217. IJRM. Social Media Images and Consumer Likes (it’s not simple).
June 2, 2022
Listen to full episode :
Consumers live on social media. It’s where they find their news. It’s where they search for information. It’s where they discover content. It’s also where they scroll, scroll and scroll right past your brand’s posts – unless you grab their attention.
But what kind of posts not only stop the scroll, but also generate a like? Despite being a very visual medium, there is very little research into the characteristics that make visuals work well on social media. Bill Rand and Gijs Overgoor have changed that.
Our conversation with them explores their recent research into the relationship between visual complexity and consumer likes.
Topics include:
The impact of visual complexity on the success of your Instagram post.
The Instagram filter that gets the most likes. Seriously. They found the best one.
Why you don’t want to have an asymmetrical image in your Instagram posts.
Click here for the Research Article
The Up Next podcast’s access to this content is courtesy of the International Journal of Research in Marketing, an international, double-blind peer-reviewed journal for marketing academics and practitioners. IJRM aims to contribute to the marketing discipline by providing high-quality, original research which advances marketing knowledge and techniques. As marketers increasingly draw on diverse and sophisticated methods, IJRM‘s target audience is comprised of marketing scholars, practitioners (e.g., marketing research and consulting professionals) and policymakers.
IJRM aims to be at the forefront of the marketing field with a particular emphasis on bringing timely ideas to market. The journal embraces innovative research with the potential to spur future research and influence practice. Hence, it welcomes contributions in various aspects of marketing. The editors, while accepting a wide array of scholarly contributions from different disciplinary approaches, especially encourage research that is novel, visionary or path breaking.
William (Bill) Rand is an Associate Professor of Marketing at the Poole College of Management at NC State University, specializing in the intersection of marketing and computer science. His research focuses on data-driven decision-making and the diffusion of information among consumers and organizations. To do this he examines the use of computational modeling techniques, such as agent-based modeling, machine learning, network analysis, natural language processing, and geographic information systems, to help understand and analyze complex systems, such as the social media marketing, organizational behavior, and predictive analytics. He has applied his methods to analyze big data sets that have been drawn from social media platforms, marketing communications, and large-scale software systems. He works to develop methods, create pedagogy, and build frameworks to allow researchers and marketing practitioners to use analytics and data-intensive methods in their own work. He has received funding for his research from the NSF, DARPA, ARL, Google, WPP, and the Marketing Science Institute. His work has been published in JM, JMR, IJRM, Management Science, and JOM. He received his doctorate in Computer Science from the University of Michigan in 2005 and prior to coming to NCSU was at the University of Maryland for eight years.
While completing his PhD in computer science at the University of Michigan, Bill became interested in the application of computer science to social science problems. As a result, while finishing his dissertation he also became heavily involved with a project, at the Center for the Study of Complex Systems (CSCS), that was using agent-based modeling, a form of computer simulation, to study urban policy and its relation to how residents choose which homes to buy. While there he was an architect on one of the first large-scale agent-based models of suburban sprawl. After graduating, Bill was awarded a postdoctoral research fellowship by the Northwestern Institute on Complexity (NICO). There he expanded his work on agent-based modeling of social systems, and became interested in the study of information diffusion among consumers. How do people find things out about new products? What causes them to make a purchase? When do they decide to recommend products to others?
Based on this work, he was offered a position at the University of Maryland’s Robert H. Smith School of Business, where he was asked to help run a brand new research center, called the Center for Complexity in Business. The goal of this Center was to capitalize on the quickly increasing amount of data in the world and the rapidly decreasing computational costs to create models of complex systems that would aid managers in making business decisions. The Center was successful, publishing papers in Marketing, Management Science, and Computer Science, raising around two million dollars in funding, and holding eight conferences that attracted participants from all over the world. The Center became known as a prominent data science and predictive analytics center. Bill served as the Director of Research, and eventually became the Director of the Center. He still serves as Director Emeritus.
Most recently, Bill has brought his skills to NC State’s Poole College of Management where he is focusing even more on the growing importance of analytics in the Marketing domain. He has developed a new MBA class on Digital Marketing, that is analytically focussed, and is providing both teaching and curriculum advice on a Business Analytics Honors program for the undergraduates. His research has also continued to be focused on the application of computational methods to marketing and management problems. Bill’s research provides computational methods, such as machine learning, artificial intelligence, and big data approaches, to deal with the large-scale data that is now available for decision-making, which is a key component of future marketing research. Marketing practitioners are demanding more and more in terms of analytics, and Bill’s research aims to provide tools, pedagogy, and frameworks to help marketing practitioners develop and use descriptive, predictive, and prescriptive analytics in a data-driven environment.
Gijs Overgoor is an Assistant Professor of Marketing at the Department of MIS, Marketing, and Digital Business in the Saunders College of Business at Rochester Institute of Technology.
He completed his Marketing PhD at the University of Amsterdam, under the supervision of Professor Willemijn van Dolen. He holds a Masters degree in Econometrics with a specialization in Big Data and Business Analytics. Gijs spent most of his time during his PhD in the United States as a visiting scholar at Poole College of Management at NC State University.
In his research, he adopts a quantitative approach to marketing. He aims to solve marketing problems by applying techniques from AI and Econometrics. His most recent paper on a framework for implementing Marketing AI projects was published in the California Management Review Special Issue on AI.
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International Journal of Research in Marketing
William (Bill) Rand
Gijs Overgoor