Newsmakers: The economics of terrorism and counterterrorism
“How many good guys are needed to catch the bad guys? That is the staffing question faced by counterterrorism agencies the world over. While government officials are quick to proclaim ‘zero tolerance’ for terrorism, unlimited resources are not made available to prevent terror attacks, nor should that be the case. Indeed, as with most public policy decisions, the appropriate staffing level depends upon both the benefits and costs of fielding counterterrorism agents.”
So wrote Edward H. Kaplan, Yale professor and president-elect of INFORMS (publishers of Analytics magazine), earlier this year in a blog posted on OUPblog. Kaplan also contributed a paper, “Socially efficient detection of terror plots,” to a special issue of Oxford Economic Papers focused on the economics of terrorism and counterterrorism.
Kaplan concludes his blog with this ominous note: “ … it is well known that many terrorist organizations behave in strategic fashion and are able to adapt their behavior to counterterror policy and tactics. This leads to a game theoretic model where strategic terrorists who understand how socially efficient staffing works modify their own attempted attack rates in accord with their own benefit-cost calculus. In this game, the resulting optimal terror plot-detection level depends upon the costs and benefits that terrorists assign to terror attacks, which provides yet another example of how strategic terrorists can manipulate counterterror agencies (or governments more broadly) to achieve their objectives.”
‘Clumpiness’ more profitable than traditional marketing segments
Marketing managers traditionally segment customers by three summary measures (also known as the RFM model): recency (the period of time since their last visit), frequency (how often they visit) and monetary value (how much they spend on a visit). However, a new study published in the INFORMS journal Marketing Science shows that, in contrast to traditional market segmentation, one based on “binge consumption” brings a higher long-term return to business.
Binge consumption is characterized by bursts of heavy buying interspersed by little or no buying. Study authors Yao Zhang of Credit Suisse and Eric Bradlow and Dylan Small of The Wharton School at the University of Pennsylvania call this pattern of consumption “clumpiness.”
In their paper “Predicting Customer Value Using Clumpiness: From RFM to RFMC,” the authors develop a new measure of clumpiness that extends the “hot hand” literature found in statistics journals. That is, just as athletes have periods of hot and cold performance (e.g., shooting), customers also have hot and cold periods of visiting (binge-visiting) and buying (binge-purchasing). They found that even after controlling for frequency, visits and monetary value, clumpy customers provide more economic value to the firm than non-clumpy ones.
Some customers purchase very frequently (high F) for a period of time but haven’t purchased in a while (low R). This behavior has two possible explanations. One is that these customers have quit or “churned.” The other possibility is that these customers are clumpy or between “bursts.” When they come back they will “burst” again. Firms can make money on customers who are between bursts if they successfully encourage them (possibly via target marketing) to return.
The authors also show two important results with implications for firms. First, by spending adequately on marketing, firms can increase the clumpiness of a customer with the hope of increasing their value. In the second result, the authors find that all customers are not equally clumpy. Women, young people and customers in loyalty programs appear to be more clumpy than others. This focus on customer clumpiness shows new insights into buying and improves on the traditional model of recency, frequency and monetary value.
The study authors are members of ISMS, the INFORMS Society for Marketing Science. ISMS is a group of scholars focused on describing, explaining and predicting market phenomena at the interface of firms and consumers.
Lee selected to National Preparedness & Response Science Board
Eva K. Lee, Ph.D., a professor in the School of Industrial and Systems Engineering at the Georgia Institute of Technology and an active member of INFORMS, was recently selected to serve a three-year term as one of the 13 members of the National Preparedness & Response Science Board (NPRSB), providing advice to the Office of the Assistant Secretary of Preparedness and Response, the U.S. Department of Health and Human Services (HHS) and to the White House.
While NPRSB members have traditionally been bio/medical experts or local/state emergency response leaders, Lee is the first with an O.R./math background. Lee’s work on biodefense and emergency medicine began in 2003 with the Centers for Disease Control (CDC) in which she focused on population protection against bioterrorism and pandemics. Her work in this domain has since been supported with funding from the CDC, the Defense Threat Reduction Agency (classified) and the National Science Foundation. In addition, Lee has served on several committees at the Institute of Medicine and assisted the White House in the Haiti earthquake response. Lee was on the ground in Fukushima for the radiological emergency response in Japan in the wake of the devastating 2011 earthquake and tsunami, and she was involved in the more recent efforts of assisting the battle against Ebola in West Africa. She has advised the White House Biodefense Policy Directors since 2007, spanning both the Bush and Obama administrations.
Lee and the other new members of the NPRSB were approved by HHS Secretary Sylvia Burwell and were sworn in as board members during a ceremony in Washington, D.C., in January.
Can sensors and analytics lower your home insurance premiums?
Few businesses’ profits and losses are determined by how good their prediction models are. The insurance business is one of the exceptions, an industry where analytics is not just a “nice to have” to improve performance, but a necessity. Claims forecasting, which drives premiums, are at the heart of what actuaries do. Given the critical nature of these models, insurance companies have been voracious customers of data, models, tools and processes.
New technologies and analytics drive newer forms of service. Take the car insurance industry, for example, where usage-based insurance models depend on the latest telematics technologies. In the usage model, car insurance coverage and service is based on data collected from the vehicle, including speed and time-of-day information, historic riskiness of the road and driving actions along with distance or time traveled. The tradeoff for consumers allowing themselves to be tracked is lower car insurance.
Can similar technologies and resulting analytics be applied to the home insurance market?
Traditionally, home insurance premiums depend on a number of variables such as what year the house was built, how many bedrooms it has, how far it is from the nearest fire station and dozens of other factors. Now imagine your home insurance company is able to install cheap sensors in your home to test your column and wall strength, how well you are looking after your house and other factors. Furthermore, they are able to take the minute-by-minute sensor data from your house and perform analytics on that information and comparing that data with your neighbor, street, town and state.
In that world, your insurance company can use the data to make you a better homeowner and give you “best practice” tips based on the data to reduce accidents and lower your claims and therefore your overall premium. A “fix it before it is broken” approach can reduce claims for a homeowner and in turn lower home insurance premiums.
However, these benefits would need to be balanced with consumers’ concerns of being tracked. The last thing that consumers want is a Big Brother Orwellian nightmare on their hands. Home insurance companies may need to take a step-by-step approach and address these concerns before utilizing advanced technologies and analytics to lower your home insurance.
Source: vHomeInsurance (http://www.vhomeinsurance.com)