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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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Digital automation, and its impact on labor, society and the economy, has been studied from multiple perspectives and through many lenses.  In his new research and analysis, Daron Acemoglu, the Elizabeth and James Killian Professor of Economics at MIT, acknowledges inequalities created when automation displaces certain human skills. However, he also says it is possible for new technology to create more complex versions of existing tasks where labor has a comparative advantage, tipping the scales back toward a future with plentiful jobs.

http://economics.mit.edu/timages/7


Acemoglu completed his graduate work in mathematical economics and econometrics at the London School of Economics, where he also received his Ph.D. in economics. His recent research focuses on the political, economic and social causes of differences in economic development across societies; the factors affecting the institutional and political evolution of nations; and how technology impacts growth and distribution of resources. Acemoglu has published four books: Economic Origins of Dictatorship and Democracy (joint with James A. Robinson), Introduction to Modern Economic Growth, Why Nations Fail: The Origins of Power, Prosperity, and Poverty (joint with James A. Robinson), and Principles of Economics (joint with David Laibson and John List).


He recently spoke at an MIT IDE seminar on the topic of, "The Race between Machine and Man: Implications of Technology for Growth, Factor Shares and Employment.” IDE Community manager Paula Klein followed up with four questions. Below are his responses.

 

 

Q.  The current rivalry between digital automation and humans seems focused on economics and labor issues— concerns that labor will be progressively marginalized and made redundant by new technologies. Is this focus premature or overstated?


A: It is certainly not premature. We have seen many different types of tasks produced and performed by labor, even fairly skilled labor, become automated over the last 30 years. We also know of new technologies that will automate some very major occupations (regulations permitting), including driving, airplane piloting, some aspects of surgery, certain types of diagnoses and even parts of the practice of law.

Yet there is an aspect of it that is overstated. These are still only some of the occupations that humans perform today. The more important overstatement comes as one turns from automation to the prospects for future employment creation. This rapid process of automation does not mean that the future economy will not create jobs. If you look at the last several decades, qualitative evidence suggests rapid automation has been going on for more than a century, and a lot of the new employment comes in new tasks and occupations. So, as machines take jobs previously performed by humans, the economy appears to create yet other tasks and jobs to employ the displaced workers.

 

Q: How does your task-based framework help explain the current economic situation and provide context? Can you briefly summarize the model and your research?


A: Our framework helps us understand the aforementioned patterns and why the fact that new employment will come from new tasks and activities. But more importantly, because it endogenizes the speeds at which existing tasks are automated and new tasks are created, it also highlights why a period of unusually rapid automation generally brings a subsequent period of rapid creation of new tasks. Put simply, rapid automation depresses the price of labor which has fewer tasks to work. This then makes it more profitable for new tasks, which employ new labor, to be created.

 

Q: How might these new tasks spur economic growth and innovation?


A: The growth implications of creating new tasks are essentially a corollary of what I have just described. Growth comes about both because of automation -- we can do things we have been doing more cheaply-- and because of the creation of new tasks; we have new goods and services using better technology. Anything that spurs innovation triggers faster economic growth. So, rapid automation is a double whammy: it benefits us directly and it spurs additional creation of further growth-enhancing new tasks.

 

Q: What guidance can you offer to employers, workers, students and policymakers to prepare and adjust for the Second Machine Age?


A: All of these scenarios are no consolation if you do not have the skills that new tasks and jobs will demand. Some economists are now questioning whether college is a good investment. There are certainly reasons for rethinking some of our long-cherished assumptions: college is expensive and college graduates have not done very well in the labor force over the last 15 years or so. Nevertheless, improving the skills of our workforce and improving our own skills still remain the only ways of ensuring that we adapt to the future of technology.

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.

 

http://mitsloan.mit.edu/uploadedImagesV9/Global/Articles_and_Press_Releases/Newsroom_-_Articles/eckles210x.jpg

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.

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.