In-depth analysis of publishers in travel affiliate marketing based on Aviasales data
Makarkina, Irina (2021)
Pro gradu -tutkielma
2021
School of Business and Management, Kauppatieteet
Kaikki oikeudet pidätetään.
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2021062139269
https://urn.fi/URN:NBN:fi-fe2021062139269
Tiivistelmä
The understanding of the underlying interconnections between the parties involved in the affiliate marketing i.e. advertisers and affiliates (publishers) gradually becomes a requirement for managers, who aim to implement and develop successful affiliate marketing strategies. Nevertheless, the studies and business cases devoted to this phenomenon are limited. This Master’s Thesis provides an in-depth analysis of the affiliates (publishers) part of the affiliate marketing based on the dataset of the Aviasales company.
In the literature review the notion and mechanism of affiliate marketing were discussed. Moreover, the overview of the affiliate marketing studies was presented together with the description of the previous attempts to categorize affiliates.
In the empirical part of the thesis methods of Machine Learning were applied. Thus, Natural Language Preprocessing was used to prepare affiliate websites data for further analysis. Clustering models (K-Means with PCA, DBScan) were applied to reveal the underlying data patterns. In terms of classification models (Gradient Boosting, CatBoost) the main types of affiliates were studied: content sites, service sites and cashback and promo code sites.
In terms of Aviasales data it was found that the content sites are the most widespread type of the affiliates. Moreover, cashback and promo code sites were defined as showing the most interest in participation in affiliate programs. Based on the findings, managerial implications and theoretical contribution of the work were given.
In the literature review the notion and mechanism of affiliate marketing were discussed. Moreover, the overview of the affiliate marketing studies was presented together with the description of the previous attempts to categorize affiliates.
In the empirical part of the thesis methods of Machine Learning were applied. Thus, Natural Language Preprocessing was used to prepare affiliate websites data for further analysis. Clustering models (K-Means with PCA, DBScan) were applied to reveal the underlying data patterns. In terms of classification models (Gradient Boosting, CatBoost) the main types of affiliates were studied: content sites, service sites and cashback and promo code sites.
In terms of Aviasales data it was found that the content sites are the most widespread type of the affiliates. Moreover, cashback and promo code sites were defined as showing the most interest in participation in affiliate programs. Based on the findings, managerial implications and theoretical contribution of the work were given.