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Valen Analytics 2015 Summit:
Bringing it all together and getting business
value out of Advanced Analytics
CONFIDENTIAL AND PROPRIETARYAny use of this material without specific permission of McKinsey & Company is strictly prohibited
March, 2015
11
Everyone, everything, every interaction generates “exhaust” data
Transactions
Social
Mobile
Audio/video
Scientific/engineering ‘Internet of things’
22
Many companies are creating their own data through rapid, structured A/B testing
33
And examples of data-driven decision making are everywhere
House of Cards: A Guaranteed Hit
What they analyzed
• 30 million ‘plays’ a
day
• 4 million subscriber
ratings
• 3 million searches
• 76,897 unique ‘tags’
What they learned
• Unexpected directors,
actors, and shows were
disproportionately popular
among viewers:
– David Fincher
– Kevin Spacey
– The British ‘House of
Cards’
4
Wine Analytics Case
Recent history in the Bordeaux wine world highlights both the
challenges and value of combining business knowledge with
analytics. It involves an expert/guru and a modeler/geek.
555
5Meet Robert Parker Jr., Wine Guru
Robert Parker is the foremost wine taster worldwide and his subjective 100 point
ratings often drive whether a vineyard is successful and if its wines get invited into the
best restaurants.
Robert Parker is the foremost wine taster worldwide and his subjective 100 point
ratings often drive whether a vineyard is successful and if its wines get invited into the
best restaurants.
The “Guru”
666
Meet Orley Ashenfelter, Princeton Economist
Orley is a respected economist at Princeton who edited the American Economic
Review. A wine lover who noticed that Bordeaux are best when the grapes are ripe
and their juice is concentrated. In years when the summer is hot, grapes get ripe.
And, in years of below-average rainfall, the fruit gets concentrated. So it's in the hot
and dry years that you tend to get the legendary vintages.
Orley is a respected economist at Princeton who edited the American Economic
Review. A wine lover who noticed that Bordeaux are best when the grapes are ripe
and their juice is concentrated. In years when the summer is hot, grapes get ripe.
And, in years of below-average rainfall, the fruit gets concentrated. So it's in the hot
and dry years that you tend to get the legendary vintages.
The “Geek”
777
Professor Ashenfelter’s Predictive Model
DIVERGENCE
302724211815129630
High Likelihood of Good Vintage
High Likelihood of Bad Vintage
Overall Wine Score
Wine quality = 12.145 + 0.00117 winter rainfall
+ 0.0614 average growing season temperature- 0.00386 harvest rainfall.
Orley built a predictive model for wine using expert knowledge.
88
So can insurance companies really take advantage of these trends to create a competitive advantage through “Advanced Analytics”?
Increasing digital interactions between carriers and their
customers is enabling real-time testing of hypotheses
On demand mobile access enables user to access
information at any location, at any time, on any device
(e.g., mobile iPad applications,)
Billions of dollars of investment is driving a rapidly
evolving ecosystem of new analytic technology and tools available for use by carriers
A massive wave of new sources of data has coming
online (unstructured data, external data, open data
initiative, internet of things)
99
Or is this all buzz?
Data Scientists
Big Data
Data Lakes
1010
Underwriting and PricingDistribution, Sales , Marketing
Claims and Servicing
▪ Submission quote/
decline decision support
▪ Prospect/ lead
identification
▪ Cross-sell and up-sell
identification
▪ …and many, many more
▪ Enhance risk selection
▪ Guide deal structure (SIR,
attachment point, etc.)
▪ Use of MVRs, inspections, etc.
▪ Elasticity and renewal pricing
▪ …and many, many more
▪ Claim severity
prediction (over life of
claim)
▪ Defense firm
assignment
▪ Subrogation
identification
▪ …and many, many
more
We believe there are many, many use cases that could add valueCommercial P&C Example
1111
Examples
Scoring prospects using only external data
A
Data-driven matching of Defense firms to claims
D
▪ External data (such as ODI OSHA,
weather, FBI data) can help identify
attractive companies before a
submission is received
▪ Efficiency and Effectiveness of
specific Defense Firms depend on
the elements of a claim (such as
plaintiff firm, court, type of injury, etc.)
