Bank on the Customer Nathan Banks

Strategy Lead, Business Analytics & Optimisation, IBM GBS

08/11/2012

© 2009 IBM Corporation

Building a smarter planet

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Key take outs from today

How to leverage uncertain data to deepen relationships, increase revenue and

streamline operations

How to use non-traditional channels and innovation to engage customers and drive

new business models

How the real-world use of big data is creating value using analytics

© 2009 IBM Corporation

Building a smarter planet

Big data is a business priority – inspiring new models and

processes for organisations, and even entire industries

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© 2009 IBM Corporation

Building a smarter planet

Big data embodies new data characteristics

created by today‟s digitised marketplace

Characteristics of big data

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© 2009 IBM Corporation

Building a smarter planet

Improving the customer experience by better understanding

behaviors drives almost half of all active big data efforts

Customer-centric outcomes

Digital connections have enabled customers

to be more vocal about expectations and

outcomes

Integrating data increases the ability to create

a complete picture of today‟s „empowered

consumer‟

Understanding behavior patterns and

preferences provides organisations with new

ways to engage customers

Other functional objectives

The ability to connect data and expand

insights for internally focused efforts was

significantly less prevalent in current activities

Big data objectives

Top functional objectives identified by organisations with active big data

pilots or implementations. Responses have been weighted and aggregated.

Customer-centric outcomes

Operational optimisation

Risk / financial management

New business model

Employee collaboration

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© 2009 IBM Corporation

Building a smarter planet

Customer analytics creates a high-impact

start to big data

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“I think big data will significantly

impact the business delivery and

consumer landscape by helping

service providers and retailers

better predict consumer needs

and reduce overall costs through

better supply-chain management,

increased speed in delivery and

higher sales”

– Entertainment /Media executive

United States

Customer analytics imperative

Customer analytics imperative Focus initial big data initiatives on areas that can provide the

most value to the business

Customer analytics enable better service to customers as a

result of being able to truly understand customer needs and

anticipate future behaviors

Dynamic customer expectations To effectively cultivate meaningful relationships with

customers, organisations must connect with them in ways

their customers perceive as valuable

The value may come through more timely, informed or

relevant interactions

Value may also come as organisations improve the

underlying operations in ways that enhance the overall

experience of those interactions

© 2009 IBM Corporation

Building a smarter planet

Business cases must include explicit forecasts of how

technology investments will impact the bottom line

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“I believe big data will force

companies to re-think their

structures and business divisions to

focus more on those areas that are

most relevant to the

accomplishment of the strategy and

corporate goals, and not just

financial, but also in terms of

customer satisfaction, product

development, research, etc.”

– Insurance industry executive

Mexico

Business case details

Articulating the case Many organisations are basing their business cases on

the following benefits that can be derived from big data:

Smarter decisions – Leverage new sources of data to

improve the quality of decision making

Faster decisions – Enable more real-time data capture and

analysis to support decision making at the “point of impact”

Decisions that make a difference – Focus big data efforts

toward areas that provide true differentiation

Secure executive support An important principle underlies each of these

recommendations: business and IT professionals must

work together throughout the big data journey

Active involvement and sponsorship from one or more

business executives throughout this process is needed to

advocate for investments

© 2009 IBM Corporation

Building a smarter planet

How to use non-traditional channels & innovation to

engage customers and drive new business models

8

© 2009 IBM Corporation

Building a smarter planet

How the real-world use of big data is creating

value using analytics

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© 2009 IBM Corporation

Building a smarter planet

Santam Insurance: Predictive analytics improve

fraud detection and speed up claims processing

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Solution

Business Opportunity

Results

South Africa’s largest short-term insurance company uses predictive analytics to uncover a major insurance fraud

syndicate, save millions on fraudulent claims and resolve legitimate claims 70 times faster than before.

Gained the ability to spot fraud early with an advanced analytics

solution that

captures data from incoming claims, assesses each claim against

identified risk factors and segments claims to five risk categories,

separating higher-risk cases from low-risk claims

Plans to use propensity modeling to enhance and refine

segmentation process as more data becomes Like most insurers around the world, Santam was losing

millions of dollars paying out fraudulent claims every year

Expenses were being passed on to the customer in the

form of higher premiums and longer waits to settle

legitimate claims

To improve its bottom line and enhance customer

satisfaction, the company needed to detect and stop

insurance fraud early in the claims process

It also needed to find a way to isolate risky, fraudulent

claims so that claims managers could more quickly

process lower-risk claims

Identified a major fraud ring less than 30 days after implementation

Saved more than $2.5M in payouts to fraudulent customers, and

nearly $5M in total repudiations

Reduced claims processing time on low-risk claims by nearly 90%

Cut operating costs by reducing the number of mobile claims

investigations

© 2009 IBM Corporation

Building a smarter planet

Vestas: Better data analysis capabilities lower

costs and improve effectiveness

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Solution

Business Opportunity

Results

Vestas Wind Systems A/S optimises capital investments based on 2.5 petabytes of information and big data technologies

Vestas can now help its customers optimise turbine placement and, as

a result, turbine performance.

Uses a big data solution on a supercomputer -- one of the world‟s

largest to date -- and a modeling solution to harvest insights from an

expanded set of factors including both structured and unstructured data

Wind turbines are a multimillion dollar investment with a

typical lifespan of 20-30 years

Placement depends upon a large number of location-

dependent factors

Vestas has been unable to support data analysis of the very

large data sets the company deemed necessary for

precision turbine placement and power forecasting due to

inadequate infrastructure and reliance on external models

Insights lead to improved decisions for wind turbine placement and

operations, as well as more accurate power production forecasts

Greater business case certainty, quicker results, and increased

predictability and reliability

Decreased cost to customers per kilowatt hour

Reduction by approximately 97 percent – from weeks to hours – of

response time for business user requests

Greatly improves the effectiveness of turbine placement

© 2009 IBM Corporation

Building a smarter planet

Getting Started…

Commit initial

efforts to drive

business value

Build analytical

capabilities based on

business priorities

1 Develop

enterprise-wide

big data blueprint

Create a business

case based on

measurable outcomes

Start with existing

data to achieve

near-term results

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2 3

5

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© 2009 IBM Corporation

Building a smarter planet

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