Presentation on theme: "Analytics…What are they? Why they are important? The important thing is, this has six different examples of very important areas of applications of analytics."— Presentation transcript:
Analytics…What are they? Why they are important? The important thing is, this has six different examples of very important areas of applications of analytics and how success and failures had huge impact on the organizations, economy, and governments Key: Analytics … What is It? Why is It Important?
Brief Introduction to Predictive Analytics Applications Examples of Marketing-Socio-Economic-Political Dynamics Sam-Nethra Sambamoorthi, PhD
Analytics … What is It? Why is It Important? A distinctive capability or product or service is the reason why organizations exist, and organization with distinctive capabilities exist making profit and delivering value to its stakeholders. What is analytics? – To ‘analyze’ means, to separate out into constituent parts or elements and determine the essential elements or features of –Dictionary.com What is business intelligence? – Analytics that provides full picture of the interactions of business operations and the levers that help manage the businesses. Steady state of the processes are inherently assumed in these situations. What is predictive Analytics? – Predictive analytics covers the broad spectrum of dynamics of consumers (note: Patients are also consumers, in this broad sense), so that the outcomes of any interaction or intervention or exposure is predictable and best optimized decisions can be managed resulting in less loss and better output. Why Analytics matters? – Analytics, as intelligence, is a guiding light, to manage processes and dynamics where known efficiencies have been achieved, and yet try to uncover for us those areas of inefficiencies and unmet market opportunities from the point of view of business operations, or fraud detection or risk management other things remain equal. Analytics is equally applicable for non-profit organizations and governmental departments Yet, it can be a challenging or a dangerous job that can land into jail because prediction was not done correctly or for over-stepping into privacy and ethical areas.
Ex1: 2009 Italy’s L’Aquila Earthquake On 6 th April, 2009, the Abruzzo region, in central Italy had a deadly 5.8 RS earth quake
308 People died, 1,600 people injured, and 65,000 people became homeless with a property loss of $16 Billion
Ex1: The Judge sentenced six scientists and a government official of National Commission for the Forecast and Prevention of Major Risks, to jail terms http://www.nytimes.com/2012/10/27/opinion/a-failed-earthquake-prediction-a- crime.html?_r=0http://www.nytimes.com/2012/10/27/opinion/a-failed-earthquake-prediction-a- crime.html?_r=0 – Is failure to predict a crime? Oct 12, 2014 Scientists acquitted: http://www.thehindu.com/opinion/editorial/laquila-italy-earthquake-legal- manslaughter-case-bizarre-verdict-reversed/article6605371.ece#comments – Nov 11, 2014http://www.thehindu.com/opinion/editorial/laquila-italy-earthquake-legal- manslaughter-case-bizarre-verdict-reversed/article6605371.ece#comments
Ex2: The Baseball Team Oakland A’s, the lowest ranked team, surprised the sports world winning 20 Consecutive games to reach the top in 2002 garnering four division titles with one tenth of the budget of the top team The true story is captured in the movie Moneyball, on what to measure, how to analyze, how to implement insights from analytics, and what challenges are faced by the new breed of analytically competitive management It captured the imagination of ordinary people on the importance of analytics and predictive methods Moneyball and Labor Market Inefficiency
Ex3. Subprime Lending and Everything With That … Still The Tremors are Around Banks Started acting like investment companies under the repeal of Glass-Steagall act. The trigger for the repeal started 20 years back, but corporations took advantage of it post 9/11/2001, an horrendous event. The $12 Trillion dollars mortgage industry was open for unprotected investments 1% shift in the highly predictive 90 day default delinquency scoring (FICO Score) opened for more credit easing Mortgages that were failing under FHA requirements were sold under ‘credit default swap’ insurance product from AIG
Ex4: Netflix Lost $13 Billion Dollars of its Market Value (80% ) in Six Months in 2011 Netflix Lost $13 billion dollars in market value, because they did not predict well the consumer behavior using predictive price elasticities, when they were coming out with new pricing plans for three types of consumers (On demand net streamers, DVD only, mixed product users) Latest news. It is back on tract after one and half year of challenges, because of the strength of the visionary mode of delivery and consolidated content management. It is a market leader and technology is on its side
Ex5: Prediction Influenced By Behavioral Dynamics of Independent Voters Trajectory of vacillation for independent Obama Likelihood Romney Likelihood
Ex5: Prediction Influenced By Behavioral Dynamics of Independent Voters Nate Silver Analysis - Trajectory of vacillation for independent voter is basically random fluctuations and/or based on unscientific survey research but in a different place he says he has used Bayesian updating process to update the winning probabilities Obama Likelihood Romney Likelihood
Predicting Next Behavior/Exposure/Event Is Important for Organizations and Government Predictive analytics: The power to predict who will click, buy, lie, or die – By Eric Segal Homeland security is interested in evaluating what type of alert is likely to pop up this month, next month, following, … Insurance companies know who is likely to retire at what age which influences the offer differentials Insurance companies and health organizations predict death of an individual, to better manage the costs or cash outlay from the balance sheets. You are more predictable than your closest people could ever do because of precious data – The company, Target was able to predict pregnancy using the associations of products people buy while family members did not know. Companies know when you are likely to quit your job, when you will have your first child, and how long you will postpone for the next child, where you will make your home, will you be a home owner or a renter and what type of neighborhood you will live, what car you will buy and when … and so on. Parole boards rely on algorithms to decide who stays in prison and who goes free and what kind of monitoring is part of the judgment All the above and the recent political discussions of NSA’s snooping gives raise to the opportunities of privacy protection for ordinary citizens