Quantitative Analyses in Digital Marketing and Business Intelligence

This work is divided into two parts. The first part consists of four essays on questions in digital marketing; this term refers to all marketing activities on the Internet, regardless of whether they primarily address users of stationary devices (e.g., a desktop PC) or users of mobile devices (e.g., a smartphone). In Essay I, we model the time it takes until an item that is offered in the popular buy-it-now offer format is sold. Our model allows drawing inference from the observation of this time on how many consumers are interested in the item and on how much they value it. By this approach, several problems can be bypassed that often arise when these factors are estimated from data on items that are offered in an auction. We demonstrate the application of our model by an example. Essay II investigates which effects ads that are displayed on search engine results pages have on the click behavior and the purchase behavior of users. For this purpose, a model and a corresponding decision rule are developed and applied to a dataset that we have obtained in a field experiment. The results show that search engine advertising can be beneficial even for search queries for which the website of the advertising firm already ranks high among the regular, so-called organic search results, and even for users who already search with one of the firm’s brand names. In Essay III, we argue theoretically and show empirically that online product ratings by customers do not represent the rated product’s quality, as it has been assumed in previous studies, but rather the customers’ satisfaction with the product. Customer satisfaction does not only depend on product quality as observed after the purchase but also on the expectations the customers had of the product before the purchase. Essay IV investigates the relationship between the offline and the mobile content delivery channel. For this purpose, we study whether a publisher can retain existing subscribers to a print medium longer if he offers a mobile app through which a digital version of the print medium can be accessed. The application of our model to the case of a respected German daily newspaper confirms the existence of such an effect, which indicates a complementary relationship between the two content delivery channels. We analyze how this relationship affects the value of a customer to the publisher. The second part of this work consists of three essays that explore various approaches for simplifying the use of business intelligence (BI) systems. The necessity of such a simplification is emphasized by the fact that BI systems are nowadays employed for the analysis of more and more heterogeneous data than in the past, especially transactional data. This has also extended their audience, which now also includes inexperienced knowledge workers. Essay V analyzes by an experiment that we have conducted among knowledge workers from different firms how the presentation of data in a BI system affects how fast and how accurate the system users answer typical tasks. With regard to this, we compare the three currently most common data models: the multidimensional one, the relational one, and the flat one. The results show that it depends on the type of the task considered which of these data models supports users best. In Essay VI, a framework for the integration of an archiving component into a BI system is developed. Such a component can identify and automatically archive reports that have become irrelevant. This is in order to reduce the system users’ effort associated with searching for relevant reports. We show by a simulation study that the proposed approach of estimating the reports’ future relevance from the log files of the BI system’s search component (and other data) is suitable for this purpose. In Essay VII, we develop a reference algorithm for searching documents in a firm context (such as reports in a BI system). Our algorithm combines aspects of several search paradigms and can easily be adapted by firms to their specificities. We evaluate an instance of our algorithm by an experiment; the results show that it outperforms traditional algorithms with regard to several measures. The work begins with a synopsis that gives further details on the essays

Cyber-crime Science = Crime Science + Information Security

Cyber-crime Science is an emerging area of study aiming to prevent cyber-crime by combining security protection techniques from Information Security with empirical research methods used in Crime Science. Information security research has developed techniques for protecting the confidentiality, integrity, and availability of information assets but is less strong on the empirical study of the effectiveness of these techniques. Crime Science studies the effect of crime prevention techniques empirically in the real world, and proposes improvements to these techniques based on this. Combining both approaches, Cyber-crime Science transfers and further develops Information Security techniques to prevent cyber-crime, and empirically studies the effectiveness of these techniques in the real world. In this paper we review the main contributions of Crime Science as of today, illustrate its application to a typical Information Security problem, namely phishing, explore the interdisciplinary structure of Cyber-crime Science, and present an agenda for research in Cyber-crime Science in the form of a set of suggested research questions

Robust and cheating-resilient power auctioning on Resource Constrained Smart Micro-Grids

