Abstract
With a surge in communication channels increasing the complexity of today’s media landscape, companies face new challenges concerning the allocation of their advertising budget. As consumers become increasingly more autonomous in gathering information from the channels they deem most suitable, they encounter several touchpoints on their customer journey. Marketers struggle with the assessment of channel effectiveness. Despite a rise in research on the topic of attribution, findings and methodology vary greatly regarding variables and outcomes. The question of how to determine suitable attribution modeling that optimizes advertising effectiveness thus remains unanswered. This article aims at assessing which factors influence channel effectiveness in the context of high-involvement goods. Based on a unique dataset from a multinational car manufacturer, a Structural Vector Autoregressive model has been formulated revealing channel interactions, lagged effects of advertising and conversion funnel stages as being highly influential factors concerning channel effectiveness.