User Acquisition and Engagement in Digital News Media

Generating revenue has been a major issue for the news industry and journalism over the past decade. In fact, vast availability of free online news sources causes online news media agencies to face user acquisition and engagement as pressing issues more than before. Although digital news media agencies are seeking sustainable relationships with their users, their current business models do not satisfy this demand. As a matter of fact, they need to understand and predict how much an article can engage a reader as a crucial step in attracting readers, and then maximize the engagement using some strategies. Moreover, news media companies need effective algorithmic tools to identify users who are prone to subscription. Last but not least, online news agencies need to make smarter decisions in the way that they deliver articles to users to maximize the potential benefits. In this dissertation, we take the first steps towards achieving these goals and investigate these challenges from data mining /machine learning perspectives. First, we investigate the problem of understanding and predicting article engagement in terms of dwell time as one of the most important factors in digital news media. In particular, we design data exploratory models studying the textual elements (e.g., events, emotions) involved in article stories, and find their relationships with the engagement patterns. In the prediction task, we design a framework to predict the article dwell time based on a deep neural network architecture which exploits the interactions among important elements (i.e., augmented features) in the article content as well as the neural representation of the content to achieve the better performance. In the second part of the dissertation, we address the problem of identifying valuable visitors who are likely to subscribe in the future. We suggest that the decision for subscription is not a sudden, instantaneous action, but it is the informed decision based on positive experience with the newspaper. As such, we propose effective engagement measures and show that they are effective in building the predictive model for subscription. We design a model that predicts not only the potential subscribers but also the time that a user would subscribe. In the last part of this thesis, we consider the paywall problem in online newspapers. The traditional paywall method offers a non-subscribed reader a fixed number of free articles in a period of time (e.g., a month), and then directs the user to the subscription page for further reading. We argue that there is no direct relationship between the number of paywalls presented to readers and the number of subscriptions, and that this artificial barrier, if not used well, may disengage potential subscribers and thus may not well serve its purpose of increasing revenue. We propose an adaptive paywall mechanism to balance the benefit of showing an article against that of displaying the paywall (i.e., terminating the session). We first define the notion of cost and utility that are used to define an objective function for optimal paywall decision making. Then, we model the problem as a stochastic sequential decision process. Finally, we propose an efficient policy function for paywall decision making. All the proposed models are evaluated on real datasets from The Globe and Mail which is a major newspaper in Canada. However, the proposed techniques are not limited to any particular dataset or strict requirement. Alternatively, they are designed based on the datasets and settings which are available and common to most of newspapers. Therefore, the models are general and can be applied by any online newspaper to improve user engagement and acquisition

Three Essays on Big Data Consumer Analytics in E-Commerce

Consumers are increasingly spending more time and money online. Business to consumer e-commerce is growing on average of 20 percent each year and has reached 1.5 trillion dollars globally in 2014. Given the scale and growth of consumer online purchase and usage data, firms\u27 ability to understand and utilize this data is becoming an essential competitive strategy. But, large-scale data analytics in e-commerce is still at its nascent stage and there is much to be learned in all aspects of e-commerce. Successful analytics on big data often require a combination of both data mining and econometrics: data mining to reduce or structure (from unstructured data such as text, photo, and video) large-scale data and econometric analyses to truly understand and assign causality to interesting patterns. In my dissertation, I study how firms can better utilize big data analytics and specific applications of machine learning techniques for improved e-commerce using theory-driven econometrical and experimental studies. I show that e-commerce managers can now formulate data-driven strategies for many aspect of business including cross-selling via recommenders on sales sites to increasing brand awareness and leads via social media content-engineered-marketing. These results are readily actionable with far-reaching economical consequences

Machine learning applications in operations management and digital marketing

In this dissertation, I study how machine learning can be used to solve prominent problems in operations management and digital marketing. The primary motivation is to show that the application of machine learning can solve problems in ways that existing approaches cannot. In its entirety, this dissertation is a study of four problems—two in operations management and two in digital marketing—and develops solutions to these problems via data-driven approaches by leveraging machine learning. These four problems are distinct, and are presented in the form of individual self-containing essays. Each essay is the result of collaborations with industry partners and is of academic and practical importance. In some cases, the solutions presented in this dissertation outperform existing state-of-the-art methods, and in other cases, it presents a solution when no reasonable alternatives are available. The problems are: consumer debt collection (Chapter 3), contact center staffing and scheduling (Chapter 4), digital marketing attribution (Chapter 5), and probabilistic device matching (Chapters 6 and 7). An introduction of the thesis is presented in Chapter 1 and some basic machine learning concepts are described in Chapter 2