Abstract
ABSTRACT Advertisers apply customer journey analyses to gain insights into customer behavior at various touchpoints and measure their advertising impact using attribution models. Along the customer journey, customers constantly adjust goals and expectations, generating new data that can influence attribution results. However, extant attribution models do not capture changes in customer behavior over time, which limits their results’ meaningfulness. In response, the authors present a dynamic approach to customer journey analysis based on Markov chains that updates attribution results on a rolling basis by sequentially considering new data. Applying this approach to a nine-year data set of 45,694 customer journeys leads to empirical generalizations and channel-specific insights. A model comparison shows that rolling determination increases the attribution accuracy and extends previous research. Furthermore, data collection periods influence advertising impact and should be considered in attribution modeling. Thus, the study enables advertisers to interactively manage their marketing strategy and improve budget allocation.
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