Decision Science News

JOURNAL OF INTERACTIVE MARKETING SPECIAL ISSUE ON SOCIAL MEDIA

CALL FOR PAPERS

Journal of Interactive Marketing Special Issue
Social Media: Issues and Challenges

Submission deadline: March 15, 2011

Special Issue Co-Editors

Donna L. Hoffman (donna.hoffman@ucr.edu) and Thomas P. Novak (tom.novak@ucr.edu)
University of California, Riverside

The Journal of Interactive Marketing announces a call for papers on topics related to the marketing issues and challenges presented by the explosive popularity of social media. We seek conceptual, analytical, empirical, and managerial papers, covering the newest and most innovative approaches to the study of this theme. The biggest buzz in the online world in the last few years has been about social media. Consumer usage of applications like Facebook and Twitter has skyrocketed, and marketing managers, increasingly desperate to reach coveted demographics that rarely read newspapers or watch television, are now seriously looking to social Web applications as an important vehicle for reaching their customers.

In keeping with its position as a thought leader and catalyst for shaping ideas and issues associated with electronic, interactive and direct marketing environments, the Journal of Interactive Marketing seeks to publish the most leading-edge academically rigorous ideas, methodologies and insights related to the marketing implications of social media.

Special Issue Topics

The special issue is seeking papers that define and address the social media challenges facing Internet marketers that include but are not limited to the following topics:
* User-generated content represents a wealth of behavioral data. What are the best ways to model and analyze these data for generalizable marketing insight?

* What is the ROI of social media?

* How do social media affect online and offline information seeking and shopping behavior?

* Why does some user-generated content “go viral?”

* Is brand-related user-generated content from different platforms (e.g. Twitter, Facebook, YouTube, and so on) consistent? Do some platforms lend themselves to systematic biases in how consumers discuss brands?

* How can managers integrate the content across multiple social media platforms to develop a consistent view of “consumer chatter” about their brands?

* Can we infer consumer characteristics such as personality, other chronic dispositions, purchase likelihood or other consumer response measures from user-generated content?

* What are the optimal strategies for responding to customer complaints in social media?

* Do social coupon applications – and more generally crowdsourcing applications- have a sustainable business model?

Interested authors should feel free to discuss the fit of their potential topic with the Special Issue editors, Donna Hoffman (donna.hoffman@ucr.edu) and Tom Novak (tom.novak@ucr.edu).

Timeline and Review Details

The deadline for submission of manuscripts is March 15, 2011. This deadline is firm. All manuscripts for the special issue will be reviewed according to the guidelines for the Journal of Interactive Marketing. The special issue will follow the same reviewing process as regular Journal of Interactive Marketing submissions. The special issue editors especially encourage the submission of shorter papers (25-30 pages inclusive).

Authors can expect decisions by June 30, 2011. Revised manuscripts will be due by August 31, 2011; the special issue is slated to appear as the first issue of 2012.

Submission Details

Manuscripts should be submitted electronically via the Journal of Interactive Marketing web site no later than March 15, 2011 according to the submission guidelines. Please indicate that your submission is for the Special Issue on Social Media.

Manuscript guidelines and paper submission: http://ees.elsevier.com/intmar

Please contact Barbara Hruska, Managing Editor, bhruska@directworks.org for submission questions.

About the Journal of Interactive Marketing

The Journal of Interactive Marketing is a peer-reviewed journal that caters to strong academic and practitioner audiences. Its readership includes direct and interactive marketers, advertisers and advertising agencies, market researchers, e-business executives, and researchers in marketing, strategy, customer behavior, managerial economics, statistics, and information technology. JIM is offered in more than 1,500 institutions and libraries in nearly 40 countries around the world and is indexed in the major bibliographic databases including the Social Science Citation Index, Business Source Premier, and ABI/Inform (FirstSearch).

The 2-year ISI Impact Factor for the Journal of Interactive Marketing is 2.60 and the 5-year Impact Factor is 4.02. These numbers offer strong confirmation that JIM is the leading journal in the area of direct and interactive marketing. The commonly used 2-year measure is the 6th highest score among marketing journals.

