The analysis of gaining, retaining and maintaining customer trust is a highly topical issue in the e-commerce industry to mitigate the challenges of increased competition and volatile customer relationships as an effect of the increasing use of the internet to purchase goods. This study is conducted at the Swedish online fashion retailer NA-KD with the aim of gaining better insight into customer behavior that determines purchases, returns and churn. Therefore, the objectives for this study are to identify the group of loyal customers as well as construct models to predict customer loyalty, frequent returns and customer churn. Two separate approaches are used for solving the problem where a clustering model is constructed to divide the data into different customer segments that can explain customer behaviour. Then a classification model is constructed to classify the customers into the classes of churners, returners and loyal customers based on the exploratory data analysis and previous insights and knowledge from the company. By using the unsupervised machine learning method K-prototypes clustering for mixed data, six clusters are identified and defined as churned, potential, loyal customers and Brand Champions, indecisive shoppers, and high-risky churners. The supervised classification method of bias reduced binary Logistic Regression is used to classify customers into the classes of loyal customers, customers of frequent returns and churners. The final models had an accuracy of 0.68, 0.75 and 0.98 for the three separate binary classification models classifying Churners, Returners and Loyalists respectively.

Treatment for lymphoma with alkylating therapy is known to increase the risk of secondary malignancies such as Acute Myeloid Leukemia (AML), although the risk is not fully understood. This study investigates the characteristics of AML that arise after lymphoma treatment in contrastto AML cases without a prior lymphoma. The study population consists of 115 individuals identified from the Swedish lymphoma register (SLR) with a diagnosis in the quality register for AML between 2000-2019, matched 1:1 to lymphoma-free comparators. A hierarchical clusteranalysis with Gower’s similarity measure and the k-prototypes clustering algorithm are employed to separately identify subgroups of those with a lymphoma history and the matched comparators. The survival of lymphoma patients is compared between subgroups in a Cox regression model. The findings suggests a two-cluster partition achieved by the hierarchical method for patients with a lymphoma history as well as for lymphoma-free patients (average Silhouette 0.853 and0.842, respectively). Both partitions completely separates patients with genetic information from those without. For AML patients with a preceding lymphoma, a subgroup defined by the hierarchical two-cluster partition is associated with an increased mortality rate (HR 2.40). A three-cluster partition achieved by the k-prototypes algorithm could be more clinically relevant, however only one subgroup is associated with increased mortality (HR 2.73).