afsc2012 turning big data into a competitive differentiator v final

Turning “Big Data” into a

Competitive Differentiator

Dr Aaron Sum

Senior Vice President, Head of Strategy & Analytics

(SME Banking)

“Big Data” in Banking: Opportunities and Challenges

Recent Trends in “Big Data” Analytics

Turning Insights into Business Value: The “Moneyball” Advantage

Agenda

3

The amount of global data is projected to

more than double every 2 years

• 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

Acceleration of Data Growth“Data, data, everywhere ...” (1,2)

(1) “Big Data: The next frontier for innovation, competition & productivity”, McKinsey Global Institute, June 2011

(2) “Data, data, everywhere”, The Economist, Feb 2010

4

The New York Stock Exchange creates 1 terabyte of data

per day vs. Twitter feeds that generates 8 terabytes of

data per day (or 80 MB per second) .

10,000 payment card transactions per second around the

world.

210 billion electronic payments generated worldwide in

2010. This is expected to double by the end of the

decade.

Between 2009 and 2014, the total number of US online

banking households will increase from 54 million to 66

million.

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.

In financial services, we are now seeing

new waves of data growth

Recent Headlines: Data Growth in Financial Services

Big Data Defined

“Big data" refers to

the management,

access, and analysis

of substantially larger

sets of (typically

unstructured) data

than had been

conventionally

possible until

recently.

5Source: Information Week, American Banker

The Banking sector is poised for

substantial gains from the use 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

Spectrum of „big data‟Data intensity by sector

Source: (1) “Big Data: The next frontier for innovation, competition & productivity”, McKinsey Global Institute, June 2011

The quest for a true “360

Degree Customer View”

6

However, banks must navigate the

complexity, variety and velocity of “big data”

Complexity

• Growing volume of unstructured data from banks‟ current applications

as well as the newer technologies being adopted

• Adds another layer of complexity to the elusive “360 Degree

Customer View” which banks have been pursuing

Variety

• Mobile technologies, social networking tools, etc are significantly

increasing the stock of unstructured data within the banks

Velocity

• The rate of change of new data formats over the past 5 years have

been unprecedented; the trend is expected to continue

Big Data Challenges

7

“Big Data” Analytics, when harnessed

correctly, can be a substantial competitive edge

From Traditional Sources of

Competitive Advantage …

Differentiation (e.g. product, price,

service)

Cost Leadership

… to Analytics-Driven

Competitive Advantage

OR

“Trade off between low cost or focused differentiation, or hybrid

approach”

“Right product, price, service levels (at the right cost), for

the right customer”

• Product

• Price

• Cost

• Service

Business strategy

• Customers

Analytics

Analytics must move from the „fringe‟ to the „core‟ of all strategic and tactical

business decisions, to develop this competitive advantage

“Big data”

8

Multiple

sources of

competitive

advantage

9

Given the current climate of protracted “slow

growth” and “hyper competition”, analytics is key

to uncover growth / optimization opportunities

Current Challenges

• Protracted “Slow Growth”:Increasingly challenging for banks to sustain revenue momentum; cost optimization begins to take centre-stage

• “Hyper-competition” and continued margin compression: Competition continues to intensify as margins are eroded

Key Imperatives

• Finding new market segments and revenue streams

• Maximizing sales and marketing effectiveness

• Optimizing costs and existing resources

• Risk-based pricing

• Ability to forecast market trends, gauge customer sentiment and adapt business strategies quickly




Agenda

10

Sentiment Analysis to

gauge „real time‟

response to campaigns

Recent Trends

“Big Data

Analytics”

Predictive staff

scheduling to optimize

costs

Trend Forecasting &

Market Research via

novel data sources

Granular Micro-

Targeting of

customers / segments

Proactive Monitoring to

detect early „fault

triggers‟

Recent Trends:

“Big Data” Analytics

Partnerships with

Analytics Specialists /

Providers

6

1

2

3

4

5

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• Sales effectiveness

• Opening up new target segments

• New product response

• Campaign effectiveness

• Brand health

• Cost optimization • Forecasting trends

• Service quality

• Proactive customer management

• New customer value proposition

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

Case example in “Big data” analytics

Example: Large Spanish Bank

• 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.

Source: Bank Annual Reports, Analyst Reports

Granular Micro-

Targeting of customers

/ segments

1

12

Micro-targeting via personalized campaigns

“The Bank”

finances until

xx/xx/xx your VISA

purchases of 1.200

EUROS in 12 quotas

of 107,18 EUROS

per month.

To finance it,

please answer TAJ

1 and the sum of

coordinates B1 +

E2

1TAJ 1 50

2Operation

successfully done.