Photo credit: http://www.flickr.com/photos/webtreatsetc/4091128553/ & http://webtreats.mysitemyway.com

FASTER, CHEAPER, EASIER BEHAVIORAL RESEARCH ONLINE

One thing Decision Science News particularly enjoys about being at Yahoo! Research is the brilliant colleagues. This week, two of them, Winter Mason and Sid Suri, presented us with this manuscript which is a guide to conducting research on Amazon’s Mechanical Turk.

Manuscript? Manuscript from heaven, we say, for here at DSN we are often being asked the ins and outs of this technology and now have a guide to link to. Read it now before it gets published:

A Guide to Conducting Behavioral Research on Amazon’s Mechanical Turk

See Decision Science News’ earlier posts on MT:

• Amazon’s Mechanical Turk meets classic JDM experiments
• How to run experiments on Mechanical Turk

Photo credit: The R language for statistical computing

WHAT MAKES JDM DISTINCT?

A friend of Decision Science News, who is co-organizing a session on JDM (judgment and decision making research) for students, recently emailed a handful of JDM researchers:

Those of us in the JDM session are doing quite different research and couldn’t really see how we were more “JDM” than, say, someone doing “cognition”, which lead us to the question “What is JDM?”

If you have a few minutes in the next couple days to just shoot me a note about what makes JDM distinct, I’d really appreciate your thoughts. My goal is to give students a couple different (anonymous, of course) opinions about what JDM is from people more senior than those of us in the session.

Here is the opinion that Decision Science News gave:

This post from Decision Science News, based on a text analysis of conference programs, gives some insight into how what is currently being done in JDM is distinct from Social Psych

http://www.decisionsciencenews.com/2010/02/15/the-difference-between-spsp-and-sjdm/

Also, the first list does a pretty good job of showing the core topics of JDM: risk, uncertainty, choice, decision, probability, prediction, future, intertemporal choice. Missing from the list would be: heuristics, utility, forecasting, normative models, prescriptive models, and descriptive models.

The Society for Judgment and Decision Making (SJDM) was formally formed in 1986 (from a core who had been meeting less formally before that) and I’ve heard it was basically people interested in the exciting field of research opened up by Tversky & Kahneman. Their 1974 Science article still touches upon much of what is done today.

The oldest President’s letter to be found online, written by Barbara Mellers in 1996, speaks of “almost five decades” of JDM research, which would point to somewhere in the late 1940s. Well after Brunswik, a few years after Von Neumann and Morgenstern’s “Theory Games and Economic Behavior” and a few year’s before Ward Edward’s Psychological Bulletin article “The theory of decision making”, the abstract of which is (emphasis added):

This literature review of decision making (how people make choices among desirable alternatives), culled from the disciplines of psychology, economics, and mathematics, covers the theory of riskless choices, the application of the theory of riskless choices to welfare economics, the theory of risky choices, transitivity of choices, and the theory of games and statistical decision functions. The theories surveyed assume rational behavior: individuals have transitive preferences (“… if A is preferred to B, and B is preferred to C, then A is preferred to C.”), choosing from among alternatives in order to “… maximize utility or expected utility.”

And Meller’s President’s letter (emphasis added) describes what she saw as the big topics (in addition to her opinions about the focus, which we won’t touch upon here):

For almost five decades, researchers in judgment and decision making have explored human errors in judgment and choice. We have documented instances in which people violate fundamental principles and axioms. We have discovered cases in which people disobey the most basic rules of statistics, probability, and logic. We have identified factors that should be irrelevant, but aren’t, such as the response mode, the problem representation, and the decision frame.

What are the legacies of this research? We have probed the boundaries of human rationality. We have discovered important limitations of cognitive processing, and we understand how poor judgment makes people their own worst enemies. But somewhere along the way, we lost sight of everything else.

