MSBA Program Heads to Facebook for Talk on Social Signals

The NYU Stern MS in Business Analytics Program class of 2015 kicked off its second module of the program with a talk at the NYC Facebook offices last week. California-based Facebook data scientist Sean Taylor gave a talk on some of his Facebook studies that track social influence and social signals. Below you will find a description of his presentation.


Social Influence from Online Social Signals

Online social signals have the potential to change behaviors on a massive scale by transmitting information, altering preferences, or changing opinions. Since they leave perfect digital traces and often may be experimentally altered, we can study their effects – and the effects of social interactions in general – on an unprecedented scale and with causal interpretation. In this talk, Taylor describes three very large scale field experiments designed to measure the causal effects of different kinds of online social signals. 

Sean Taylor is a computational social scientist specializing in field experiments on web and mobile platforms. His research interests include causal inference techniques, social influence processes, information credibility, and evaluation of predictions. Sean received his Ph.D. in information systems at NYU’s Stern School of Business and holds a B.S. in economics from The Wharton School.

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Professor Arun Sundararajan on the Effectiveness of Behavior Modification Apps

The following is an excerpt from Fast Company’s article entitled: “Can Technology Really Change Your Habits?”


“If technology can provide the rewards needed to change your behavior, what happens to your behavior after youstop using the app or program?

Do Apps Work?

The answer comes down to the behavior you were originally trying to change, says Arun Sundararajan, a professor at NYU’s Stern School of Business whose research program focuses on how information technologies transform business and society.

According to Sundararajan, there are three kinds of behavioral changes.

  • The first includes changing behaviors that you learned through experience, such as the way you manage your time.
  • The second involves retraining your biomechanical system to behave differently, such as not pressing the breaks constantly while you’re driving.
  • The third has to do with physiological behaviors such as smoking and exercising.

The behaviors that have the highest chance of changing even after app usage are the second and third. Why? “Because they’re not changing you. They’re training you to do something differently, so once you’ve trained yourself, you can stop using [the app],” says Sundararajan. When it comes to learned behavior (the first one), there’s a greater chance you’ll revert back to your old behavior after using the app.”

If the app only changes your reaction to feedback, such as reprimanding you for checking your social media, then there’s a good chance you’re only changing your behavior because you’re using the app. When it comes to changing, Sundararajan says your best bet is to not put too much stock in the digital and technology.

“Over the last decade, we’ve started to overestimate the power of technology and we reduce the importance of things like community,” he says. “A big part of behavior change has to do with changing the environment that you’re in and changing the interactions that you have with people.””

Read the entire article here.

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Arun Sundararajan: Bringing New Opportunities to India’s 1.2 Billion Citizens

Professor Arun Sundararajan & MBA students Amy Nelson and Reva Gaur discuss a Stern Signature Project (SSP) with India’s Unique Identification (UID) initiative, which aims to provide identification to India’s 1.2 billion citizens. Watch below:

arun ssp



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Prof. JP Eggers on Losing Technology: Get Ahead by Betting Wrong

The following is a Harvard Business Review article by NYU Stern Professor JP Eggers

Get Ahead by Betting Wrong


“Companies that invest heavily in R&D are often torn between emerging technologies, wondering which will win in the market and is therefore the one to develop. (The classic example is VHS versus Betamax video recorders.) Conventional wisdom suggests that they pay dearly for getting it wrong. But my research shows that betting on a losing technology and then switching to the winner can position a company to come out ahead of competitors that were on the right track all along.

Much of my investigation centered on flat-panel computer displays. I examined company and product data for 55 firms from the 1980s through the 2000s. Initially, companies pursued either plasma screens or liquid crystal displays. LCDs turned out to be the right call, but several firms with an early focus on plasma, including IBM, ended up as the top LCD performers. Why? I believe that switching to a new technology often forces companies to rapidly ascend a steep learning curve, and they can then use their knowledge to beat competitors whose learning proceeded more slowly.

My study encompassed detailed data on 694 products, 30 years of financial data, thousands of industry patents and scientific publications, interviews with more than 25 longtime industry veterans, and dozens of internal strategic planning documents from one particular firm. It yields several insights for companies facing competing technologies…”

Read the entire article here.

