In the marketing sector the growing wealth of data on individual purchase behaviour, online activity, social media and socio-demographic profiles is changing the face of media buying and analytics. Firstly, by consolidating the information into single data management platforms, media agencies can achieve more granular segmentation of viewing audiences, leading to increasing efficiency in digital media buying and targeting. Secondly, the sheer volume and complexity of available data has prompted an increasing use of machine learning methods to generate marketing insights. This typically covers correlation-based model building, network analysis, consumer segmentation, classification and forecasting.
Against this backdrop, more conventional ‘small data’ analytics such as econometrics and marketing mix modelling have taken something of a back seat. This is unfortunate since big data should not necessarily be viewed as more accurate with no need for conventional interpretation. Data mining techniques for example are ideal for association rule learning, where customer purchasing patterns on frequent and jointly purchased products can help guide marketing strategy. However, such methods shed little light on underlying data generation processes, marketing ROI and the causal impacts central to simulation and scenario planning. Consequently, despite the significant role that unstructured big data can play in marketing analytics it should not overshadow the importance of traditional analysis of large structured data sets. The key is to build analytical frameworks that can harness the value of increasing data size, yet retain the benefits of sound economic theory and valid causal inference. This is the domain of Big Data econometrics.