While walking across campus to a colloquium one afternoon, a colleague asked me whether the speaker was a member of the JDM Society. When I told him “yes,” he said, “Then give me a quick preview. What is the error of the day?” He was perfectly serious. We are well known for setting traps and taking delight at human failure.

Haven’t we reached the point of diminishing returns? Demonstrations of one more error for the sake of an error, or one more violation for the sake of a violation, are nothing new. Not only are they not new, they add to an already lopsided view of human competence. We need theories of decision making that predict not only errors, biases, and violations of axioms, but also broader themes of psychological and social functioning. We know very little about the effects of emotions on choice. We know very little about the relationships between decision making and signal detection, memory retrieval, or categorization. Not only that, we know very little about the impact of social context. Why are certain errors, and not others, attenuated in experimental markets, and possibly other institutional settings?

One of the reasons we may have become so preoccupied with errors is because we applied to our descriptive theories the organizing principles from our normative theories. In normative theories, we classify decisions depending on the assignment of probabilities to states of nature (decision making under certainty, risk, uncertainty, or conflict), and these categories may not be optimal for descriptive theorizing. In the animal literature, decisions are often classified on the basis of the animal’s activities, such as foraging and mating. Perhaps functional distinctions might be appropriate in the human literature as well. How often have you heard complaints that our theories apply to purchasing decisions, but not decisions about marriage or children? How often have you heard complaints that our theories of gambles don’t generalize to medical treatments, job opportunities, or even vacation sites? Perhaps the missing links in our descriptive theories would become more apparent with a different set of organizing principles that highlight our activities, goals, and desires.

We have gotten a great deal of mileage out of errors. Decision making is discussed in many psychology texts. It is also cited in marketing, organizational behavior, political science, and microeconomics texts. Philosophers, economists, and statisticians are also developing richer and more interesting definitions of rationality. Finally, psychologists have begun to study human strengths as well as human weakness, and this work should have important consequences for artificial intelligence systems designed to complement and aid human decision making.

To have a lasting impact, we should continue to go beyond errors, mistakes, and other human failures and adopt a broader perspective. As John Locke said, “It is one thing to show a man that he is in error, and another to put him in possession of the truth.”

The point is, for better or for worse, the majority of JDM research has always been about the difference between formalisms and human behavior. The formalisms are drawn from economics, mathematics, and psychology as Edwards said, and I’d guess that the following list of formal models (with examples of JDM research areas in parens) is close to complete:

• probability (base-rate neglect / conservatism, confidence),
• logic (Wason selection task),
• subjective expected utility (Prospect Theory, Support Theory),
• choice axioms (Independence of irrelevant alternatives, attraction / compromise effects)
• statistics
  • sampling (Representativeness, law of small numbers, probability weighting, decisions from experience)
  • inference (lens model, fast&frugal heuristics)
  • estimation (Availability, Anchoring, risk perception)

Outside of this, there is a bit of descriptive work (Naturalistic DM, individual differences) and a bit of prescriptive work, though the latter is usually taken up in the field known as Decision Analysis. Like Mellers quite a few JDM researchers have not been happy with the organization around axiomatic norms, but if we are to define JDM by what it is has primarily been in the past, this generalization is hard to deny.

Since Meller’s letter, attention has moved from documenting differences to building more formal models of what people do, with Prospect Theory being the field’s most successful export.

As to the differences with Social Psychology, I think the blog post above addresses the differences in current practice.

As to the differences with Cognitive Psychology, Barsalou’s textbook puts JDM as a field within Cognitive Psychology and I think this is right: judging, choosing, and deciding are thought processes. Cognitive Psych is defined as covering perception, memory, thinking, language, and problem solving. Barsalou’s chapters are: categorization, representation, executive control, working memory, long-term memory, knowledge, language structure, language process, and thought. JDM typically falls under “thinking” / “thought”.

