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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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Online media and social-advertising necessitate new ways to measure and drive data-based decision-making among customers. They are also creating a new field of experimental learning techniques and tools that are replacing classic, randomized market testing practices in many cases.

 

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Dean Eckles, a social scientist, statistician and assistant professor in the MIT Sloan School of Management, explained how tools for designing, deploying and analyzing online field experiments can encourage good statistical and methodological practices as well as better understanding of online customer behavior. As MIT Sloan professor Glen Urban and IDE researcher, Sinan Aral, and others, have discussed, different types of ads and messaging are being tested all the time to determine what motivates online marketing and social activities.

 

Eckles, a former member of the Core Data Science team at Facebook who also worked at Yahoo, knows first-hand that “the Internet industry has distinct advantages in how organizations can use data to make decisions. Firms can cheaply introduce numerous variations on the service and observe how a large random sample responds when randomly assigned to these variations.”

 

At the same time, he told a recent MIT IDE seminar, “rapid, iterative, and organizationally distributed experimentation” also introduces important challenges-- such as understanding the effects of a change intervention.

 

Challenges arise because many experiments are being run -- often by different teams -- requiring tools to support rapid experimentation. For example, "How can multiple different teams experiment with the design of a single page at the same time?"

 

PlanOut: An App for Experimental Design

Facebook was seeking alternatives to standard A/B tests to answer questions like these about its users. A/B tests work well when minor tweaks to a system are needed, but not when more complicated or nuanced change has to be measured. Eckles and his team team built an open-source app called PlanOut, a language for describing complex experimental designs for behavioral science experiments. It uses standard code script to assign value to specific procedures and also can help manage multiple testing that takes place simultaneously on a site. Less technical users can program it via a GUI.

 

Eckles is interested in other applications for these types of tools and analytics that might, for example, show the best way to motivate voting or civic participation.

Feedback is important on social media content, he said, and experiments can focus on straightforward items such as comment boxes to measure user engagement or they can analyze more subtle factors such as how comments affect users and influence others.

 

 

For more on Eckles’ research on PlanOut see his 2014 paper here and a list of 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.]