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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/

 

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.

That the amount of business data is skyrocketing is hardly news. All we have to do is consider the huge volumes of data and archives at any major financial institution, retail business or healthcare organization. Then multiply those amounts by a several times and you’ll have an idea of the staggering amount of information amassed at web-based businesses such as Google and Amazon.

 

More important than the quantity of information generated, however, is an understanding of how it is used and how it can create value for organizations, their customers and the overall economy. At the MIT Center for Digital Business, a recent statistical study on the implications of big data offers significant proof that proper use of analytics and business intelligence tools can help businesses use their digital information to grow efficiently and show bottom-line results.

 

Specifically, my paper with Heekyung Kim, Strength in Numbers: How Does Data-Driven Decisionmaking Affect Firm Performance?, finds that “companies that use data-driven analytics instead of intuition have 5%-6% higher productivity and profits than competitors.” Research was based on the business practices and information technology investments of 179 large publicly traded firms. Huge improvements in metrics are allowing a granular analysis of data—whether it resides on mobile devices, in email or elsewhere in data centers-- to find out more about customer behavior. What’s more, the relationship between data-driven decisionmaking and performance also appears in other measures such as asset utilization, return on equity and market value. Our results provide some of the first large-scale data on the direct connection between data-driven decision making and firm performance.

 

As we wrote in the recent Atlantic magazine article:

“Today, businesses can measure their activities and customer relationships with unprecedented precision. As a result, they are awash with data. This is particularly evident in the digital economy, where clickstream data give precisely targeted and real-time insights into consumer behavior.”

 

And while web-based digital companies – notably, Amazon and Google--are in the forefront of data analysis, now we are seeing offline companies in logistics, manufacturing, retail, casinos and finance making use of these techniques as well. Gallo Wines, UPS, Caesar’s Entertainment and Match.com are a few examples cited in the Atlantic article. Marketing and sales organization are leading the way and are far ahead of some other departmental users, but increasingly, we see business units such as HR using email to help internal staff productivity benefits. Similarly, manufacturing lines are gaining access to real-time data from CRM and ERP systems to help them track trends and demand.

 

To delve into this topic in-depth, we are offering a new two-day executive education course on March 28 at MIT Sloan in Cambridge, Mass. [see related blog for details here.]