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
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© 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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