Neues Research Paper erschienen: Controlling and Evaluating Affiliates
– an Exploratory Research in the Education Sector
Soeben ist das gemeinsam mit Prof. Dr. Rainer Olbrich und Patrick Bormann erarbeitete Research Paper erschienen: „Controlling and Evaluating Affiliates
– an Exploratory Research in the Education Sector„. Zum Einsatz kamen der Two-Step Clustering Approach in Kombination mit dem Kruskall-Wallis-H Test.
Im Folgenden werden unsere Forschungsfrage und zentrale Ergebnisse vorgestellt – größtenteils in Englisch.
Die zentrale Forschungsfrage lautet:
Welche Faktoren sind relevant zur Steuerung, Kontrolle und Bewertung von Affiliate-Partnern?
We seek to understand the affiliate marketing process in detail by identifying and grouping affiliates’ pre-economic behavior, both on their own websites and social media channels, before analyzing some key performance indicators. In addition, we address users’ perceptions of advertising channels and their influence on affiliate marketing.
Daten: 2 Affiliate-Netzwerke, facebook und twitter
We use data of an affiliate marketing campaign by a private education merchant which includes 329 affiliates. The dataset contains 179,338 unique clicks by users who visited the merchant’s website. 5,340 Leads were generated.
That means 5,340 users ordered further education information about the merchant’s service. 82 users finally signed up for the service of the merchant, and triggered a sale. Each sale brings the merchant a marginal income of 3.000 €. Every affiliate is paid on a 5 € pay-per-lead basis. Thus, the merchant paid nearly 25,000 € for this affiliate marketing campaign.
Überblick zentrale Ergebnisse
Controlling and Evaluating Different Affiliate Types
– It is rare for academic research to include data from multiple affiliate
networks and social networks.
Literature Overview on Affiliate Marketing
– Several topics have been examined in prior literature: trust, the impact of network size on search engine rankings, perceived usefulness of affiliates, moral hazard, episodic complementary goods, product complexity, and involvement.
– Remaining issues include social networks, different affiliate types, and the
focus of affiliate websites.
Data Analysis and Clustering Approach
– Affiliates use text ads more often than banner ads. Search engines dominate
perceived advertising channels (Descriptive Results 3.3).
– If the website focus of an affiliate is thematically unlike the offer of the merchant, nearly all the social activity of the affiliate is high
(Clustering Approach 3.4).
– Affiliates with the most ads, most social activity, and most clicks and leads do not behave as expected; for example, they do not achieve many sales
(Clustering Approach 3.4).
– Clusters with many affiliates do not necessarily achieve better key performance indicators. For example, the largest cluster of thematically related affiliates has no more sales than a thematically unrelated cluster.
– Merchants should work with affiliates that exhibit different focuses in their thematic websites (Managerial Implications 5.1).
– Merchants could work together with affiliates that use social networks, though with this approach, competition increases for the merchant
(Managerial Implications 5.1).
– Further research should consider affiliates’ business models, users’ recommendation behavior, users’ navigation, the behaviors of fraudulent affiliates, consumer goods settings, and other payment models
(Limitations and Further Research 5.2).