Abstract
A user’s digital profile is a summary of the consumer’s interests and preferences revealed through the consumer’s online activity. It is a fundamental component of numerous applications in digital marketing. McKinsey & Company regards online user profiling as a promising opportunity that companies should leverage to unlock the potential of Big Data. This paper discusses a modelling approach that uncovers individual user profiles from online surfing data and allows online businesses to make profile predictions when only limited information is available. The approach is easily parallelised and scales well for processing massive records of user online activity. The paper demonstrates the application of the authors’ approach to display advertising. Using economic simulation it shows the potential gains the approach may offer to a firm if used for targeting display ads at the individual level.
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