Author:
Florian Nottorf
Affiliation:
Leuphana University, Germany
Keyword(s):
Online Advertising, User-journey, Consumer Behavior, Purchasing Probabilities, Clickstream Data, Bayesian Analyis, Mixture of Normals.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Models
;
Business Process Management
;
Business-It Alignment
;
e-Business
;
Enterprise Engineering
;
Enterprise Information Systems
;
Knowledge Management and Information Sharing
;
Knowledge-Based Systems
;
Model Driven Development
;
Sustainable e-Business
;
Symbolic Systems
;
Technology Platforms
;
Transaction Support
Abstract:
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 consum
ers (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.
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