If forced to choose two books that represent what the field is about, I’d go with:

• Rational Choice In An Uncertain World, by Reid Hastie and Robyn M. Dawes
• Judgment under Uncertainty: Heuristics and Biases, edited by Daniel Kahneman, Paul Slovic, Amos Tversky

Photo credit:http://www.flickr.com/photos/captkodak/272746539/

TYPES OF DEFAULTS AND HOW TO SET THEM

Defaults are settings or choices that apply to individuals who do not take active steps to change them (Brown & Krishna, 2004). Collections of default settings, or “default configurations” determine the way products, services, or policies are initially encountered by consumers, while “reuse defaults” come into play with subsequent uses of a product. At the finest level, a single question can have “choice option default”, which on electronic forms can take the shape of a pre-checked box (Johnson, Bellman, and Lohse, 2002).

Defaults have been shown to have strong effects on real-world choices in domains including investment (Cronqvist & Thaler, 2004; Madrian & Shea, 2001), insurance (Johnson et al, 2003), organ donation (Johnson & Goldstein, 2004), marketing (Goldstein et al, 2008) and beyond.

They have a wide appeal among marketers and policy makers in that they guide choice while at the same time preserving freedom to choose. They are often regarded as the prototypical instruments of libertarian paternalism (Sunstein & Thaler, 2003).

Through default-setting policies, choice architects exhibit influence over resulting choices. The palette of policies includes simple defaults (choosing one default for all audiences), random defaults (assigning a configuration at random, for instance, as an experiment), forced choice (withholding the product or service by default, and releasing it only after an active choice is made), and sensory defaults (those that change according to what can be inferred about the user, for example, web sites that change language based on the visitor’s IP address).

Products and services that are re-used can also avail themselves of persistent or reverting defaults (which, respectively, remember or forget the last changes made to the default configuration) and predictive defaults (which intelligently alter reuse defaults based on observation of the user).

Those setting defaults should be aware of the ethical risks involved (Smith, Goldstein & Johnson, 2010). The ethical acceptability of using a default to guide choice has much to do with the reason why the default has an effect in the first place. When consumers are aware that defaults may be recommendations in some cases and manipulation attempts in other cases (Brown & Krishna), they exhibit a level of “marketplace metacognition” that suggests they retain autonomy and freedom of choice. However, if defaults are effective because consumers are not aware that they have choices, or because the transaction costs of changing from the default are too high, defaults impinge upon consumer autonomy. An often prudent policy, though not a cure-all, is to set the default to the alternative most people prefer when making an active choice, without time pressure, in the absence of any default. Running an experiment on a sample of the greater population can determine these preferences, and can be done in little time and at a low cost in the age of Internet experimentation (Gosling & Johnson, 2010).

REFERENCES

Brown, Christina L. and Aradhna Krishna (2004), “The Skeptical Shopper: A Metacognitive Account for the Effects of Default Options on Choice,” Journal of Consumer Research, 31 (3), 529-539.
Cronqvist, Henrik and Richard H. Thaler (2004), “Design Choices in Privatized Social Security Systems: Learning from the Swedish Experience,” American Economic Review, 94 (2), 424-428.
Goldstein, Daniel G., Eric J. Johnson, Andreas Herrman, and Mark Heitmann (2008), “Nudge Your Customers Toward Better Choices,” Harvard Business Review, December, 99-105.
Gosling, Samuel D. and John A. Johnson (2010), Advanced methods for conducting online behavioral research. Washington, DC: American Psychological Association.
Johnson, Eric J., Steven Bellman, and Gerald L. Lohse (2002), “Defaults, Framing, and Privacy: Why Opting In Is Not Equal To Opting Out,” Marketing Letters, 13 (1), 5–15.
Johnson, Eric J. and Daniel G. Goldstein (2003), “Do Defaults Save Lives?” Science, 302, 1338-1339.
Johnson, Eric J., John Hershey, Jacqueline Meszaros, and Howard Kunreuther (1993), “Framing, Probability Distortions, and Insurance Decisions,” Journal of Risk and Uncertainty, 7, 35-53.
Madrian, Brigitte C. and Dennis F. Shea, D. F. (2001), “The Power of Suggestion: Inertia in 401(k) Participation and Savings Behavior,” Quarterly Journal of Economics, 116 (4), 1149-1187.
Thaler, Richard, Daniel Kahneman and Jack L. Knetsch (1992), “The Endowment Effect, Loss Aversion and Status Quo Bias,” in Richard Thaler, The Winner’s Curse, Princeton: Princeton University Press, 63-78.
Samuelson, William and Richard Zeckhauser (1988), “Status Quo Bias in Decision Making,” Journal of Risk and Uncertainty, 1 (1), 7-59.
Smith, N. Craig, Daniel G. Goldstein, and Eric J. Johnson (2010). Choice without Awareness: Ethical and Policy Implications of Defaults. Working paper.
Sunstein, Cass R. and Richard H. Thaler (2003), “Libertarian Paternalism Is Not an Oxymoron,” The University of Chicago Law Review, 70 (4), 1159-1202.

