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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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My MIT colleague David Autor delivered a wonderful paper at the recent Jackson Hole Economic Policy Symposium about American job and wage patterns in recent decades, and their link to the computerization of the economy. I’ll say more later about his paper, which was one of the highlights of the event for me (sighting this moose was another one). For now I just want to highlight one graph that he included, and draw a couple additional ones.

 

Autor included a chart very much like the one below, which tracks US corporate spending over time on digital stuff — hardware and software — as a percentage of GDP:

 

 

The most striking pattern in this graph is the sharp increase in the late 1990s, and the steep falloff since then. We’re spending just about a full percentage point of GDP less on IT than we were fifteen years ago. This seems like a compelling prima facie case for believing that IT’s impact on the economy and the labor force should be less than it was before the turn of the century.

 

And this is what Autor believes. As he writes

the onset of the weak U.S. labor market of the 2000s coincided with a sharp deceleration in computer investment—a fact that appears first-order inconsistent with the onset of a new era of capital-labor substitution.

I completely agree with him (based largely on his very convincing work) that other factors have strongly shaped the US economy and labor force since the 2000, particularly the emergence of China as an offshoring and manufacturing powerhouse. But I’m not so sure that the impact of digital technologies tapered off to the extent the graph above would have us believe.

 

To see this, let’s break out the data on software. Information processing equipment is simply a vehicle for software, in much the same way that a bottle of 5-hour energy is a delivery system for caffeine. Hardware runs software, in other words, and it’s software that runs things.

 

It’s easy to lose sight of that fact in an era of gorgeous devices like Apple’s, but without the apps my iPhone is just a… phone. It’s software that is ‘eating the world,’ to use Marc Andreessen’s memorable phrase.

 

So how has software spending held up? Pretty well:

 

 

There was a slight dropoff after the dot-com bubble burst and the Y2K fiasco passed, but we’re back near the all-time software spending peak. It’s true that this spending has been pretty flat for the past fifteen years, but we should keep in mind that this is also the time when open source software and the cloud and everything-as-a-service burst on the scene. All of these development have significantly lowered the bill for a given level of enterprise software capability, so I look at the graph above as pretty good evidence of constantly increasing demand for software, even though spending has remained constant for a while now.

 

The ascendancy of software can be seen in a graph of its share of total IT spending over time:

 

software

 

Software now accounts for over half of all IT spending. As Moore’s Law, volume manufacturing, and the cloud continue to drive down the costs of hardware, I expect software’s share of total spend to continue to rise steadily.

 

I don’t know what’s going to happen to total IT investment as a percentage of GDP going forward. It does feel to me like a sea change is taking place — that it’s getting so much cheaper to acquire digital technologies that even if demand for them rises strongly in the future total spending on them might not (or as an economist would put it, the price effect might be greater than the quantity effect).

 

So even if the first graph above doesn’t greatly change its shape in the years to come, I won’t take that as evidence that the digital revolution has run its course. Will you?

 

 

 

 

This post first appeared on my Business Impact of IT blog Sept. 3 here.

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.