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Register Now! Free Webinar with Anindya Ghose 8/19: Mobile and Wearable Computing: Using Data Analytics to Understand the Mobile Shopper

Please join Professor Anindya Ghose for his upcoming IIBA webinar entitled Mobile and Wearable Computing: Using Data Analytics to Understand the Mobile Shopper. 

Register here today!

When: Tuesday, August 19th, 11am EST


Abstract: The dramatic growth in mobile and wearable computing activities has led to a corresponding torrent of granular data that capture a wide variety of user behavior in the digital world. Whether mobile users are communicating with others, posting or consuming digital content, alerting others to their location, purchasing goods or services, redeeming coupons or reacting to ads, they are creating a “digital trail.” Compounding this data generation is the imminent adoption and usage of different kinds of wearable computing devices. Such digital trace data on human interaction and activity lends itself to deeper explanatory and predictive models in the realm of customer analytics. Anindya Ghose will discuss how to measure and quantify the value created from the use of apps and advertising on mobile devices and their integration with wearable computing devices, in the course of meeting some strategic goals and tactical issues for firms. A key goal is to gain a better understanding of the “mobile shopper” using tools from business analytics that combine statistical and econometric modeling with randomized experiments “in the wild”.  The webinar will cover several projects that Professor Ghose has undertaken in multiple countries in Asia and Europe as well as discuss the state of the art knowledge in this space.

3 learning points:
(i) Understanding audience behavior and advertising effectiveness on mobile devices
(ii) Understanding how consumers respond to geo targeting and geo fencing strategies using smart phone technologies.
(ii) Exploring the implications of wearable computing devices on marketing.

Bio: Anindya Ghose is a Professor of Information, Operations and Management Sciences and a Professor of Marketing at New York University’s Leonard N. Stern School of Business. He is the co-Director of the Center for Business Analytics at NYU Stern. He is the Robert L. & Dale Atkins Rosen Faculty Fellow and a Daniel P. Paduano Fellow of Business Ethics at NYU Stern. He has been a Visiting Associate Professor at the Wharton School of Business. He also serves as the main Scientific Advisor to 3TI . He was selected by Business Week as one of the “Top 40 Professors Under 40 Worldwide” and by Analytics Week as one the “Top 200 Thought Leaders in Big Data and Business Analytics”. His research analyzes the economic consequences of the Internet on industries and markets transformed by its shared technology infrastructure. He is an expert on product reviews, reputation and rating systems, sponsored search advertising, mobile commerce, mobile apps, mobile ads, crowdfunding, ecommerce, and online markets. He frequently works with and consults for leading firms in the information technology, retail, financial services, telecommunications, digital media, and travel industries on projects related to internet marketing, social media analytics, mobile marketing and digital advertising analytics. He also plays a senior advisory role to several start-ups in the Internet space. He has been interviewed and his research has been profiled numerous times in the BBC, Bloomberg TV, New York Times, Financial Times, Forbes, NBC, Xinhua, Time, LA Times, Reuters, Washington Post, New York Daily, National Public Radio, Wall Street Journal, MSNBC, CNBC, China Daily, Knowledge@Wharton, and elsewhere. He teaches courses on social media, digital marketing, business analytics and IT strategy at the undergraduate, MBA, EMBA, MSBA, and Executive Education level in various parts of the world including the US, India, and South Korea.

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Prof. Arun Sundararajan on Sharing Economy Start-ups

NYU Stern Professor Arun Sundararajan was quoted in TIME magazine’s article on entitled “Recycle, Reuse, Reprofit: Startups Try to Make Money Selling Your Stuff.”

DownloadedFile-2The following is an excerpt from TIME –

“‘People are seeking out human connection in our day-to-day economic transactions,’ says Arun Sundararajan, a business professor at New York University who studies these budding economies. ‘There is a noneconomic value that comes from giving your stuff to other people.’

Sundararajan says that if a company like Yerdle achieves its aim of displacing 25% of new sales, that’s good for the economy because it decreases waste. On the flip side, there is a possibility of job losses among people who make those new items. But he believes that other jobs in newer sectors would replace them, as happened when technological innovation put farmers out of work. “Efficiency is the name of the game in all of consumption,” says Ready-Campbell of Twice, “and in the whole economy, really.”