We have financed

your VISA purchase

of 1.200 EUR in 12

payments of 107,18

EUR per month.

Check this

transaction in

xxbank.com

3

Example of “real-time” offer: Proposal for a personal loan just after the purchase event

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

The customer buys an LCD TV with

his credit card and the Bank sends

him the following message

The operation ends when the customer

receives the confirmation message showing

that his purchase has been financed

If the customer is interested,

he replies with a code

Hundreds of automated algorithms (continually tested &

refined), generate thousands of personalized campaigns

every month, pushed to customers’ mobile phones

13

Granular Micro-

Targeting of customers

/ segments

1

Business Value Generated

• Enable targeting of new segments based on deeper understanding

of risk-returns (e.g. higher risk segments that were previously

„blacklisted‟)


Example: Progressive Insurance

• 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.

Source: “Competing on Analytics”, Thomas H Davenport, Don Cohen and Al Jacobson14

Opening up new target customer segments


Example: Large Australian 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

Sentiment Analysis to

gauge „real time‟

response to campaigns

2

• 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.

Source: IDC “Journey into Big Data: From Transactional Data to Big Data Analytics”, 2011

Examples: ING, Citi, SunTrust

15

Tracking social media sentiment towards campaigns

Leading banks are embracing online & social media

analytics of customer sentiment and opinions to gauge

response to new products and campaigns

Source: IDC “Journey into Big Data: From Transactional Data to Big Data Analytics”, 2011

Leading banks are

already turning to

social analytics to

gauge sentiment

towards key

initiatives such as:

• New Product

launches

(e.g. ING)

• Marketing

campaigns

ObservationsExample: Social analytics dashboard

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Example: UBS Investment Bank

Trend Forecasting &

Market Research via

novel data sources

3

• UBS Investment Research issued its earnings preview for Wal-Mart's 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.

Source: CNBC “New Big Brother: Market-Moving Satellite Images “, Aug 201017

Forecasting sales trends using satellite 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.

More Accurate Forecasting

Source: CNBC “New Big Brother: Market-Moving Satellite Images “, Aug 2010

Novel application of “Big Data”

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UBS found greater correlation from its satellite

data projections than its traditional statistical

methods

http://www.cnbc.com/id/38722872/New_Big_Brother_Market_Moving_Satellite_Images


Example: Bank of America

Proactive Monitoring to

detect early “customer

service issues”

4

• 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

Source: “State of the Art: Sentiment Analysis”, Nexidia 200919

Contact centre sentiment analytics


Example: Bangor Savings Bank (USA)

Predictive staff

scheduling to optimize

costs

5

• Predictive model that forecasts teller staffing based on forecasted transaction

volumes.

• The tool uses business intelligence to analyze transaction data that's

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.

Source: “Banks Turn to Staff Scheduling Software to Cut Costs”, American Banker, Jan 201220

Predictive model to optimize branch staffing


Example: Cardlytics

Partnerships with

Analytics Specialists /

Providers

6

• 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

Source: “Cardlytics”, American Banker, Dec 201121

To date, 100 – 200 financial institutions partner with Cardlytics to offer

this service to their customers (e.g. PNC Financial Services Group)

Transaction-driven marketing using propensity models

http://www.cardlytics.com/Default.aspx

22Source: Cardlytics website

The rise of niche analytics firms such as Cardlytics that

can be valuable partners to banks seeking to enhance

their customer value proposition

Transaction Driven Marketing

http://www.cardlytics.com/Default.aspx




Agenda

23

Developing the “Moneyball” Advantage:

Analytics-driven strategies vs. Conventional wisdom

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

most effective in 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

Using Analytics to Develop a

Winning Advantage

Analytics, when harnessed to its full potential,

can serve to „level the playing field‟ and enable

smaller players to rapidly gain market share

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Building Competitive Advantage:

Moving up the Analytical Capability Curve

Competitive

Advantage

Sophistication of Intelligence

Standard reports

Ad-hoc reports

Query

Alerts

Statistical Analysis

Forecasting

Predictive Modeling

Optimization What is best that can happen?

What will happen next?

What if these trends continue?

Why is this happening

What actions are needed?

What exactly is the problem?

How many, how often, where?

What happened?

Source: (1)“Customer Analytics – Cutting a New Path to Growth and High Performance”, Accenture, 2010

Analytical Capability Curve(1)

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The Path from Insights to Business Value

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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)

6 Guiding Principles

Dr Aaron Sum

Senior Vice President, Head of Strategy & Analytics

(SME Banking)

[email protected]

Contact Information

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afsc2012 turning big data into a competitive differentiator v final

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