Digital media attribution aims to identify the combination of online marketing activities and touchpoints contributing to online sales conversion. Given the availability of unique user-identifiers, analysis conventionally traces the actions of single individuals. Traditional media attribution, on the other hand, evaluates the offline sales impact of offline marketing investments. Measurement is typically carried out at an aggregated level, using marketing mix analysis applied to groups of consumers, either at store, chain or market level. With the advent of multi-channel marketing, comes the need to measure the sales impact of inherently micro-focused digital media alongside more macro-oriented traditional advertising. Consequently, any analytical approach that aims to incorporate both elements inevitably involves a degree of data aggregation or disaggregation depending on whether we adopt a macro or a micro route.
This article presents an aggregate modelling framework for traditional and digital marketing attribution. The model structure is based on a theory of consumer purchase behaviour that naturally combines off and online marketing touchpoints, with response parameters estimated using appropriate dynamic econometric techniques. Outputs provide many managerial benefits, ranging from accurate ROI and media planning inputs through to simulation and demand forecasting.