ONE POST, TWO JOBS

The YALE SCHOOL OF MANAGEMENT is seeking additional faculty members at all levels in the areas of economics and organizational behavior. Ph.D. or equivalent is required; research and teaching interest in theory and application preferred, as well as an interdisciplinary orientation. Appointments will be made for the 2011 – 2012 academic year.

To apply online click here.

Please note that only electronic applications are accepted this year.

The deadlines for receipt of all materials is October 15, 2010.

For more information visit: http://mba.yale.edu/faculty/faculty_openings.shtml

Yale is an equal opportunity, affirmative action employer and especially encourages applications from women and members of minority groups.

————

THE DEPARTMENT OF SOCIAL AND DECISION SCIENCES AT CARNEGIE MELLON UNIVERSITY seeks candidates to fill a junior tenure-track position in decision making and public policy.

Candidates should have a strong commitment both to applying decision-making research to public policy and to creating the scientific foundations for such applications. Their letter of application should describe a research program designed to influence public policy and contribute to basic knowledge. Although policy interests could be in any area, the department has strengths in environment, energy, health, safety, finance, national security, and risk. Teaching would support the department’s educational programs.

The department is interdisciplinary, with faculty members from psychology, economics, political science, decision science, and history. Several have joint appointments in other departments, notably Engineering and Public Policy. Collaboration is a hallmark of the Department and University.

For more information, please visit: http://www.hss.cmu.edu/departments/sds/

Applicants should send a CV, two papers, three letters of recommendation, and a statement of research interests to:

Chair, Behavioral Decision Research and Policy Search Committee
Carnegie Mellon University
Department of Social and Decision Sciences
Pittsburgh, PA 15213-3890.

Please submit applications by December 1.

Carnegie Mellon University is an Affirmative Action/ Equal Opportunity employer. We encourage minorities, women, and individuals with disabilities to apply.

CMU Baker & Porter Halls photo credit: http://www.flickr.com/photos/aschultz/3254899110/

HOMOPHILY + MAPS WITHOUT MAPPING SOFTWARE

In the past, Decision Science News has posted about homophily (“birds of a feather shop together“) and cool, lightweight visualizations (“maps without map packages in R“). Today, both topics come together in Eric Fischer’s fascinating set of images on Flickr called “Race and Ethnicity”(*).  According to Eric:

Red is White, Blue is Black, Green is Asian, Orange is Hispanic, Gray is Other, and each dot is 25 people. Data from Census 2000.

Your Editor had just thought this was just an interesting visualization, but a few seconds after emailing it to Jake and Sharad, the latter called out “homophily!” from a few desks away, making a sensible tie-in to our paper. To refresh the collective memory:

Homophily is the idea that people who are in contact with one another tend to be similar in a number of dimensions.

Here we guess a less friendly term for it would be segregation, though that word is often used with some sense of causality (e.g. a dictionary speaks of “enforced” or “voluntary” segregation), whereas homophily is plainly correlational.