▪ ~5 point improve-
ment in new
business loss ratio
Prioritizing new Middle Market submissions
B
▪ Underwriters make intuition-driven
decisions and if provided with a
“likelihood to win” score would make
different decisions on what to quote/
decline
▪ 25% improvement
to hit ratios
Mining social media data to inform risk selection
C
▪ Analyzing sentiment of pharma-
ceuticals/ med devices can inform
underwriting decisions as changes in
sentiment precede law suites
▪ 3 point improvement
in overall loss ratio
▪ ~10% reduction in
defense spend
Rational Impact
And we have seen Advanced Analytic use cases deliver significant value
1212
▪ Failure to launch
– Limited business appetite – too much focus on Business
Intelligence and the next report
– Inability to build the business case for investment
– Perception that it is too costly and/ or you do not have the data
▪ Failure to deliver meaningful insights
– Poor data quality, completeness and access
– Seen as “IT’s thing”
– Results not put in the business’ language/ tied to their processes
▪ Failure to operationalize the insights
– Inability to efficiently integrate analytic solutions into workflow
– Limited front line adoption (don’t believe the output, the analytical
solution creates more work) or over-reliance on the model (the
model told me the price has to go up)
However, we do see carriers facing real barriers in delivering value from Advanced Analytics
1313
Competitive differentiators
▪ Linking your analytics agenda to your strategy
▪ End-to-end, cross-functional teams that drive use cases from inception to adoption
▪ “Two-sport” analytic managers that can act as liaisons between analytics team and business
▪ Sourcing and enriching data through business-back hypotheses (“Smart Box”)
Table stakes
▪ Easy access to large structured and unstructured data files across (likely multiple) systems (ETL factory)
▪ Cadre of talented statisticians, modelers, programmers, etc. (Data Scientists)
▪ Technology to support advanced statistical techniques (Analytic Sandbox)
How to get business impact from Advanced Analytics: table stakes versus the true competitive differentiators
1414
Data ecosystem
Modeling insights
Data modeling“Black box”
Heuristic insights“Smart box”
Workflow integration
Process redesign
Techenablement
Adoption
Internal
External
Capability building
Change management
Source of value
1 2 3 4 5
Source of valueData ecosystem
Modelinginsights
Workflowintegration
Adoption
▪ Clear articulation
of the business
need and
projected impact
to Loss Ratio,
Growth, etc.
▪ Crisp view of how
the solution
would be used by
the business
▪ Data ETL from
internal systems
▪ Appending key
external data
▪ Data enhance-
ment (new
variables)
▪ Advanced
analytical
analyses
to drive new
insights
▪ Codified
heuristics
dispersed in the
organization to
enhance
analytics
▪ Easy-to-use user
interface built in
the platform
▪ Redesigned
processes to
embed rules in
the workflow
▪ Developed
frontline
and manage-
ment
capabilities
▪ Proactive change
management”
and tracking
of adoption
with performance
indicators
Successfully delivering a use case requires covering 5 bases – ideally with one cross-functional team
1515
Strong analytic problem solving
Familiar with business processes
Buildsrelation-ships
Expert project leader-ship
Two-sport manager role
▪ Act as “translator” between the
analytics function and the business
– Understand business processes
and various relevant data sources
– Identify opportunities (use cases) to
apply analytics to enhance decision
making
– Structure analytical problems and
syndicate with key analytic and
business leadership
– Interpret analytics results and draw-
out the practical business insights
▪ Facilitate business implementation by
designing frontline relevant process
integration, data visualization, and
capability building programs
▪ Drive analytical proof of concept pilots
Deploying the right talent is critical for success – The new two-sport analytics manager
1616
▪ Senior analytics person
and/ or committee in
charge of advanced
analytics agenda
▪ Pilots running in several
areas
▪ Investments in new
talent, tools, etc.
