Digital Advertising in Physical Stores: Measuring Impressions in Digital Out-of-Home Advertising
Keywords:
Digital Out-Of-Home Advertising, Impression Measurement, Opportunity-To-See, Audience Analytics, Programmatic AdvertisingAbstract
Digital Out-of-Home (DOOH) advertising combines multimedia display technology, computer vision, and real-time audience profiling in retail and public environments. DOOH measurement does not follow the deterministic methodology of online digital media measurement. DOOH publisher measurements are based on probabilistic inference of sensor fusion, geometry, and behavior rather than deterministic impressions. This paper provides a technical survey of DOOH impression measurement methods. We focus on multimedia sensing, real-time processing, and data-driven optimization in DOOH systems. In this section we discuss four parts of the measurement pipeline. First, we consider traffic volume measurement using optical and wireless traffic fingerprints, dwell time assessment using computer vision and projected ultrasonic waves, demographic models using statistical classification, and opportunity-to-see (OTS) estimation using a mathematical model of attention-weighted exposure probabilities. We also discuss how new technologies, such as state-of-the-art gaze estimation via deep learning, mobile location analytics and programmatic advertising platforms, can assist in measuring and optimizing ad campaigns. These challenges relate to environmental factors affecting sensor performance, noisy crowds, privacy-preserving computation, and the lack of industry measurement standards. The paper suggests that the implementation of a transparent‚ auditable and standardized infrastructure for measuring impressions will allow strong‚ reliable and verifiable measurement. It argues that this will enable the DOOH industry to flourish as a reliable data-driven medium.
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