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With an increase in the potential to allocate financial online advertising spending, managers are facing a sophisticated decision and allocation process. We developed a binary logit model with a Bayesian mixture approach to address consumers' buying decision processes and to account for the effects of multiple online advertising channels. By analyzing data from a medium-sized online mail order business, we found inherent differences in the effects of consumer clicks on purchasing probabilities across multiple advertising channels. We developed an alternative approach to account for the different attribution of success of advertising channels — the average success probability (ASP). Compared to standardized metrics, we found paid search advertising to be overestimated and retargeting display advertising to be underestimated. We further found that the mixture approach is useful for considering heterogeneity in the individual propensity of consumers to purchase; for the majority of consumers (more than 90%), repeated clicks on advertisements decrease their probability of purchasing. In contrast with this segment, we found a smaller segment of consumers (nearly 10%) whose clicks on advertisements increase conversion probabilities. Our approaches will help managers to better understand consumer online search and buying behavior over time and to allocate financial spending more efficiently across multiple types of online advertising.

Which Clicks Lead to Conversions? - Modeling User-journeys Across Multiple Types of Online Advertising