Prof. Dr. Michael Scholz
Prof. Scholz and his team are dedicated to the current challenges of electronic commerce with focus on designing e-commerce systems and investigating their economic impact. Our research projects aim at investigating intelligent e-commerce solutions such as recommendation systems, ranking systems or product configuration systems. We offer our students theoretically sound and practically relevant knowledge about the Internet economy, algorithms in modern e-commerce systems and the economic impact of these algorithms. We cooperate with several companies in order to ensure practical relevance of our work.
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Economics of Word of Mouth
Firms typically invest in marketing activities to promote novel goods and attract potential consumers. Consumers who have a sufficient willingness to pay recommend the good to others via word-of-mouth (WoM) communication. Both marketing activities and WoM determine demand. The economic effects of WoM, however, have been scarcely examined. Prof. Scholz, Dr. Wöhner and Prof. Peters developed a microeconomic propagation model to analyze the effects of WoM on the profit-maximizing price of a good and the optimal number of consumers targeted for advertising. They find that reducing the price relative to that in informed markets maximizes profit in an WoM scenario. The price reduction depends on WoM intensity. A moderate spread of WoM renders a considerable price reduction most profitable. The results therefore demonstrate that price is not only an instrument for optimally skimming consumers' willingness to pay and enforcing competition but also an instrument for increasing consumers' awareness of a good. The results also show that investing in WoM by lowering the price is more profitable than investing in mass advertising if there is a moderate spread of WoM.
R-Package to Analyze Clickstreams
More and more retailers collect, store, analyze and aggregate their customers' clickstreams. Prof. Scholz now has developed an R-package that supports the process of analyzing and aggregating clickstream data. The package called clickstream is based on Markov chains.