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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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There are many metrics and data available that quantify the use of digital goods and services. We know, for example, how many billions songs are downloaded and how much revenue that garners. We can tell how many articles there are on Wikipedia and how many hours people spend on Facebook.

 

To date, there are primarily three ways to quantify the impact digitization is having: We can look at the contribution of the transactions to the GDP; we can look at an IT company’s stock values, or we can track consumer spending and prices as indicators of consumer surplus created.

 

 

But none of these approaches measure the real value of digital services when the services are free, according to our latest research at MIT’s Center for Digital Business. For example, a money-only model may be missing 95% of the value consumers derive online. Finding that new metric is the focus of my team’s latest research. Free goods and services on the Internet have exploded in the past decade, and the average American now spends more than 32 hours a month online.

 

Once they have an Internet connection, they don't spend money to use Wikipedia, Facebook or Youtube, so the value of these services isn't properly reflected in the GDP statistics.

This gap is a problem we have been grappling with for some time, including in a Sloan Management Review article a few years ago.

 

At the recent annual meeting we offered a possible solution to the measurement problem: Consumers pay with time, not just money, and where they choose to spend their limited time and attention online is a form of voting. Increasingly, the digital economy is the 'attention economy.' Our research is calculating a demand curve for time and estimating how much consumers implicitly value free goods based on use of their time, not on dollars, spent on the Internet.

 

One preliminary finding shows that free goods added the equivalent of $139 billion in value to the economy in 2010-- more than 1% of the GDP and equal to $647 per person. The findings may have an impact of calculations such as the GDP and other economic metrics.


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The same robotic technologies that enable laboratory droids to bake cookies and construct small buildings have the potential to dramatically automate manufacturing processes in the next decade.

 

Robots are already changing lives and accelerating productivity much as computing did in the last few decades, according to MIT professor and researcher, Daniela Rus. The next wave of exponential growth and advancements in robotic fabrication also will have a huge impact on the digital economy. “We will soon get to an age where it is as easy to have your own robots as it is to print on paper today,” she said at an October IDE seminar.  Rus, Professor of Electrical Engineering and Computer Science as well as Director of the Computer Science and Artificial Intelligence Laboratory (CSAIL), discussed and demonstrated the results of her most recent work.A1-MIT-robots-rus_0.jpg

 

Based on a 2014 McKinsey report advanced robotics, the Internet of Things and autonomous cars are among the top 12 disruptive technologies with a total potential economic impact between $14 trillion and $33 trillion a year in 2025. (See infographics here). Advanced robotics alone could generate from $1.7 to $4.5 trillion, according to McKinsey. The estimates are  "based on an in-depth analysis of key potential applications and the value they could create in a number of ways, including the consumer surplus that arises from better products, lower prices, a cleaner environment, and better health."

 

Rus said despite huge progress, the high cost of robotic design and production, as well as limitations in communications and physical dexterity still have to be overcome before bots can reach their full economic potential. Toward that end, her group is building and deploying easily designed robots that can perform complex, multi-step tasks. They can also interact with humans and follow instructions with new levels of precision and accuracy. For example, a team of bots in the lab have built a log cabin by identifying parts and language sequences, then dividing the tasks among themselves. Once at work, the robots analyze instructions and adjust their processes to accommodate their droid co-workers. They communicate with each other and can ask their human partners for specific help if they get stuck with a task as well.

 

bakebot-2.jpgIn another example, an Iron Chef bot baked cookies by “reading” a series directions, mixing the ingredients and popping the pan into the oven. Executing such seemingly simple tasks required more than a year’s work in computational and lab development at MIT and cost about $500,000 to produce. The group is also testing a series of  simple, self-assembling "origami" style robots. The results of all of these efforts clearly “stretch the boundaries of what robots can do” now and indicate how much more they can achieve in the very near-term, Rus said—especially when commercial developers stake their claim.


“The state of robot production today is similar to where programming was before the invention of compilers,” she said. And with the rapid pace of advancements, the proliferation of low-cost, high function robots is just a blink away.

Whether you think it’s a good idea or not, mobile and social technologies are creating new ways to follow, analyze and predict how people are “embedded in society,” and how and where they spend their time and money. The implications of these changes for individuals, as well as society, are being studied by Alex `Sandy’ Pentland, director of MIT’s Human Dynamics Laboratory and the MIT Media Lab Entrepreneurship Program.

 

His current research examines four ways that Big Data can help to understand human behavior: By modeling social influence; by examining social influence dynamics; by actually shaping behavior, and by creating more data-driven societies.  Pentland hopes these insights may help reverse “many of the frustrating phenomena that we are familiar with....fads, groupthink, and projects that just go nowhere.”

 

The MIT researchers looked at social influence networks and their relationship to learning, purchases and other behaviors by following 65 young families for one year. One finding was that social influence incentives work to change behavior more than other incentives because in a group, members have common ties and an exchange network on which to rely. Local information can pressure peers to act in certain ways and to be rewarded for those behaviors. “Incenting the social ties can be efficient,” Pentland explained at a recent MIT CDB seminar.

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In call centers, for example, productivity improves with more coffee breaks, because workers share information that leads to better performance. Similarly, “social traders who aren’t isolated and aren’t in echo chambers,” perform best, he said. The point is to “encourage diversity of ideas and engagement.”

 

The Human Dynamics Lab at the MIT Media Laboratories pioneered the idea of a society enabled by Big Data. The Lab has developed technologies such as reality mining, which uses mobile phone data to extract patterns that predict future human behavior, as well as a `nervous system’ framework for dramatically more efficient transportation, health, energy, and financial systems.

 

Pentland’s latest research could be applied to what he calls, “data-driven societies.” Since geography influences behavior and patterns of communications, which creates “collective intelligence” in local groups,” city-scientist, for instance, may be able to predict the GDP of a city by looking at social-tie patterns. In turn, this might help city planners build environments that better match the habits of the local citizens.

 

Separately, McKinsey is conducting research into social intelligence. In its new report, McKinsey discusses social intelligence as a means of guiding better business decisions.

 

The report states that by tapping into social platforms, businesses can gather and harness employee knowledge.

Today, many people who have expert knowledge and shape perceptions about markets are freely exchanging data and viewpoints through social platforms. By identifying and engaging these players, employing potent Web-focused analytics to draw strategic meaning from social-media data, and channeling this information to people within the organization who need and want it, companies can develop a “social intelligence” that is forward looking, global in scope, and capable of playing out in real time.

This isn’t to suggest that “social” will entirely displace current methods of intelligence gathering. But it should emerge as a strong complement. As it does, social-intelligence literacy will become a critical asset for C-level executives and board members seeking the best possible basis for their decisions.

And in another report Capturing Business Value with Social Technologies, McKinsey conducted an in-depth analysis of four industry sectors that represent almost 20 percent of global sales.

[The analysis] suggests that social platforms can unlock $900 billion to $1.3 trillion in value in those sectors alone. Two-thirds of this value creation opportunity lies in improving communication and collaboration within and across enterprises. Frequently, these improvements will go well beyond the areas many companies have focused on to date in their social-media efforts: connecting with consumers, deriving customer insights for marketing and product development, and providing customer service.

 

Clearly, Pentland’s work supports McKinsey’s conclusion that: “Social technologies are destined to play a much larger role, not only in individual interactions, but also in how companies (and Pentland might add, societies), are organized and managed.”

 

Sandy Pentland is a member of this community. Comment on his work here.