Read the entire article here.

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Anindya Ghose, Faculty Publication- “Tracking Mobile: The Rise of Smartphones in Marketing”

The following is an excerpt from NYU Stern MSBA Professor Anindya Ghose’s article in Insight, the Indian School of Business Magazine entitled “Tracking Mobile: The Rise of Smartphones in Marketing.”


“Mobile usage as a proportion of Internet traffic continues to skyrocket, but mobile advertising often gets the rap for being ineffective. In this article, Professor Anindya Ghose unravels the myths and paradoxes of mobile marketing through findings from a series of studies conducted in four different countries − the United States (US), Germany, China and South Korea. By conducting various randomised experiments and deploying sophisticated econometric models on historical data, brands can determine the right time, right place and right device to reach the right customer.

One of the most exciting developments in the digital media arena is the explosion of mobile phone data and the possibilities for analysing it. Brands, marketers and strategists have invested considerable time and money in trying to decipher such data and develop insights from it. Mobile data provides us the opportunity and the ability to ask a plethora of interesting and sometimes highly intuitive questions. This article presents a series of studies that were undertaken in four different countries − the US, Germany, China and South Korea. These studies helped us access detailed granular atomic datasets that enabled us to comprehend and identify actionable insights about what brands and marketers can do with mobile data.”

Read the entire article here. Learn more about the ISB Insight publication here.

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Professor Big Data

The Programmatic Mind recently featured an interview with NYU Stern Professor and MS in Business Analytics Faculty Member, Anindya Ghose (“Professor Big Data”). Professor Ghose spoke with Ben Plomion, VP of Marketing at Chango, to discuss use of data and consumer preference in the age of digital advertising.

Read the full article here: Professor Big Data

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Anindya Ghose to Co-Chair AIG-NYU $5.5 Million Collaborative Research Initiative

The following is a news item from NYU’s Center for Data Science:

Anindya Ghose, NYU Professor of Information, Operations and Management Sciences and Professor of Marketing, is co-Director of the Center for Business Analytics, a Daniel P. Paduano Fellow of Business Ethics, and the Robert L. & Dale Atkins Rosen Faculty Fellow, all at NYU’s Stern School of Business.

Additionally, Dr. Ghose is one of two co-chairs of the recently-created AIG-NYU Partnership on Innovation for Global Resilience. He represents NYU, while Dr. Siddhartha Dalal is the co-chair from AIG. Both lead the Partnership’s Advisory Committee, which will select for funding those projects proposed by NYU faculty which demonstrate the potential to have a transformational impact on the world at large. Funded by AIG, the $5.5 million initiative will award $1.1 million in grants annually for five years.

Dr. Ghose’s research, which analyzes the economic consequences of the Internet on industries and markets, has received numerous awards and grants, including the National Science Foundation CAREER Award in 2007, a $2.12 million NSF grant in 2009, a $2.9 million grant from the NSF’s IGERT program in 2010 and five awards from Google and Microsoft.

Before joining NYU Stern, Dr. Ghose held positions at GlaxoSmithKline, Hewlett-Packard and IBM. He received his doctoral and master’s degrees in Information Systems from Carnegie Mellon University, an M.B.A. in Finance, Marketing & Systems from the Indian Institute of Management, Calcutta, and a B. Tech (with honors) in Electronics & Instrumentation Engineering from the Regional Engineering College (NIT), Jalandhar, India.

Professor Ghose recently met with reporter ML Ball to discuss his current research, the AIG-NYU Partnership and his love of the Internet.

What brought you from India to America, and when?

I came here in the year 2000. Before that, I was working for IBM in a consulting group, and before that, I was with HCL-Hewlett-Packard. So I’ve always loved IT.

Coming out of business school after my M.B.A., I wanted a job in IT, not so much in programming but in consulting or analytics, that sort of space. I enjoyed the first couple of years in industry but then I started exploring other professions where I would have the independence to choose my projects, have flexibility with respect to time, and be able to work on deep intellectual questions that may have bigger ramifications than what I was doing at the time. So all of that pointed to academics. To be in a good university in the US, you have to go through the rigors of a Ph.D. program, so I got my doctorate from Carnegie Mellon in 2000 and then came here to NYU in 2004, ten years ago.

