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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/

 

Two papers came out last year that examined important issues around jobs and wages. Both are in top journals. Both were written by first-rate researchers, none of whom specialize in studying the impact of technology. And both came to the same conclusion: that digital technologies were largely responsible for the phenomena they examined.

The first paper, by David Dorn and my MIT colleague David Autor,  is about how jobs and wages changed in America from 1980-2005. It was published last year in the American Economic Review and called “The Growth of Low-Skill Service Jobs and the Polarization of the US Labor Market,” which is an admirably informative title.

Equally admirable are the graphs the authors draw to illustrate their main findings. Here’s the one for jobs (the one for wages has a pretty similar shape). It gives the changes in employment share — which you can think of as changes in the the ‘market share’ of jobs — between 1980 and 2005. And it shows vividly that low-skill and high-skill jobs gained market share over that period, which those in the middle of the skill range lost.

Autor

Autor and Dorn are clear on what accounts for this shift:

The adoption of computers substitutes for… workers performing routine tasks—such as bookkeeping, clerical work, and repetitive production and monitoring activities—which are readily computerized because they follow precise, well-defined procedures. Importantly, occupations intensive in these tasks are most commonplace in the middle of the occupational skill and wage distribution.

and what doesn’t:

We evaluate numerous alternative explanations for the pronounced differences in wage and employment polarization… including deindustrialization, offshoring, … and growing low-skill immigration. None of these alternatives appears central to our findings.

The second paper concentrates on wages, and tries to determine what’s caused the red line in the graph below to decline so fast in recent years

http://www.slideshare.net/amcafee/mc-afee-econ-data

profits and labor share

This line documents the labor share of GDP in the US over the post-war period — the percentage of GDP that gets paid out in compensation (wages and benefits) to workers. As the graph above shows, US labor share has  been heading down sharply just as corporate profits have reached hew heights.

Is this because of globalization? Nope, because it’s been happening around the globe. As Loukas Karabarbounis and Brent Neiman write in “The Global Decline of the Labor Share” (out in the current issue of the Quarterly Journal of Economics):

We document, however, that the global labor share has significantly declined since the early 1980s, with the decline occurring within the large majority of countries and industries. We show that the decrease in the relative price of investment goods, often attributed to advances in information technology and the computer age, induced firms to shift away from labor and toward capital.

The AER and QJE are the two top journals in the economics field, so this research is about as solid as it gets. In light of this and plenty of other work, it really is time to stop arguing about whether technology has been one of the tectonic forces reshaping work and the workforce in recent decades. The evidence is just too clear that it is, and that we see evidence of the second machine age everywhere, including in the statistics.

This post first appeared March 12 on my Business Impact of IT blog 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.