One funny thing about this representation is that it can make things look exaggerated when population density is high. For instance, on the Upper East Side of Manhattan (just to the right of the sharp rectangle that is Central Park, a third of the way down from the top) it looks like everybody is white. However, there are places that are just as white, but seem less so because of lower population density (e.g., the bit of New Jersey on the upper left). The white space makes things seem less, well, white. Also, if you zoom in on the Upper East Side, as below, one can see it is not pure red:

Now for another former home of Decision Science News: Chicago. The little red blob on the coast about two-thirds of the way down is the University of Chicago / Hyde Park. Your editor remembers being a student and making regular 20-minute drives to the orange blob due West of campus to get burritos at the original Maravillas.

(*) Well, it is not exactly without mapping software, but the background image adds little.

H/T Eric Fischer. I found out about these pics from Mike Arauz. I also just learned that Andrew Gelman has blogged about this, too http://www.stat.columbia.edu/~cook/movabletype/archives/2010/09/how_segregated.html

THE POTENTIAL FOR A BOND BUBBLE

Small investors have been a lot of fun to watch for quite some time now. In the 1930s, doing the opposite of the small investors (the so-called “odd lot” crowd because they could not afford to trade in amounts as large as standard units) was a popular contrarian strategy. The basic idea is that the small fries will always be wrong so that one can make money by doing the opposite.

In the 1990s, Barber and Odean excavated a goldmine of a data set of 60,000 individual investor accounts which revealed, among other things, that the more frequently individuals trade, the worse they do (see Figure 1).

And in today’s gloomy economy, investors are fleeing the stock market and moving into bonds. Check out this article from the New York Times: In Striking Shift, Small Investors Flee Stock Market. Don’t miss that prodigious spike in the bond inflows at the bottom. Also note the inaccurate language about “the relative safety of bonds.” Bonds offer no safety against regret.

Shlomo Benartzi speaks of a bond bubble, and also suspects that people are probably overestimating the best that long-term bonds could do in the best possible scenario (for bonds). More to come on this.

SUGGESTED READING:

http://finance.yahoo.com/banking-budgeting/article/110706/bond-risks-and-how-to-beat-them

Photo credit: www.nytimes.com/2010/08/22/business/22invest.html

DEPARTMENT OF OPERATIONS AND INFORMATION MANAGEMENT PROFESSORSHIP

The Operations and Information Management Department at the Wharton School is home to faculty with a diverse set of interests in decision-making, information technology, information-based strategy, operations management, and operations research. We are seeking applicants for a full-time, tenure-track faculty position. Applicants must have a Ph.D. (expected completion by June 30, 2012 is acceptable) from an accredited institution and have an outstanding research record or potential in the OPIM Department’s areas of research. Candidates with interests in multiple fields are encouraged to apply. The appointment is expected to begin July 1, 2011 and the rank is open.
More information about the Department is available at: http://opimweb.wharton.upenn.edu/
Interested individuals should complete and submit an online application via our secure website, and must include:

•A cover letter (indicating the areas for which you wish to be considered)
•Curriculum vitae
•Names of three recommenders, including email addresses [junior-level candidates]
•Sample publications and abstracts
•Teaching summary information, if applicable (courses taught, enrollment and evaluations)
To apply please visit our web site: http://opim.wharton.upenn.edu/home/recruiting.html
Further materials, including (additional) papers and letters of recommendation, will be requested as needed.
To ensure full consideration, materials should be received by November 12th, 2010, but applications will continue to be reviewed until the position is filled.

Contact:
Professor Karl Ulrich
The Wharton School
University of Pennsylvania
3730 Walnut Street
500 Jon M. Huntsman Hall
Philadelphia, PA 19104-6340

The University of Pennsylvania values diversity and seeks talented students, faculty and staff from diverse backgrounds. The University of Pennsylvania is an equal opportunity, affirmative action employer. Women, minority candidates, veterans and individuals with disabilities are strongly encouraged to apply.

PREDICTING CONSUMER BEHAVIOR FROM SOCIAL NETWORKS

This week, Decision Science News is doing a special cross-posting with Messy Matters. The post below is by Sharad Goel and describes work that he and your Decision Science News editor Dan Goldstein are jointly undertaking at Yahoo!

