More Related Content Similar to OVERCOMING THE FEAR OF TRYING from Structure:Data 2012 (20) OVERCOMING THE FEAR OF TRYING from Structure:Data 20121. OVERCOMING FEAR OF TRYING
SPEAKER: John Lucker
Principal
Deloitte
Friday, July 27, 2012
2. GigaOM Structure:Data
2012
Overcoming Fear of
Trying:
Organizational &
Cognitive Challenges to
Implementing Business
John Lucker
Principal - Deloitte Consulting LLP
March 21, 2011
Friday, July 27, 2012
4. Behavioral Economics & Cognitive Biases – Redefining
Success
Current What a What a Analytical
business business business theoretical
performan is able to could achieve performan
ce achieve with with analytics ce
analytics
1 2 3 4
The
Winners!
Deloitte Analytics Institute © 2011 Deloitte LLP
Friday, July 27, 2012
5. Some Famous Thinkers Know That Improvement is
Possible
“The problem is not that baseball professionals are stupid; it
is that they are human. Like most people, including experts,
they tend to rely on simple rules of thumb, on traditions, on
habits, on what other experts seem to believe.”
-- Cass Sunstein & Richard Thaler review of Moneyball
“The most difficult subjects can be explained to the most
slow-witted man if he has not formed any idea of them
already; but the simplest thing cannot be made clear to the
most intelligent man if he is firmly persuaded that he knows
already, without a shadow of doubt, what is laid before him.”
-- Leo Tolstoy
Deloitte Analytics Institute © 2011 Deloitte LLP
Friday, July 27, 2012
7. Cognitive Bias Examples from Summer Time
Every summer – Watch Out for Sharks! (Availability
Heuristic)
However:
• It is more likely you will be killed by slipping on a wet floor
• During a 423 year period from 1580 to 2003 there were 1,909 shark attacks – 1 in 11.5
million in the US
• During the same 423 year period there were only 38 reported deaths caused by shark
attacks – 1 in 260 million in the US
Deloitte Analytics Institute © 2011 Deloitte LLP
Friday, July 27, 2012
8. Cognitive Bias Examples from Life Insurance
When applying for life insurance, it is not unusual to be
asked about “risky
activities” – do you Skydive? Rock Climb? Scuba dive?
(Herd Behavior)
Cause of Death Odds of
Dying (1 in)
Swimming 56,587
Cycling 92,325
Running 97,455
Skydiving 101,083
Soccer 103,187
Hang-Gliding 116,000
Scuba Diving 200,000
Source: http://www.medicine.ox.ac.uk/bandolier/booth/Risk/sports.html
Deloitte Analytics Institute © 2011 Deloitte LLP
Friday, July 27, 2012
9. Cognitive Bias Examples from Insurance Claims
Experience
The results are never completely all over the map…
…but they usually contain telling inconsistencies
ILLUSTRATIVE
Risk Characteristic Assigned Rank
Years in Business 1 4 6 5 3
Loss Control Results 6 6 5 7 5
Total Number of Prior Claims (past 3 years) 3 2 3 3 2
Number of Locations 7 5 7 6 8
Geographic Location/Territory 8 7 8 8 6
Business Financial Score 2 3 4 4 1
Prior Year Loss Ratio of the Agent 5 8 2 2 4
Class of Business 4 1 1 1 7
Deloitte Analytics Institute © 2011 Deloitte LLP
Friday, July 27, 2012
10. Cognitive Bias Examples from Insurance Claims
Experience
It’s hard enough to get everyone to agree on variable
rankings…
… what about weighing the variables together?
Most underwriters
assign weights
that are divisible by
5
What can we gather
from this?
Deloitte Analytics Institute © 2011 Deloitte LLP
Friday, July 27, 2012
11. Cognitive Bias examples from Consulting Experience
Examples
• Comment from a Financial Services executive in a pricing meeting: “I
recommend we increase interest rates (APR) on our accessory motor lending
products by 30 basis points this year because this summer will be very hot
and the demand for motor “toys” will increase”.
• How does the executive know what the weather will be like in a few months?
Can she be sure that demand for motor “toys” will go up?
• During a price optimization exercise a food retailer decided not to price
different flavors of a product differently despite empirical evidence that some
flavors were significantly more sensitive to price. They (wrongly) discussed
that other food products were not priced that way. By pricing all flavors the
same the company was unable to obtain a gross margin benefit of more than
$4 million in the first year.
• Comment from a Retail executive: “we don’t care as much about the needs of
men in our catalog and stores” because 80% of our customers are women.
• But what if sales are saturated for women and there could be a latent
opportunity to sell more to men? If the infrastructure is there to sell to men
(since 20% of sales is to men) then why not see if there is a way to increase
male traffic to stores and catalog?
Deloitte Analytics Institute © 2011 Deloitte LLP
Friday, July 27, 2012
12. Oct 1, 2010 to Sep 30, 2011 – I Got More than 250
Credit Card Offers
I am familiar with how several of the credit card issuers and special program sponsors
that sent me offers have worked to improve their customer insights capabilities.
However, in my wallet, are two credit cards – one issued to me in 1986 and the other in
1990. Why do the companies sending me these offers feel that I am inclined to
respond?
Deloitte Analytics Institute © 2011 Deloitte LLP
Friday, July 27, 2012
13. The Moral of our Story
• The human brain is very bad at juggling probabilities,
weighing evidence, and making decisions that relate to
contingent events
• Our brains didn’t evolve to efficiently do the tasks that we are
required to do every day in business
• “Human judges are not merely worse than optimal regression
equations; they are worse than almost any regression
equation.”
‒ Richard Nisbett & Lee Ross, Human Inference: Strategies and Shortcomings of
Social Judgment
Deloitte Analytics Institute © 2011 Deloitte LLP
Friday, July 27, 2012
15. End-To-End Approach to Advanced Analytics
Implementation
In today’s competitive market, the development and deployment of advanced analytics to solve
pervasive and vexing business problems goes beyond a statistical exercise. As companies
become increasingly savvy with the use of advanced analytics (including predictive models),
market leaders will be those organizations that take a holistic end-to-end approach to predictive
model development and deployment.
Advanced
Analytics
Development
Technology
Strategy
Deploymen
(Innovation, End-to-End
t,
Pricing, Advanced Analytics
Integration
Distribution, Development and
and
Marketing, Deployment Business
etc.)
Intelligence
Business
Implementation,
Business Process
Redesign, Change
Mgmt
Deloitte Analytics Institute © 2011 Deloitte LLP
Friday, July 27, 2012
16. Analytics Integration & Deployment: Some Critical
Success Factors
• Define a clear link between analytics development and
business implementation to the corporate strategy
• Develop an analytics roadmap of short/medium/long-term
objectives with business benefits to drive the timeline – self-
funding mechanisms are great when possible
• Ensure executive buy-in and support to drive unrelenting
focus on value/benefit realization
• Transform business process when appropriate versus
shoe-horn solutions into old processes
• Use defined, validated and automated business rules to
drive consistency wherever possible
• Define organizational readiness and manage change
proactively and definitively
• Define performance metrics and measure the organization
accordingly
Deloitte Analytics Institute © 2011 Deloitte LLP
Friday, July 27, 2012
17. Contact
John Lucker
Principal
Deloitte Consulting
(860) 725-3022
jlucker@deloitte.com
Deloitte Analytics Institute © 2011 Deloitte LLP
Friday, July 27, 2012