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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.

Not all clicks are equal: detecting engagement with digital content