Combining similarity in time and space for training set formation under concept drift

Concept drift is a challenge in supervised learning for sequential data. It describes a phenomenon when the data distributions change over time. In such a case accuracy of a classifier benefits from the selective sampling for training. We develop a method for training set selection, particularly relevant when the expected drift is gradual. Training set selection at each time step is based on the distance to the target instance. The distance function combines similarity in space and in time. The method determines an optimal training set size online at every time step using cross validation. It is a wrapper approach, it can be used plugging in different base classifiers. The proposed method shows the best accuracy in the peer group on the real and artificial drifting data. The method complexity is reasonable for the field applications

Maximize What Matters: Predicting Customer Churn With Decision-Centric Ensemble Selection

Churn modeling is important to sustain profitable customer relationships in saturated consumer markets. A churn model predicts the likelihood of customer defection. This is important to target retention offers to the right customers and to use marketing resources efficiently. The prevailing approach toward churn model development, supervised learning, suffers an important limitation: it does not allow the marketing analyst to account for campaign planning objectives and constraints during model building. Our key proposition is that creating a churn model in awareness of actual business requirements increases the performance of the final model for marketing decision support. To demonstrate this, we propose a decision-centric framework to create churn models. We test our modeling framework on eight real-life churn data sets and find that it performs significantly better than state-of-the-art churn models. Further analysis suggests that this improvement comes directly from incorporating business objectives into model building, which confirms the effectiveness of the proposed framework. In particular, we estimate that our approach increases the per customer profits of retention campaigns by $.47 on average

DATA-DRIVEN PRODUCT RETURNS PREDICTION: A CLOUD-BASED ENSEMBLE SELECTION APPROACH

The number of product returns represents a considerable cost factor in e-commerce, especially in the apparel sector. The application of advanced information technologies and predictive analytics, enabling to capture and analyze massive amounts of user data, pave the way for a more efficient management of product returns and reverse logistics. However, we identify a lack of data-driven approaches in this area, especially regarding product returns prediction. In this paper, we present an ensemble selection approach for predicting product returns in the apparel sector. Computational experiments indicate that our approach produces satisfying results in terms of prediction quality. We further explore the correlation between sample sizes and computational times. Thereby, we demonstrate that the run-time increases exponentially when using more data records. To address heavy run-time overheads resulting from high processing and memory requirements of classifiers, we present a framework to embed ensemble selection processes into a highly scalable cloud environment. The framework explains the provisioning of cloud resources and parallelization of tasks according to ensemble selection processes. It further builds a basis for considering data streams, data splitting, and a dynamic adoption of changing customer behavior over time, which has not been considered in related work so far. The envisioned forecasting support system aids retailers in reducing product returns and increasing profit margins

The Digital Transformation of the News Media Business – Paid Content and Entrepreneurship in Digital Journalism

The digital transformation of the news business continues to agitate publishers. Concerned about declining sales in the print segment, legacy outlets, local news companies and freelance journalists alike search for ways to monetize digital journalism properly. At first glance, digital journalism and its monetisation as paid content seem a promising effort. The digitisation of the news business enabled distribution at a marginal cost of almost zero while giving journalists access to new research technologies and lowering the cost of entry for smaller companies. However, while digital journalism enjoys broad popularity and use, online news are gaining few paying customers. Furthermore, online news compete within a larger digital media complex, comprising movies, games, and social media. After 25 years of experimentation, the digital future of journalism is still heavily debated in media management. Concerning the reconstitution as a digital medium, this research examines conditions of success and obstacles for the digital news media business to be successful as a business venture. Therefore, the research question reads What factors enable the viability and entrepreneurial success of the news media business in light of the consequences of digital transformation? The overarching research question is considered from two angles: The first angle concerns the demand side by looking at the antecedents of the audience's willingness to pay for paid content. The second angle focuses on the supply side and therefore examines antecedents of success in the context of digital journalistic start-ups and founders. In four studies, this thesis develops an analysis of the online news business with a local focus on the German news market. For this purpose, a variety of methods ranging from qualitative work and literature review to empirical research employing path analysis and predictive analytics are applied. Theoretically, digital transformation, free mentality and other peculiarities of information goods inform the frame of this work. Thus, this research aims at contributing to a financially sustainable news media business