A Price-per-attention Auction Scheme Using Mouse Cursor Information

Abstract

Payments in online ad auctions are typically derived from click-through rates, so that advertisers do not pay for ineffective ads. But advertisers often care about more than just clicks. That is, for example, if they aim to raise brand awareness or visibility. There is thus an opportunity to devise a more effective ad pricing paradigm, in which ads are paid only if they are actually noticed. This article contributes a novel auction format based on a pay-per-attention (PPA) scheme. We show that the PPA auction inherits the desirable properties (strategy-proofness and efficiency) as its pay-per-impression and pay-per-click counterparts, and that it also compares favourably in terms of revenues. To make the PPA format feasible, we also contribute a scalable diagnostic technology to predict user attention to ads in sponsored search using raw mouse cursor coordinates only, regardless of the page content and structure. We use the user attention predictions in numerical simulations to evaluate the PPA auction scheme. Our results show that, in relevant economic settings, the PPA revenues would be strictly higher than the existing auction payment schemes.

The Attentive Cursor Dataset

“…Boi et al ( 2016 ) proposed a method for predicting whether the user is actually looking at the content pointed by the cursor, exploiting the mouse cursor data and a segmentation of the web page contents. Lastly, Arapakis and Leiva ( 2016 ) investigated user engagement with direct displays on SERPs and provided further evidence that supports the utility of mouse cursor data for measuring user attention at a display-level granularity (Arapakis and Leiva, 2020 ; Arapakis et al, 2020 ).…”

“…For example, most columns pertaining demographics information are stored as integers, therefore researchers should consult Table 1 to retrieve the corresponding categorical labels. We also recommend researchers to apply other filtering methods, depending on the nature of their experiments, such as collapsing the ground-truth attention labels from the original 1–5 scale to a binary scale (Arapakis and Leiva, 2020 ; Arapakis et al, 2020 ) or ignoring cursor trajectories having <5 coordinates, which in most cases would correspond to 1 s of interaction data.…”

“…Two manuscripts using a post-processed version of this dataset have been recently published by the authors (Arapakis and Leiva, 2020 ; Arapakis et al, 2020 ). LAL acknowledged the support from the Finnish Center for Artificial Intelligence (FCAI).…”

A Survey of the Role of Viewability Within the Online Advertising Ecosystem

“…To reliably determine if certain viewability requirements are met (e.g., the percentage of ad pixels within the viewable space and the length of time the ad is in the viewable space of the browser), new types of data and analysis techniques are very likely to be utilized. Among others, computer-mouse data (e.g., clicks, movements and use of the scroll wheel) are very promising, albeit not a new alternative [6]. As early as 2013, Facebook claimed to use mouse tracking to learn how long a user's cursor hovered over a certain part of its website, or whether a user's newsfeed was visible at a given moment on their screen [119].…”

“…As early as 2013, Facebook claimed to use mouse tracking to learn how long a user's cursor hovered over a certain part of its website, or whether a user's newsfeed was visible at a given moment on their screen [119]. One of the disadvantages of mouse tracking is that it is very difficult to prevent while browsing the web today, especially because it can be implemented silently at scale, in incognito mode, and even without JavaScript enabled [6].…”