyte of data Big Data Defined per day vs. Twitter feeds that generates 8 terabytes of data per day (or 80 MB per second) . “Big data" refers to  10,000 payment card transactions per second around the the management, world. access, and analysis  210 billion electronic payments generated worldwide in of substantially larger 2010. This is expected to double by the end of the sets of (typically decade. unstructured) data than had been  Between 2009 and 2014, the total number of US online conventionally banking households will increase from 54 million to 66 million. possible until recently.  46% of financial services CIO‟s are exploring the potential of could computing, up 33% from 2010.  10x growth in Market Data volumes between 2007-2011 and growing. 5Source: Information Week, American Banker
  5. 5. The Banking sector is poised forsubstantial gains from the use of big data Data intensity by sector Spectrum of „big data‟ Examples: • Transactional data • Lifestyle-related information • Behavioral data • Demographics • Geospatial information and location intelligence on customers • Online and social media interactions • Mobile (smart-phone) usage trends The quest for a true “360 Degree Customer View” 6Source: (1) “Big Data: The next frontier for innovation, competition & productivity”, McKinsey Global Institute, June 2011
  6. 6. However, banks must navigate thecomplexity, variety and velocity of “big data” Big Data Challenges • Growing volume of unstructured data from banks‟ current applications as well as the newer technologies being adopted Complexity • Adds another layer of complexity to the elusive “360 Degree Customer View” which banks have been pursuing • Mobile technologies, social networking tools, etc are significantly increasing the stock of unstructured data within the banks Variety • The rate of change of new data formats over the past 5 years have been unprecedented; the trend is expected to continue Velocity 7
  7. 7. “Big Data” Analytics, when harnessedcorrectly, can be a substantial competitive edge From Traditional Sources of … to Analytics-Driven Competitive Advantage … Competitive Advantage Differentiation • Product (e.g. product, price, • Price service) • Cost • Service • Customers OR Multiple Business strategy sources of Cost Leadership competitive Analytics advantage “Big data” “Trade off between low cost or “Right product, price, service focused differentiation, or hybrid levels (at the right cost), for approach” the right customer” Analytics must move from the „fringe‟ to the „core‟ of all strategic and tactical business decisions, to develop this competitive advantage 8
  8. 8. Given the current climate of protracted “slowgrowth” and “hyper competition”, analytics is keyto uncover growth / optimization opportunities Current Challenges Key Imperatives • Protracted “Slow Growth”: • Finding new market segments and Increasingly challenging for banks to revenue streams sustain revenue momentum; cost optimization begins to take centre-stage • Maximizing sales and marketing effectiveness • “Hyper-competition” and continued margin compression: Competition continues to intensify as margins are • Optimizing costs and existing resources eroded • Risk-based pricing • Ability to forecast market trends, gauge customer sentiment and adapt business strategies quickly 9
  9. 9. Agenda “Big Data” in Banking: Opportunities and Challenges Recent Trends in “Big Data” Analytics Turning Insights into Business Value: The “Moneyball” Advantage 10
  10. 10. Recent Trends:“Big Data” Analytics • Sales effectiveness • Opening up new target segments Granular Micro- 1 Targeting of • New product response • Campaign effectiveness • New customer value proposition customers / segments • Brand health Partnerships with Sentiment Analysis to 6 Analytics Specialists / 2 gauge „real time‟ Providers response to campaigns Recent Trends “Big Data Analytics” Predictive staff Trend Forecasting & 5 scheduling to optimize 3 Market Research via costs novel data sources • Cost optimization Proactive Monitoring to • Forecasting trends 4 detect early „fault triggers‟ • Service quality 11 • Proactive customer management
  11. 11. Case example in “Big data” analytics Example: Large Spanish Bank Granular Micro- 1 Targeting of customers Micro-targeting via personalized campaigns / segments • The bank adopts an event-driven marketing approach on retail and SME customers • Hundreds of automated algorithms tested during the last 10 years to produce 500 personalized campaigns every week  Multi-year effort of customer data collection to refine customer potential value  Strong use of customer potential value to prioritize commercial effort. Business Value Generated • Cross-selling index of 6.45 vs. Spanish average of 3 • Churn rate of 6.90% vs. Spanish average of 14% • Service level index of 76,8 vs. Spanish market index of 70.5 12Source: Bank Annual Reports, Analyst Reports
