Probabilistic Attribution Models for Digital Out-of-Home Advertising: A Design Science Approach to Bridging Physical Exposure and Digital Behavior

Abstract

However‚ since no end-user interaction such as a click or impression exists within a public digital Out-of-Home advertising environment‚ the article presents a probabilistic attribution framework for linking offline advertisement exposures to observable end-user digital behavior through defined geographical regions of exposure․ Using DSR methodology‚ we construct and validate a spatial-temporal modeling framework that utilizes geolocation signals‚ sensor data harvested from devices of subjects and privacy-aware inference algorithms․ Within this framework‚ a probabilistic viewability fence concept introduces spatial and temporal constraints on the inferred exposure while employing quality filters including dwell time‚ device orientation‚ and movement patterns․ Comparative validation with attribution modeling against benchmarks set by location-based and machine learning models shows that multi-dimensional probabilistic exposure inference is applicable and effective․ The framework thus turns DOOH into an accountable advertising medium‚ as opposed to the pure brand building medium‚ allowing cross-channel comparison of campaigns and data-driven actions by marketers․ This article contributes to the probabilistic attribution theory for non-interactive environments and gives a practical architecture to connect sample-based behavior offline and online․