MSBA Student Bennett Borden on How Data Science is Speeding Up Lawsuits

The following is a Legal Borden, BennettTalk Network interview with MSBA Class of 2015 student Bennett Borden. Bennett spoke with Legal Talk Network about data science advancements in the legal field and their impact on litigation procedures. Bennett referenced how descriptive analytics are entering the new area of predictive analytics, as companies review patterns from past legal infractions to learn about future prevention methods. Borden asks the question: how do companies get value out of the information they own?

Bennett Borden is the Chair of Information Governance at Drinker Biddle & Reath LLP.

 

 

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Faculty Publication: Anindya Ghose on Crowdfunding and Privacy

The following is an abstract of NYU Stern Professor Anindya Ghose’s recent forthcoming publication in Management Science: “The Hidden Cost of Accommodating Crowdfunder Privacy Preferences: A Randomized Field Experiment”

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In this paper, my co-authors and I examined online crowdfunding behavior related to user’s privacy controls. As you know, crowdfunding has received a great deal of attention from entrepreneurs and policymakers as a promising avenue to fostering entrepreneurship and innovation. A notable aspect of this shift from an offline to an online setting is that it brings increased visibility and traceability of transactions. Many crowdfunding platforms therefore provide mechanisms that enable a campaign contributor to conceal his or her identity or contribution amount from peers. So we studied the impact of these information (privacy) control mechanisms on crowdfunder behavior. Employing a randomized experiment at one of the world’s largest online crowdfunding platforms, we found evidence that offering users information control provides both positive (e.g., making users feel comfortable) and negative (e.g., priming users to have privacy concerns) causal effects. We found that reducing access to information controls induces a net increase in fundraising, yet this outcome results from two competing influences – an increase in willingness to engage with the platform (a 4.9% increase in the probability of contribution) and simultaneously a decrease in the average contribution (a $5.81 decline). We found that this decline derives from a publicity effect, wherein contributors respond to a lack of privacy by tempering extreme contributions. We thus were able to unravel the causal mechanisms that drive the results and discuss the implications of their findings for the design of online platforms.

You can read the full paper here.

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Inc.com Article Highlights MSBA Student for Data Science Contributions

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Inc.com’s article entitled “The Real Price of Fast Growth” highlights current MSBA student Brad Blanchard, noting his Data Science contributions to menswear retailer, J. Hilburn.

“J.Hilburn is strategizing for significant future growth. Not just the freewheeling startup kind of growth: This time, it will be deliberate. Among other things, the new data-scientist hire, Brad Blanchard, is helping to create a more personalized and predictive online shopping experience for men who browse the website without the help of their personal stylist. (Though more than 90 percent of sales are done through personal stylists, the company is pushing for increased online-sales growth.)”

Read the entire article here.

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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.

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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?”

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“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:

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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

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“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

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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.”

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“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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