  12. 12. Hundreds of automated algorithms (continually tested &refined), generate thousands of personalized campaignsevery month, pushed to customers’ mobile phonesExample of “real-time” offer: Proposal for a personal loan just after the purchase eventThe customer buys an LCD TV with If the customer is interested, The operation ends when the customerhis credit card and the Bank sends he replies with a code receives the confirmation message showinghim the following message that his purchase has been financed 1 2 3 “The Bank” TAJ 1 50 Operation finances until successfully done. xx/xx/xx your VISA We have financed purchases of 1.200 your VISA purchase EUROS in 12 quotas of 1.200 EUR in 12 of 107,18 EUROS payments of 107,18 per month. EUR per month. To finance it, Check this please answer TAJ transaction in 1 and the sum of xxbank.com coordinates B1 + E2 Matrix Card (Tarjeta de claves): It is a card containing letters and numbers from which, each time a customer needs to perform an operation (e.g. a money transfer), the Bank asks for a code 13
  13. 13. Case example in “Big data” analytics Example: Progressive Insurance Granular Micro- 1 Targeting of customers Opening up new target customer segments / segments • Progressive defines narrow groups of customers (or “cells”)—for example, motorcycle riders older than 30 with no previous accidents, a college education, and a credit score higher than a certain level. • For each cell, the company performs regression analysis to identify the factors that most closely correlate with its loss experience. • They set prices for each cell they believe will enable them to earn a profit across a portfolio of customer groups. • A simulation model is used to test the financial implications of these hypotheses. Business Value Generated • Enable targeting of new segments based on deeper understanding of risk-returns (e.g. higher risk segments that were previously „blacklisted‟) 14Source: “Competing on Analytics”, Thomas H Davenport, Don Cohen and Al Jacobson
  14. 14. Case example in “Big data” analytics Example: Large Australian Bank Sentiment Analysis to 2 gauge „real time‟ Tracking social media sentiment towards campaigns response to campaigns • The bank had started its social media activities like most banks in the region: it launched a Facebook page, created a twitter account, as well as its LinkedIn profile. • However, it soon realized that social media was not only about presence but also about engaging with customers. At this stage, the bank was only using social media as a unidirectional marketing channel — in a similar way to how traditional marketing channels were normally used. • However, the bank recognized that social media presented great opportunities for the organization, since millions of conversations are constantly taking place, and some of those were about their bank. Application of Analytics • Put in place social media analytics tool to gauge sentiment on bank‟s overall brand perception, as well as to specific marketing campaigns Examples: ING, Citi, SunTrust 15Source: IDC “Journey into Big Data: From Transactional Data to Big Data Analytics”, 2011
  15. 15. Leading banks are embracing online & social mediaanalytics of customer sentiment and opinions to gaugeresponse to new products and campaignsExample: Social analytics dashboard Observations Leading banks are already turning to social analytics to gauge sentiment towards key initiatives such as: • New Product launches (e.g. ING) • Marketing campaigns 16Source: IDC “Journey into Big Data: From Transactional Data to Big Data Analytics”, 2011
  16. 16. Case example in “Big data” analytics Example: UBS Investment Bank Trend Forecasting & 3 Market Research via Forecasting sales trends using satellite data novel data sources • UBS Investment Research issued its earnings preview for Wal-Marts second quarter, which publicly revealed that UBS had been using used satellite services of private-sector satellite companies to gather the comings and goings of the parking lots at Wal-Mart stores. “UBS proprietary satellite parking lot fill rate analysis points to an interesting cadence intra-quarter and potential upside to our view,” the report read • UBS analyst Neil Currie had been looking at satellite data on Wal-Mart during each month of 2010, and he‟d concluded that there was enough correlation between what he was seeing in the satellite pictures of Wal-Mart‟s parking lots to the big-box chain‟s quarterly earnings, that he was ready to incorporate that data into UBS‟ report on Wal-Mart • By counting the cars in Wal-Mart‟s parking lots month in and month out, Remote Sensing Metrics analysts were able to get a fix on the company‟s customer flow. From there, they worked up a mathematical regression to come up with a prediction of the company‟s quarterly revenue each month. 17Source: CNBC “New Big Brother: Market-Moving Satellite Images “, Aug 2010
  17. 17. UBS found greater correlation from its satellitedata projections than its traditional statisticalmethods More Accurate Forecasting Novel application of “Big Data” • In the second quarter, the satellite analysts had spotted a surge in traffic to Wal-Mart stores during the month of June, which was 4 percent ahead of the same month a year ago. • That, they speculated, was driven by an aggressive Wal- Mart price rollback marketing campaign that brought a lot more customers into the stores • Because they could see that traffic showing up in the parking lots, the satellite analysts came up with a much different projection for the company‟s quarterly earnings in the second quarter than the UBS team did using traditional methods. 18Source: CNBC “New Big Brother: Market-Moving Satellite Images “, Aug 2010