▪ Use of unstructured
data (e.g., text mining)
▪ Growing Center of
Excellence
▪ One or two pockets of
advanced analytics (predictive/
prescriptive analytics)
▪ Limited/ no dedicated
expertise
▪ Standard tools and data
sources (structured internal
data)
▪ Growing alignment amongst
the leadership around the
opportunity
▪ Proven economic impact
from specific use cases
▪ Multiple champions
across different business
areas/ functions
▪ Clear analytics agenda
with a portfolio of use
cases
▪ Suite of new capabilities
commonly used
▪ Center of Excellence
establishes (size/ scope/
decision rights vary by
institution)
Early stage
Ramping up
Substantial impact
Driving broad impact from advanced analytics is a multi-year journey
1717
Service/ customer
experience
Etc.Claims
Sales/ distribution
UW/ Risk selection for Commercial
lines
Initial area of analytic expertise
(Auto pricing, Marketing, Small Commercial UW/
pricing, etc.)
Where to start?
What technology investments?
How to organize?
How to measure?
As part of that journey, many carriers are looking to radiate knowledge and expertise from one areas to all other parts of their business
What talent do I need?
Who owns the agenda?
1818
Lessons learned from designing a successful data and analytics strategy
Operationalizing the data-driven insights is often a bigger challenge than
producing the insights
Drive data & analytics transformation on the back of strong use casesbefore heavily investing in technology and capabilities
Rather than “boiling the ocean”, concentrate on key priority data domains that drive value and fix end-to-end
Commitment from the top is needed to make the transformation happen –
getting buy-in, communication and change management are key to success
Use cases delivery should be based on a prioritized roadmap that is co-
developed with the business
Responsibility for data (quality, integrity, access) needs to be in the hands of the business, supported by a strong IT organization (tandem structure)
Scaling the impact requires attracting top data analytics talent and creating a strong learning culture
4
1
5
2
3
6
7
1919
Ways that Ingenuity supports clients
Description
Relevant phases(from page 14)
Create a “heat map” of specific analytic opportunities
1
▪ Create a business-back prioritization of advanced analytic opportunities based on impact and feasibility
▪ Conducted at the enterprise level, across specific BU’s/ functions, or for specific end-to-end processes
▪ Step 1, but often across the entire enterprise
End-to-end execution of a use case
2
▪ Build and deploy a new analytical solution for a use case (e.g., claim severity prediction)
▪ Determine how the operations will utilize the analytical insight and build the necessary technological integration (e.g., bespoke application)
▪ All 5
Operationalize an existing (often “dormant”) model
3
▪ Re-design the technological enablement and process change/ adoption approach for model
▪ Additional enhancements to analytic model are typically made along the way
▪ Primarily 4 and 5
Build technical capabilities4
▪ Provide support to clients that are ramping-up their data and analytics team
▪ The capability building is typically focused on the data management components and/ or predictive analytic tools and techniques
▪ Primarily 2 and 3
2020
For further information and/ or discussion please reach-out to us
Richard Clarke, Ph.D.
� Richard leads Ingenuity, a McKinsey Solution focused
on delivering impact through Advanced Analytic
solutions for Insurance carriers
� Ingenuity brings together expertise in data science, data
management, software development and process
change to deliver impact to its’ clients
� Richard is based in McKinsey’s Pittsburgh office
Overview
Lee Scoggins
� Lee is a Senior Solution Manager at Ingenuity where he
leads the day-to-day development and delivery of
advanced analytical solutions
� Lee also leads product development for Ingenuity,
including identification and evaluation of new
partnerships and managing the product portfolio
� Lee is based in McKinsey’s Atlanta office
Overview
