OVERCOMING THE FEAR OF TRYING from Structure:Data 2012

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Presentation from John Lucker, Deloitte
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More at http://event.gigaom.com/structuredata/

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OVERCOMING THE FEAR OF TRYING from Structure:Data 2012

  1. 1. OVERCOMING FEAR OF TRYING SPEAKER: John Lucker Principal DeloitteFriday, July 27, 2012
  2. 2. GigaOM Structure:Data 2012 Overcoming Fear of Trying: Organizational & Cognitive Challenges to Implementing Business John Lucker Principal - Deloitte Consulting LLP March 21, 2011Friday, July 27, 2012
  3. 3. Core ThemeFriday, July 27, 2012
  4. 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 LLPFriday, July 27, 2012
  5. 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 LLPFriday, July 27, 2012
  6. 6. Cognitive BiasesFriday, July 27, 2012
  7. 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 LLPFriday, July 27, 2012
  8. 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 LLPFriday, July 27, 2012
  9. 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 LLPFriday, July 27, 2012
  10. 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 LLPFriday, July 27, 2012
  11. 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 LLPFriday, July 27, 2012
  12. 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 LLPFriday, July 27, 2012
  13. 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 LLPFriday, July 27, 2012
  14. 14. Making ProgressFriday, July 27, 2012
  15. 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 LLPFriday, July 27, 2012
  16. 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 LLPFriday, July 27, 2012
  17. 17. Contact John Lucker Principal Deloitte Consulting (860) 725-3022 jlucker@deloitte.com Deloitte Analytics Institute © 2011 Deloitte LLPFriday, July 27, 2012
  18. 18. Friday, July 27, 2012

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