Not all clicks are equal: detecting engagement with digital content

ABSTRACT

Clickstream data recording each click that each individual user makes on a media website has become the currency for evaluating digital platforms in order to maximise advertising and/or subscription revenue. There is a growing recognition, however, that the mere volume of clicks is not adequate for this purpose. We propose a new systematic approach to this problem based on an underlying theory of engagement. Engagement is construed theoretically as user experiences that connect to higher-order personal goals or social values. We show that such experiences can be described qualitatively using survey items that form engagement measurement scales and that these engagement scales, in fact, explain a willingness-to-pay outcome variable. Moreover, these experiences can be translated into surrogate decomposed clickstream variables. We analyse data from three news websites and show that these decomposed clickstream variables predict willingness-to-pay for the sites better than raw, undecomposed clickstream data. Our methodological framework thus provides a new way of using clickstream data to detect engagement with digital content, a method that provides a basis for improving engagement and ultimately outcomes such as the willingness to pay for content.

KEYWORDS:

• User engagement
• digital news
• user experience
• clickstream data

Acknowledgments

The Northwestern Local News Initiative (LNI) and the Spiegel Research Center (SRC) are supported by the Lilly Endowment, Myrta Pulliam Charitable Trust, John Mutz, and several anonymous donors. We are grateful to Tim Franklin, director of LNI; Tom Collinger, executive director of SRC; and the participating news organizations.

Disclosure of potential conflicts of interest

No potential conflict of interest was reported by the author(s).

Notes

1. Engagement is a higher-order factor that includes Regularity, but Regularity has been separated for the purpose of this analysis.

Additional information

Notes on contributors

Yayu Zhou

Yayu Zhou is a four-year Ph.D. student at the Industrial Engineering & Management Science, Northwestern University. She majors in applied statistics and statistical learning and minors in analytics. Her research interest centers on predictive analytics, big data, customer engagement, and lifetime value models.

Bobby J. Calder

Bobby J. Calder is the Kellstadt Professor of Marketing (Emeritus), Kellogg Graduate School of Management, Northwestern University, and Chair, International Standardization Organization (IS0) Technical Committee 289 on Brand Evaluation. He is also a Professor in the Medill School of Journalism, Media, and Integrated Marketing Communications. Formerly, he has served on the faculty of the University of Illinois and the Wharton School, University of Pennsylvania. His work is primarily in the areas of brand strategy and the financial value of brands, consumer psychology, media, and research methodology.

Edward C. Malthouse

Edward C. Malthouse is the Erastus Otis Haven Professor of Integrated Marketing Communications and Professor of Industrial Engineering and Management Science at Northwestern University. He is the Research Director for the Spiegel Center for Digital and Database Marketing and a researcher for the Local News Initiative, both at Northwestern University. He is a co-editor of the Journal of Service Research and associate editor for Frontiers in Big Data-Recommender Systems. He was the co-editor of the Journal of Interactive Marketing between 2005-2011 and has co-edited two special issues for the Journal of Advertising. His research interests center on customer engagement and experiences; digital, social, and mobile media; media management; big data; customer relationship management and lifetime value models; recommender systems; and predictive analytics.

Yasaman Kamyab Hessary

Yasaman Kamyab Hessary was a post-doctoral fellow at Spiegel, she received her Ph.D. in Computer Science from the University of North Carolina at Charlotte. Her dissertation explores agent-based computational economics models with emphasis on herding behavior.