Do you know what the #$*! your social media strategy is? Perhaps it’s “to facilitate audience conversations and drive engagement with social currency”? Or maybe, “to amplify word of mouth by motivating influencers”? Well, given all the lies and damned lies being told about social, fellow yahoo Dan Goldstein and I decided to enter the fray with statistics. We measured the extent to which your friends’ behavior predicts your own, and found that in several consumer domains the effect is substantial, complementing traditional demographic and behavioral predictors.

That friends are similar along a variety of dimensions is a long-observed empirical regularity—a pattern sociologists call homophily. As McPherson et al. write in their canonical review on the subject, “homophily limits people’s social worlds in a way that has powerful implications for the information they receive, the attitudes they form, and the interactions they experience.” Turning this statement around, where there is homophily, one can in principle predict an individual’s behavior based on the attributes and actions of his or her associates.

To assess the quality of such network-based predictions, we merged a large social network (based on email and IM exchanges) with offline sales data at an upscale, national department store chain. Thus, for each of over one million users, we had their past purchase amounts in dollars, and had the same information for each of their network contacts. Think about this for a minute: we not only know how much these individuals themselves spent at an offline retailer, but also how much their social contacts spent, a testament to how profoundly the Internet is changing the way we study human behavior. (Despite bolstering social science research, these newfound tools raise serious privacy issues. We left the matching to a third party that specializes in doing this securely, so neither we nor the department store had access to the other’s complete customer database.)

The plot below summarizes our findings. First, as indicated by the top line, consumers whose friends spent a lot, also spent a lot themselves, consistent with the hypothesis that homophily extends to consumer behavior. When friends (alters) on average spent $400 during the six-month observation period, the consumer herself (ego) spent nearly $600, more than twice the typical consumer (indicated by the dotted line). As our aim is prediction, however, the relevant question is not just whether friends are similar in their purchasing behavior, but rather how much information is conveyed by social ties relative to other attributes. One might conjecture that ties simply indicate demographic (i.e., age and sex) similarity, that those who spend a lot are more likely to be middle-aged women—the primary market segment for this department store—and that friends of middle-aged women tend also to be middle-aged women. To test this hypothesis, we first paired each individual with a randomly chosen consumer of identical age and sex. The bottom line shows that this demographically matched group is, perhaps surprisingly, pretty ordinary. In other words, looking only at age and sex, you can’t identify consumers whose friends spend a lot (and who we know spend a lot themselves).

Though it’s standard marketing practice to target consumers based on their demographics, it’s an admittedly noisy profiling technique. So, to put social through the wringer, we next took the “socially select” group—consumers whose friends spent a lot—and matched them to random consumers with identical age, sex, and past purchase amounts. Each social candidate, that is, was matched to a consumer not only of the same age and sex, but one who spent approximately the same amount as the social candidate during the previous six months. Even relative to this formidable baseline, social cues still provide considerable information. As the middle line indicates, knowing a consumer’s age, sex and past purchases, but not that their friends are shopaholics, one would still underestimate their future sales.^[1]

We repeated this analysis for two other domains—examining signups for Yahoo! Fantasy Football, and clicks on ten online banner ads for movies, apparel, government programs, and beyond—again finding that the predictive power of social persists even after adjusting for age, sex, and past behavior. Lest you run off to rejigger your social strategy, we should mention a couple of caveats. First, we have shown that consumers with big-spending friends tend to spend a lot—more, in fact, than demographics and past purchases alone would suggest. But since most people, even premium customers, don’t have shopaholic friends, social cues do not substantially boost average predictive performance. Second, though social signals help predict how much consumers spend, they don’t always help identify which consumers will spend the most. Those who recently spent fifty grand on sartorial elegance are likely to be habitual top spenders, regardless of what you know about their friends.

Assessing the value of social, as with most things, is a messy affair. On the one hand, network ties convey information not captured by the usual egocentric metrics, a conclusion that at the very least we find scientifically interesting. On the other hand, it’s not immediately obvious how to use that knowledge to take over the world. Well, rest assured that an army of social strategy gurus are waiting in the wings with a game-changing, technology-disrupting way to, you know, “leverage the social graph to deliver personalized experiences” or something.

