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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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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.

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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I had a chance to present my research with Joohee Oh about the value of free goods at the recent Techonomy conference in Tuscon. We now to have the full video and transcript available to read and view. As I wrote about here and as Andy McAfee described, I’m convinced that we are in the midst of a major technological revolution that is not being fully reflected in official government statistics, most notably, the GDP and productivity numbers

 

According to official government data, there has bee zero growth in the information sector. But we have all seen an explosion of new digital products and services, from Wikipedia and Facebook, to Youtube and GPS mapping. The official data say this sector is the same size as what it was in the 1960s! Now how can that be? Obviously, there’s some major measurement problems in the way we keep our statistics, and that’s a real concern because, as the saying goes, you can’t manage what you don’t measure. So we need to come up with a better way of measuring things.  That’s what we’ve been working at the MIT Center for Digital Business.

 

Here are a few highlights from my talk:

 

      • We start with the fact that many digital goods are delivered for free. In some cases, they're funded through advertising. In many other cases, users just contribute their time; they develop content and make it available. And maybe there’s a little bit of contributions or advertising that pays for the bandwidth, but those costs are relatively minimal. We know that there’s just been an explosion of the availability of these goods because you can count the number of bits produced; you can count the number of Wikipedia articles produced, for example--and those have grown ten-fold since 2004.
      • Counting bits is a start, but what we really want to know is not just the number of bits but the actual value of information goods. This is where the bug in the GDP measurement occurs; because GDP measures only the total amount spent on goods and services, not their value. So what happens if the price is zero? Well, zero times any quantity is still zero. So you could have an enormous of explosion of bits or articles or whatever else and the statisticians still see it as a big fat zero contribution for our GDP
      • Traditional metrics are really missing what’s going on in this information economy because so much of the digital economy is a free economy. We found a number of other ways to go about measuring it. One is to look at the time that people spent, and that is something that we do. If you just look at the dollars, you’re going to get a sense that actually the economy is stagnant or even shrinking. But we calculate the demand curve for information goods based on the time spent. This is a very different approach than the traditional GDP accounting, but it’s one that we think better captures the real value that goods and services are producing in the economy.
      • After you do the math and plug in the numbers, we estimate that the annual welfare gain from all these free goods is about $300 billion. Now, that’s the average over the past ten years or so. And that works out to about $1,400 per person.

 

We also came up with a way to calibrate the value of this time which can you hear more about if you watch the full video. I will continue to write about our findings as the research unfolds.