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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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As social media becomes more pervasive in business and economic life, many researchers are trying to figure out just how much impact it is having on sales and on business models.

 

Jeffrey Hu, Associate Professor at Scheller College of Business  at Georgia Institute of Technology, and an MIT Sloan alumni, is among those studying the effects of social media. In particular, he examined just how much online broadcasting channels and crowdsourcing are influencing markets and customers compared with more traditional marketing channels. “With the emergence of social media and Web 2.0, broadcasting in the online environment has evolved into a new form of marketing due to the much broader reach enabled by information technology,” Hu said.hu_jeffrey_profile.jpg1.jpg

 

Turning Buzz into Business

During 2008 to 2009, Hu studied the patterns of the MySpace music community, the largest at the time, with 14 million users. He wanted to know if broadcasting information via social media –sending updates, bulletins and texts (this was before Twitter really had a strong presence) would result in greater economic returns. In other words, he said at a recent MIT CDB lunch seminar, “whether buzz could turn into sales.”

 

Hu and his team employed a panel vector auto-regression (PVAR) model to investigate the inter-relationship between broadcasting promotions in social media and music sales. By correlating social media activity of 631 musicians for 32 weeks and comparing the data to Amazon rankings, Hu was able to see a significant effect on sales. The study accounted for control variables such as promotional spending, new album releases and size of network, among other factors.

 

The research concludes that artist-generated content -- particularly personal messages versus automated ones-- can increase sales and ranking on Amazon. By extension, Hu believes that companies can use social media to promote products and boost sales. “Our findings also point to the importance of conducting captivating conversations with customers in the organizational use of social media,” he said.

 

The Wisdom of Crowds

The second study Hu described at the seminar looked at the wisdom of crowds and crowdsourcing compared with expert advice and content online. Many people have pointed out that while Wikipedia contains errors, for example, it also can be corrected quickly from a vast range of sources versus traditional, permanent resources such as print encyclopedias. Some advocates believe that customers turn to peer-based communities, such as Yelp, for restaurant reviews over venerable sources like Michelin guides because the websites are more current, are more accessible and have wider coverage areas.

 

In his research of the financial analysis sector, Hu found that the online community Seeking Alpha--which relies on investor input instead of journalists or professional analysts-- has been “surprisingly accurate” in predicting financial trends and making investment recommendations.

 

Of course, there are also many caveats where enterprise social media is concerned. As McKinsey notes in this recent journal article, “on-demand marketing” is putting enormous pressures on businesses to respond in four key areas:

1. Now: Consumers will want to interact anywhere at any time.

2. Can I: They will want to do truly new things as disparate kinds of information (from financial accounts to data on physical activity) are deployed more effectively in ways that create value for them.

3. For me: They will expect all data stored about them to be targeted precisely to their needs or used to personalize what they experience.

4. Simply: They will expect all interactions to be easy.

 

Maybe the next studies will focus on how well social media can help achieve these daunting consumer demands.

 

 

For related MIT research about social advertising, see this blog describing Catherine Tucker’s research.


For  more of Jeffrey Hu's research, see the following abstracts:


http://ssrn.com/abstract=2201430

 

http://ssrn.com/abstract=1807265)

 

and his Georgia Tech profile here:  http://scheller.gatech.edu/directory/faculty/hu/

 

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.

I teach at a School of Management so you won’t be surprised to learn that I think good management can make a huge difference in the performance of companies, and ultimately the economy.  But you may be surprised that there is very little economic research on the effects of management.  Sure, there’s lots of speculation and countless management books and articles, but a recent review of the economic literature by Chad Syverson concluded: “No potential driving factor of productivity has seen a higher ratio of speculation to empirical study [than management practices].”  The biggest problem has been simply a lack of a comprehensive, reliable data set of management practices.

 

To address this gap, I recently helped formulate the U.S. Census Bureau’s survey of management and organizational practices at more than 30,000 manufacturing plants across the country--the first large-scale survey of management in America. Along with Nick Bloom, Lucia Foster, Ron Jarmin, Itay Saporta and John Van Reenen, we examined three types of practices-- performance monitoring; setting targets, and offering incentives—which we called “Structured Management.”

 

Analysis of the data reveals several striking results about the relationship between performance goals and improved business. Specifically, setting business goals and monitoring results are among the practices that actually yield better business productivity and growth, according to this comprehensive survey of U.S. management conducted in 2011. The survey was funded by the National Science Foundation and had administrative support from the National Bureau of Economic Research and the MIT Center for Digital Business. It was a joint, academic-U.S. census bureau collaboration.

 

My fellow researchers and I set out to determine whether, and what type of management practices influence bottom-line business results such as productivity, output and growth. Based on the responses, we found a tight link between Structured Management and performance outcomes such as growth, expenditures and innovation as indicated by R&D and patent intensity.

 

Figure 1: Better Performance is Associated With More Structured Management

ebchart1a.bmp

 

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While the survey did not focus exclusively on digital technologies, the conclusions may partly reflect the increasing adoption of information technologies, like Enterprise Resource Planning (ERP) systems, which make data collection and processing much cheaper, easier and more effective. Structured Management scores for data use have improved the most, according to the data. Presumably this reflects the growing use of IT in modern firms.

 

The study also highlights the important rise of data-driven decisionmaking, which the MIT CDB has championed for several years. Most of the rise in structured management practices, for example, has come among businesses that have implemented data-driven performance monitoring.

 

Figure 2: Average Management Scores Increased between 2005 and 2010,Especially for Data Driven Performance Monitoring

eb3.bmp

 

It was also interesting to note that adoption of structured management practices has increased between 2005 and 2010, particularly for those practices involving data collection and analysis.  This is consistent with my earlier research with Lorin Hitt and Heekyung Kim on Data-Driven Decisionmaking.

 

Among other key findings:

 

-- There is a substantial dispersion of management practices across the establishments. Eighteen percent have adopted at least 75% of these more structured management practices, while 27% adopted less than 50% of these.

 

--There is a positive correlation between structured management practices and location, firm size, establishment-level measures of worker education, and export status.

 

Going forward we will continue to analyze the data and explore causality. Additionally, we may do another survey in 2015 to establish longer-term data and perhaps will focus on the retail or health-care sector.

 

Let me know what other ideas you think we should explore.