N.B.Thanks to Randall Lewis and David Reiley for acquiring the sales data, Jake Hofman for assembling the email data, and Duncan Watts and Dan Reeves for comments. For related work in the telecom domain, check out the paper, “Network-Based Marketing: Identifying Likely Adopters via Consumer Networks,” by Shawndra Hill, Foster Provost, and Chris Volinsky.

Illustration by Kelly Savage

Footnotes

[1] It’s perhaps tempting to conclude from these results that shopping is contagious (i.e., to assert causation where only correlation has been shown). Though there is probably some truth to that claim, establishing such is neither our objective nor justified from our analysis.

DSN OF THE WEEK

In response to last week’s post, Mike DeKay sent in this paper, which PNAS is good enough to let you down load for free.

CITATION
Attari, S. Z., DeKay, M. L., Davidson, C. I., & Bruine de Bruin, W. (in press). Public perceptions of energy consumption and savings. Proceedings of the National Academy of Sciences of the United States of America.

ABSTRACT
In a national online survey, 505 participants reported their perceptions of energy consumption and savings for a variety of household, transportation, and recycling activities. When asked for the most effective strategy they could implement to conserve energy, most participants mentioned curtailment (e.g., turning off lights, driving less) rather than effciency improvements (e.g., installing more effcient light bulbs and appliances), in contrast to experts’ recommendations. For a sample of 15 activities, participants underestimated energy use and savings by a factor of 2.8 on average, with small overestimates for low-energy activities and large underestimates for high-energy activities. Additional estimation and ranking tasks also yielded relatively flat functions for perceived energy use and savings. Across several tasks, participants with higher numeracy scores and stronger proenvironmental attitudes hadmore accurate perceptions. The serious defciencies highlighted by these results suggest that well-designed efforts to improve the public’s understanding of energy use and savings could pay large dividends.

For press coverage, see The New York Times, USA Today, Newsweek, The Economist, National Geographic, and Pocket Science on YouTube, among others.

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Peter McGraw, who is a big (in the sense of “notable” and in the sense of “six foot five inches tall” ) Decision Making researcher has launched a new

• Blog
• Website

There’s a nice profile of the man here: What makes us laugh? Professor Peter McGraw thinks he’s found the answer to one of humanity’s greatest questions

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Here is a cool paper documenting an amusing sort of less-is-more effect in which professionals do worse than laypeople in a crime-solving task. In addition, learning valid information decreases people’s accuracy. That said, logisitic regression beats ’em all, which doesn’t fit the less-is-more theme, but then again, logistic regression is less than human.

CITATION
Bennell, C; Bloomfield, S; Snook, B; Taylor, P; Barnes, C. (2010). Linkage analysis in cases of serial burglary: comparing the performance of university students, police professionals, and a logistic regression model. Psychology, Crime and Law 16 (6), 507-524.

ABSTRACT
University students, police professionals, and a logistic regression model were provided with information on 38 pairs of burglaries, 20% of which were committed by the same offender, in order to examine their ability to accurately identify linked serial burglaries. For each offense pair, the information included: (1) the offense locations as points on a map, (2) the distance (in km) between the two offenses, (3) entry methods, (4) target characteristics, and (5) property stolen. Half of the participants received training informing them that the likelihood of two offenses being committed by the same offender increases as the distance between the offenses decreases. Results showed that students outperformed police professionals, that training increased decision accuracy, and that the logistic regression model achieved the highest rate of success. Potential explanations for these results are presented, focusing primarily on the participants’ use of offense information, and their implications are discussed.

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Finally, Isaac Dinner and I are working on a thought piece that applies our research on defaults to the question of energy conservation. It’s called:

• Goldstein, D. G. & Dinner, I. M. A fairly mechanical method for policy innovation. Working paper.

We may add something about “reducing carbon emissions” to the title. We welcome feedback in the next week.