Gijs Overgoor Research

In his research Gijs Overgoor focuses on applying techniques from AI and Econometrics to solve marketing problems, mainly in the space of visual marketing.

He has published in the International Journal of Research in Marketing and California Management Review. 

Gijs’ research has featured in the Business Insider, UpNext Podcast, Today in Digital Marketing, and  The World Transformed podcast.

Publications

+ G. Overgoor, W. Rand, W. Van Dolen, M. Mazloom, ”Simplicity is not Key: Understanding Marketer-Generated Social Media Images and Consumer Liking”.  Forthcoming at International Journal of Research in Marketing

Social media channels are becoming increasingly important marketing channels, and recently these channels are becoming more and more dominated by content that is not textual, but visual in nature. Relating textual content to sales and conversions is difficult enough, but visual content is even more difficult to analyze. In this paper, we explore how consumers engage with visual content. Speci fically, we explore the role of the complexity of images in creating consumer liking. To carry this out, we use a number of different features of the images posted on Instagram by brands and relate these features to likes on the images. We use a convolutional neural network that can automatically identify objects contained in the images, to create features used to build the model. We show that there is a u-shaped relationship between the complexity of images and the amount of likes that they generate from consumers, and provide insights into how this knowledge can be used to generate and choose better social media images.

+ G. Overgoor, M. Chica, W. Rand, A. Weishampel (2019), ”Letting the Computers Take Over: Using AI to Solve Marketing Problems”, in press at California Management Review. https://doi.org/10.1177/0008125619859318

Artificial intelligence (AI) has proven to be useful in many applications from automating cars to providing customer service responses. However, though many firms want to take advantage of AI to improve marketing, they lack a process by which to execute a Marketing AI project. This article discusses the use of AI to provide support for marketing decisions. Based on the established Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, it creates a process for managers to use when executing a Marketing AI project and discusses issues that might arise. It explores how this framework was used to develop three cutting-edge Marketing AI applications.

Working Papers:

+ G. Overgoor, W. Rand, W. Van Dolen, ”The Champion of Images: Understanding the Role of Images in the Decision-Making Process of Online Hotel Bookings”. Invited for resubmission
 – Nominated for best paper at HICSS 2020

Images are vitally important in engaging consumers and helping them to make decisions. On many online travel agency (OTA) websites, the hotel`s image can take up 33 % of the space of the search result listing, but the importance of this image in the decision-making process has yet to be studied. In this research, we use deep learning to extract information directly from hotel images and we apply visual analytics to understand the importance of this information during consideration set formation. We perform a hotel-level prediction and find that we are able to accurately predict what hotel will be more likely to be clicked on based on the information we can extract from the image. We then complement these findings using LambdaMART to predict consumer clicks during search and find that on average there is a 10 % improvement when we incorporate image information as compared to just the textual and numerical features. In addition, we find that the imagery impacts the importance of other attributes such as price, with a decrease in importance of over 70 % in some locations. Finally, in a neuroscience experiment we show that our results can be explained by the fact that the human brain processes images with a high click-through rate differently than low click-through rate images. Overall, we present one of the first visual analytics frameworks that can be used at a large-scale to help understand the impact of imagery online. Our research has valuable theoretical and methodological implications that advance the study of unstructured data in marketing. 

+ G. Overgoor, R. Mestri, W. Rand, “In the Eye of the Reviewer: An Application of Unsupervised Clustering to User Generated Imagery in Online Reviews” – in preparation for submission
Nominated for best paper at HICSS 2021

Mining opinions from online reviews has been shown to be extremely valuable in the past decades. There has been a surge of research focused on understanding consumer brand perceptions from the textual content of online reviews using text mining methods. With the increase in smartphone usage and ease of posting images, these reviews now often contain visual content. We propose an unsupervised cluster method to understand the user-generated imagery (UGI) of online reviews in the travel industry. Using the deep embedded clustering model we group together similar UGI and examine the average review ratings of these clusters to identify imagery associated with positive and negative reviews. After training the method on the entire dataset, we map out individual hotels and their corresponding UGI to show how hotel managers can use the method to understand their performance in particular areas of customer service based on UGI. The performance in a  cluster relative to the population can be a clear indicator of areas that need improvement or areas that should be highlighted in the hotel’s marketing efforts. Overall, we present a useful application using visual analytics for mining consumer opinions and perceptions directly from image data.  

Research in Progress:

+  “The Impact of TV Ads on Consumer Decision Journey
Metrics: An Application of Video Analytics” with A. Colicev, Y. Bart, K. Pauwels

+ “Measuring Diversity in Visual Marketing Communication” with W. Xie, H.H. Lee, H. Zhu

+ “Dectecting Fake Review Buyers Using Network Structure: Direct Evidence from Amazon” with S. He, B. Hollenbeck, D. Proserpio, A. Tosyali