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MIT CDB Assistant Professor, Renee Gosline, Describes Digital Marketing's Newest Influencers

MIT CDB Video: Sinan Aral on Social Commerce

At the recent MIT CDB conference on Big Data, professor and social networking expert, Sinan Aral, discusses peer influence and how it can impact product marketing strategies and the online economy.

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Online media and social-advertising necessitate new ways to measure and drive data-based decision-making among customers. They are also creating a new field of experimental learning techniques and tools that are replacing classic, randomized market testing practices in many cases.

Dean Eckles, a social scientist, statistician and assistant professor in the MIT Sloan School of Management, explained how tools for designing, deploying and analyzing online field experiments can encourage good statistical and methodological practices as well as better understanding of online customer behavior. As MIT Sloan professor Glen Urban and IDE researcher, Sinan Aral, and others, have discussed, different types of ads and messaging are being tested all the time to determine what motivates online marketing and social activities.


Eckles, a former member of the Core Data Science team at Facebook who also worked at Yahoo, knows first-hand that “the Internet industry has distinct advantages in how organizations can use data to make decisions. Firms can cheaply introduce numerous variations on the service and observe how a large random sample responds when randomly assigned to these variations.”


At the same time, he told a recent MIT IDE seminar, “rapid, iterative, and organizationally distributed experimentation” also introduces important challenges-- such as understanding the effects of a change intervention.


Challenges arise because many experiments are being run -- often by different teams -- requiring tools to support rapid experimentation. For example, "How can multiple different teams experiment with the design of a single page at the same time?"


PlanOut: An App for Experimental Design

Facebook was seeking alternatives to standard A/B tests to answer questions like these about its users. A/B tests work well when minor tweaks to a system are needed, but not when more complicated or nuanced change has to be measured. Eckles and his team team built an open-source app called PlanOut, a language for describing complex experimental designs for behavioral science experiments. It uses standard code script to assign value to specific procedures and also can help manage multiple testing that takes place simultaneously on a site. Less technical users can program it via a GUI.


Eckles is interested in other applications for these types of tools and analytics that might, for example, show the best way to motivate voting or civic participation.

Feedback is important on social media content, he said, and experiments can focus on straightforward items such as comment boxes to measure user engagement or they can analyze more subtle factors such as how comments affect users and influence others.



For more on Eckles’ research on PlanOut see his 2014 paper here and a list of work here.

When machine “workers” are on 24 x 7 shifts, how can humans compete? When autonomous drones can achieve tasks without human intervention, what are our moral responsibilities?


In the rush to bring newer, smarter and more capable technologies to market, few are addressing the ethical and moral dilemmas that automation has raised. Psychology professor Joshua Greene, Director of the Moral Cognition Lab at Harvard University, however, is starting to relate his research about the brain and human morality to the world of IT and robotics.


At a February 18 seminar hosted by the MIT IDE, Greene noted that until recently, he didn’t fully make the connection between his own work and the long-term issues of Artificial Intelligence (AI). That intersection becomes very clear, however, when you think about the real-world issues of job displacement, how machines are programmed and what they are instructed to do. (More about Automated Ethics can be found here and here.)



The idea of machine intelligence displacing human labor--as discussed in MIT’s Erik Brynolfsson and Andrew McAfee’s book, The Second Machine Age--is no longer science fiction; “it’s not crazy,” Greene said.


Drawing on insights from his 2013 book, Moral Tribes: Emotion, Reason, and the Gap Between Us and Them, Greene explained that we react most strongly to harmful actions like punching someone in the face, where the harm is caused intentionally and directly, and the victim is an identifiable person. The social and moral challenges posed by advancing AI are different. If advanced AI puts millions of people out of work it won’t feel like intentionally punching someone—or a million people. The harm will be caused as an indirect side effect of doing something good. And those affected will be “statistical” people rather than identified individuals. It’s this mismatch between our moral psychology and the consequences at stake that makes modern moral problems so challenging.


Greene believes more focus is needed on critical problems like whether--and how—moral sensibilities can be programmed into autonomous machines such as military drones and self-driving cars. On a larger scale, societies have to re-imagine the world as one in which machines do more and more of the work currently done by humans. Technological advances may soon outpace our own moral sensibilities, according to Greene. “We’ll need to find new solutions.”





Joshua D. Greene is Professor of Psychology, a member of the Center for Brain Science faculty, and the director of the Moral Cognition Lab at Harvard University. He studies the psychology and neuroscience of morality, focusing on the interplay between emotion and reasoning in moral decision-making. His broader interests cluster around the intersection of philosophy, psychology, and neuroscience. He is the author of Moral Tribes: Emotion, Reason, and the Gap Between Us and Them.

Exciting big data possibilities – as well as business intelligence and business analytics possibilities -- are all well and good, but what businesses really want is to deliver value from the massive amounts of data they have amassed over time. And most agree that the best way to demonstrate that value is to monetize it. But what exactly does that mean, and how can it be achieved?


These are among the key questions that Professor Barbara Wixom is attempting to address in her current research. Wixom joined MIT Sloan in June 2013 as a Principal Research Scientist for the Center for Information Systems Research (CISR). At a recent MIT IDE/CDB seminar describing her preliminary work, Show Me the Money: Delivering Business Value through Data, Wixom noted that: “In a digital economy, data, and the information it produces, is one of a company's most important assets. Increasingly, companies are monetizing their data assets and generating business value via existing core products and services or new digitized ones.”BarbaraWixom.jpg


For Wixom’s current study, she interviewed more than 50 business leaders involved in data monetization efforts and discovered that definitions of data monetization varied widely – ranging from selling data products and services for revenue generation, to exploiting data internally to drive tangible bottom-line results. When a company explores data monetization with the latter intent in mind, Wixom noted that data providers are good companies to use as role models. Because data monetization is at the core of their business models, data providers have learned over the years how to be really good at monetizing.


Provider, non-Provider Examples

Wixom studied comScore, a 14-year-old marketing research firm “with 14 petabytes of online data, collected real time from around the world.” In a research paper earlier this year, she describes how comScore achieves value creation from big data via three key assets: “A cost-efficient, scalable platform; an analytics-savvy workforce; and a deep understanding of its clients.” She concluded: “Data and analytics providers are highly experienced at working with big data. They create, build, and hone capabilities to exploit their data assets.”


Wixom also discussed the evolution of one her early case studies: medical supply distributor Owens & Minor. Although the company’s core business is distribution, Owens & Minor has a long history of gathering, using and ultimately monetizing its data via its “spend analytics” products and services. Since the 1990’s the distributor has collected information from its supply chain and sold it to suppliers that wanted to increase market penetration and sales – and to customers that wanted to manage cost of patient care. Over the next decade, Owens & Minor won new business and generated revenue as a result of its unique analytics capabilities. In addition to hard-dollar gains, it earned brand and reputational benefits as an early technology leader and consulting partner in the healthcare industry. Nevertheless, Owens & Minor’s analytics offerings now must co-exist and compete with offerings from software vendors, consulting firms and group purchasing organizations, Wixom said.


The bottom line to both the comScore and Owens & Minor stories, according to Wixom is this: Data monetization is not easy. As companies consider selling their data, they need to get into the game with eyes wide open, she adds.