  18. 18. Case example in “Big data” analytics Example: Bank of America Proactive Monitoring to 4 detect early “customer Contact centre sentiment analytics service issues” • Sentiment analytics to provide insight into a customer‟s feelings about the organization, its products, services, customer service processes, as well as its individual agent behaviors. • Sentiment analysis data is then used across an organization to aid in customer relationship management, agent training, and to help identify and resolve troubling issues as they emerge 19Source: “State of the Art: Sentiment Analysis”, Nexidia 2009
  19. 19. Case example in “Big data” analytics Example: Bangor Savings Bank (USA) Predictive staff 5 scheduling to optimize Predictive model to optimize branch staffing costs • Predictive model that forecasts teller staffing based on forecasted transaction volumes. • The tool uses business intelligence to analyze transaction data thats collected. Reports are produced each month on transaction workloads, labor cost per transaction, and salary and benefit expenses matched against transactions; these reports are updated hourly and coupled with projections. • The result is a benchmark that a bank can use to match an expected level of service. Bangor Savings Bank is using it to execute mundane yet time consuming scheduling challenges, such as computing part-time teller hours, or moving tellers around during the day to take care of other tasks based on customer traffic. 20Source: “Banks Turn to Staff Scheduling Software to Cut Costs”, American Banker, Jan 2012
  20. 20. Case example in “Big data” analytics Example: Cardlytics Partnerships with 6 Analytics Specialists / Transaction-driven marketing using propensity models Providers • Cardlytics combines transaction marketing with daily deal couponing and online banking to help banks provide a new service to customers. • It plays in the "merchant funded rewards" space, a nascent industry where banks allow merchants to offer customers rewards and discounts through the online banking channel, based on customer card transactions. • Banks never share any personally identifiable information on Cardlytics platform. It looks at anonymized transaction data only and matches merchant offers based on a forecasted propensity to buy. • Merchants only pay if the offers are successfully redeemed, and Cardlytics shares that revenue with the banks. • In Cardlytics model, banks present offers to customers via electronic statements. But users will soon be able to activate offers via the ATM and through social media sites like Facebook and Twitter To date, 100 – 200 financial institutions partner with Cardlytics to offer this service to their customers (e.g. PNC Financial Services Group) 21Source: “Cardlytics”, American Banker, Dec 2011
  21. 21. The rise of niche analytics firms such as Cardlytics thatcan be valuable partners to banks seeking to enhancetheir customer value proposition Transaction Driven Marketing 22Source: Cardlytics website
  22. 22. Agenda “Big Data” in Banking: Opportunities and Challenges Recent Trends in “Big Data” Analytics Turning Insights into Business Value: The “Moneyball” Advantage 23
  23. 23. Developing the “Moneyball” Advantage:Analytics-driven strategies vs. Conventional wisdom Using Analytics to Develop a Winning Advantage  Small market Oakland A‟s general manager Billy Beane success story as he uses statistical analysis to find overlooked talent to take on teams like the New York Yankees  Author Michael Lewis details how statistician Bill James showed that people overlooked the information that would reveal which strategies would be Analytics, when harnessed to its full potential, most effective in to compete and win in can serve to „level the playing field‟ and enable baseball smaller players to rapidly gain market share  The central premise of Moneyball is that the collected wisdom of baseball insiders (including players, managers, coaches, scouts, and the front office) over the past century is subjective and often flawed 24
  24. 24. Building Competitive Advantage:Moving up the Analytical Capability Curve Analytical Capability Curve(1)Competitive Optimization What is best that can happen?Advantage Predictive Modeling What will happen next? Forecasting What if these trends continue? Statistical Analysis Why is this happening Alerts What actions are needed? Query What exactly is the problem? Ad-hoc reports How many, how often, where? Standard reports What happened? Sophistication of Intelligence 25 Source: (1)“Customer Analytics – Cutting a New Path to Growth and High Performance”, Accenture, 2010
  25. 25. The Path from Insights to Business Value 6 Guiding Principles 1. Focus on the highest value opportunities 2. Start with key questions and hypotheses, not data 3. “Test, learn and refine” 4. Build internal analytics capability 5. Instill an analytics-driven culture to inform all strategic decisions 6. Augment with specialist analytics providers (where required) 26
  26. 26. Contact InformationDr Aaron SumSenior Vice President, Head of Strategy & Analytics(SME Banking)aaronsww@alliancefg.com 27

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