What made you choose NYU?

NYU’s reputation is stellar, especially in the business school space, and in the two areas I work in, marketing and IT, it is pretty much considered at the very top, if not the best. And New York was a big draw. I grew up in big cities for the most part, Bombay and Delhi and Calcutta. Plus, I knew some of the people. The synergy was quite explicit and I realized this was the place where I would be able to be most successful.

What interests you most about data science?

When I started doing my Ph.D., the one area that really fascinated me, and still fascinates me to no end, is the Internet. I have always been totally enamored by new phenomena that are cropping up on the Internet because of its underlying infrastructure: new business models, new forms of communication, new forms of collaboration. And every new phenomenon brings with it a swath of fascinating new data.

I’ve always tried to look for the next frontier. Today, “topic X” is the most cutting-edge recent phenomena, so I want to work on that. But I also want to work on, and anticipate, what would be the most cutting-edge thing in the next two years. I need to start working on that today because getting access to data and negotiating data with companies takes time, and if I wait until everybody recognizes something as “hot,” I might be behind the curve.

Have you been able to accurately anticipate trends?

I would have to say yes. A lot of people can see what’s coming. But it’s not just important to see it coming, you also should find it interesting. And somehow or other, I have not only been able to predict what was coming but also found it interesting enough to work on it, to collaborate with companies to get access to data and run field experiments with them.

What have your research predictions led to?

For business school academics, the single most important criteria in research accomplishments are journal publications. Specifically, premier journal publications that are rated top tier by every university in the world. So that’s our target. We also present at conferences, but those take a second seat.

Second in importance would be recognition, in the form of prestigious awards or grants, from NSF and the corporate world. My research has been funded extensively by Google and Microsoft, as well as by many other companies in industry.

I think what really motivates me increasingly these days is to work on a problem that is very real, where I can take the findings from my research and apply them in the companies that gave me the data in the first place.

Have your research results been applied to industry, affecting business outcomes?

Yes, that has happened extensively in the last four to five years. My work takes me overseas a lot, particularly to China and South Korea. My most recent work has been in the mobile computing space, in which China and Korea tend to be like crystal balls for the rest of the world. What happens over there today will happen in the U.S. about a year or two from now, and in Europe, a couple of years from now.

One of the most recent projects I’ve done where the research results were then directly applied was exploring only-channel symmetries in the world of digital advertising in South Korea. We were studying whether, when people get exposed to an advertisement by a brand in one channel, then also get exposed to the same ad or the same brand in a different channel, that increases the propensity to buy the product. Or does it actually reduce it because of the annoyance effect?

We learned that if people are shown an ad in a different format in a different channel, even though it’s the same message, our brains tend to process it differently, so we don’t face that wear-out effect. Rather, there is a reinforcement effect in our mind which actually increases our propensity to buy. This is something that companies in South Korea asked me to investigate. They wanted to know if there are synergies in the first place, and if so, can they be measured. So we ran a number of randomized field experiments to causally ascertain the synergies. We then shared our findings with the companies, which got very excited. They executed our study themselves and saw similar results.

Another study I did recently was with companies in Germany which were measuring the effectiveness of mobile coupons. You can now target people with real-time coupons based on their location. For instance, as I’m walking past the shopping mall with an iPhone, stores can sense that I’m walking past them and they can send me a coupon. Some firms are beginning to do this in the US; they are not yet as sophisticated as the folks in Korea or China but it’s only a matter of time.

In the German study, we looked at the effect on location on smartphone coupon redemption. We then went back to the companies and told them how they should run their experiments and change their prices. They did, and saw a lift in their redemption rates. So you can meaningfully design studies to not only get good research out of them but to also have an impact on industry.

Can you describe the AIG-NYU Partnership and your role in it.

I am the Chair from NYU, and Siddartha Dalal is the Chair from AIG. Obviously, AIG has been a leader in the insurance space for a number of years, but recently, they have set up a data science team. In the process, they have become very excited about collaborating with academics, especially asking top universities to help their own science team jointly figure out answers to problems of interest to them.

As they talked with NYU, they started plotting the idea of joint research collaborations on transformative projects that would be of interest to professors at NYU and the data science team at AIG. During those discussions, we determined that this would be a long term, five-year collaboration.

The initial discussions took place with Dr. Paul Horn, Senior Vice Provost for Research here at NYU. He really championed the whole thing and formed the steering committee across NYU disciplines. He also chose me to be the Chair from NYU; I’m very flattered and honored that I was chosen. I’m excited to work with a stellar group of advisory committee members, the Who’s Who of NYU. On the AIG-NYU team, we have nine people from NYU and a similar number from AIG. It’s been very enlightening to figure out how we can actually work on problems of direct relevance and applicability with a company like AIG. Right now, we’re in the process of requesting proposals, from NYU faculty but also advisory members. Soon, we will meet together and look at the proposals which have been submitted, and then hopefully select the majority of them.

I anticipate there will be a strong response to our call for proposals; the deadline is April 11th. There is already so much buzz about this, as it’s being circulated widely across NYU, and I would love to see a lot of people being funded.

The AIG funding is for five years. Will some of the projects last longer?

Yes, certainly. Some projects might show meaningful results in two years, but others might take three to five years, some maybe even more. So this is a long-term process. I won’t be surprised if after five years, AIG is happy with what we have accomplished and asks us to renew for another five years.

My plan is, after a few months, to organize a workshop/symposium, inviting those faculty whose proposals have been selected to present their most current state of research that has come out of the funding. We would probably do that twice a year; research projects take a long time, and twice a year would give us a sense of where they are in the early stages and where, subsequently, they will be a year from now.

The second step involves AIG figuring out which of those funded projects can be applied directly in their companies. Can they be monetized? Can the ideas be productized? Can they incorporate them into their current data science initiative?

What’s the benefit of the partnership to NYU?

This program is unprecedented. NYU has never had this sort of project, of this scale and scope: five million dollars over five years. It’s a big deal. Imagine the number of faculty whose research can be funded out of this. The National Science Foundation has dramatically cut down on funding for faculty, meaning that federal research funds are slowing down considerably.

Because of this, there are many, many faculty to whom even a small-size grant would make a huge difference for the potential of completing their research. That is why I personally would like to make this as inclusive as possible, rather than making it overly selective.

In essence, the fascinating research you can do with this data is limited only by your imagination. For us geeks, this is amazing. For me, it applies directly to the new science of cities, which is something I’m currently working on. I am very interested in figuring out how people in cities live. When do they wake up? When are they the most energetic? When do they party? We can now measure this because of available technologies, like smartphones. Every time your phone is with you, it’s streaming out a ton of data about where you are, what you’re doing. So by combining that with available technology data, you can literally map out and visualize how a city breathes, when it is most alive. That’s the kind of research I’d like to be doing in the next few years.

Learn more here.

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Arun Sundararajan on the Crowdfunding Market

The following is an excerpt from Bloomberg BusinessWeek article titled, “Crowdfunding Lures Investors Seeking Stock Over Goggles.”


Newer crowdfunding sites are giving investors the chance to earn something else: money. CircleUp Network Inc. offers equity in private companies, while Funding Circle Ltd. lets investors buy company debt. College graduates can turn to Social Finance Inc. to refinance loans with help from alumni.

The crowdfunding market is taking off, altering how capital gets deployed, as projects, companies and individuals flock to the Web for fundraising instead of tapping banks and big financial firms. Regulators are hopping on board — at a measured pace — loosening restrictions that to date have limited how companies raise money and who can be an investor.

“It’s going to be a big market and it’s going to change how a lot of small businesses finance themselves,” said Arun Sundararajan, a professor at New York University’s Leonard N. Stern School of Business.

In all, online crowdfunding jumped 89 percent to $5.1 billion last year, according to Massolution, a research firm. The appeal goes well beyond the U.S. In developing markets, where smartphones and high-speed Internet are gaining rapid adoption, crowdfunding may attract as much as $96 billion a year by 2025, with China representing about half that amount, according to a 2013 report from the World Bank.

Read the entire article here.

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