The principle of Continuous Double Auctioning (CDA) is known to provide an efficient way of matching supply and demand among distributed selfish participants with limited information. However, the literature indicates that the classic CDA algorithms developed for grid-like applications are centralised and insensitive to the processing resources capacity, which poses a hindrance for their application on resource constrained, smart micro-grids (RCSMG). A RCSMG loosely describes a micro-grid with distributed generators and demand controlled by selfish participants with limited information, power storage capacity and low literacy, communicate over an unreliable infrastructure burdened by limited bandwidth and low computational power of devices. In this thesis, we design and evaluate a CDA algorithm for power allocation in a RCSMG. Specifically, we offer the following contributions towards power auctioning on RCSMGs. First, we extend the original CDA scheme to enable decentralised auctioning. We do this by integrating a token-based, mutual-exclusion (MUTEX) distributive primitive, that ensures the CDA operates at a reasonably efficient time and message complexity of O(N) and O(logN) respectively, per critical section invocation (auction market execution). Our CDA algorithm scales better and avoids the single point of failure problem associated with centralised CDAs (which could be used to adversarially provoke a break-down of the grid marketing mechanism). In addition, the decentralised approach in our algorithm can help eliminate privacy and security concerns associated with centralised CDAs. Second, to handle CDA performance issues due to malfunctioning devices on an unreliable network (such as a lossy network), we extend our proposed CDA scheme to ensure robustness to failure. Using node redundancy, we modify the MUTEX protocol supporting our CDA algorithm to handle fail-stop and some Byzantine type faults of sites. This yields a time complexity of O(N), where N is number of cluster-head nodes; and message complexity of O((logN)+W) time, where W is the number of check-pointing messages. These results indicate that it is possible to add fault tolerance to a decentralised CDA, which guarantees continued participation in the auction while retaining reasonable performance overheads. In addition, we propose a decentralised consumption scheduling scheme that complements the auctioning scheme in guaranteeing successful power allocation within the RCSMG. Third, since grid participants are self-interested we must consider the issue of power theft that is provoked when participants cheat. We propose threat models centred on cheating attacks aimed at foiling the extended CDA scheme. More specifically, we focus on the Victim Strategy Downgrade; Collusion by Dynamic Strategy Change, Profiling with Market Prediction; and Strategy Manipulation cheating attacks, which are carried out by internal adversaries (auction participants). Internal adversaries are participants who want to get more benefits but have no interest in provoking a breakdown of the grid. However, their behaviour is dangerous because it could result in a breakdown of the grid. Fourth, to mitigate these cheating attacks, we propose an exception handling (EH) scheme, where sentinel agents use allocative efficiency and message overheads to detect and mitigate cheating forms. Sentinel agents are tasked to monitor trading agents to detect cheating and reprimand the misbehaving participant. Overall, message complexity expected in light demand is O(nLogN). The detection and resolution algorithm is expected to run in linear time complexity O(M). Overall, the main aim of our study is achieved by designing a resilient and cheating-free CDA algorithm that is scalable and performs well on resource constrained micro-grids. With the growing popularity of the CDA and its resource allocation applications, specifically to low resourced micro-grids, this thesis highlights further avenues for future research. First, we intend to extend the decentralised CDA algorithm to allow for participants’ mobile phones to connect (reconnect) at different shared smart meters. Such mobility should guarantee the desired CDA properties, the reliability and adequate security. Secondly, we seek to develop a simulation of the decentralised CDA based on the formal proofs presented in this thesis. Such a simulation platform can be used for future studies that involve decentralised CDAs. Third, we seek to find an optimal and efficient way in which the decentralised CDA and the scheduling algorithm can be integrated and deployed in a low resourced, smart micro-grid. Such an integration is important for system developers interested in exploiting the benefits of the two schemes while maintaining system efficiency. Forth, we aim to improve on the cheating detection and mitigation mechanism by developing an intrusion tolerance protocol. Such a scheme will allow continued auctioning in the presence of cheating attacks while incurring low performance overheads for applicability in a RCSMG

Search Engine Advertising Adoption and Utilization: An Empirical Investigation of Inflectional Factors

© Taylor & Francis Group, LLC. Search engine advertising (SEA) is a prominent source of revenue for search engine companies, and also a solution for businesses to promote their visibility on the web. However, there is little academic research available about the factors and the extent to which they may influence businesses’ decision to adopt SEA. Building on Theory of Planned Behavior, Technology Acceptance Model, and Unified Theory of Acceptance and Use of Technology, this study develops a context-specific model for understanding the factors that influence the decision of businesses to use SEA. Using structural equation modeling and survey data collected from 142 businesses, this research finds that the intention of businesses to use SEA is directly influenced by four factors: (i) attitude toward SEA, (ii) subjective norms, (iii) perceived control over SEA, and (iv) perceived benefits of SEA in terms of increasing web traffic, increasing sales and creating awareness. Furthermore, the research we discover six additional factors that have an indirect influence: (i) trust in search engines, (ii) perceived risk of SEA, (iii) ability to manage keywords and bids, (iv) ability to analyze and monitor outcomes, (v) advertising expertise, and (vi) using external experts

Exchange Automation and Adaptive Efficiency at the Kenyan Securities Market

Developments in electronic trading has played an increasing role in changing the microstructure of securities markets. Worldwide, securities exchanges are gradually replacing their traditional physically convened markets with electronic markets. In order to contribute to wealth maximization objective of investors and economic growth, securities markets need to be efficient in terms of price discovery process. The Nairobi Securities Exchange has automated it operations by installing an automated trading and depository systems to improve its efficiency. Information is however lacking on how these changes have affected the informational efficiency of the Exchange. This study tried to determine whether the automation of the Exchange had improved its informational efficiency. Using secondary data collected from the Exchange on share prices for computing an All Share Index between 1994 and 2019, non-parametric approaches were used to measure market efficiency before and after market automation. The results show that market returns in the post-automation period were higher and more volatile than those in the pre-automation period. The higher returns can be attributed to improved price discovery process, while the higher volatility may be due to changes in market microstructure through use of electronic systems. While Normality tests indicate that returns are not normally distributed in both the periods, Runs test results reveal that returns are more random in the period following automation than the prior period, implying that the market has improved in efficiency. The introduction of automation in the Kenyan securities market has thus led to improved market efficiency, providing support for the adaptive market hypothesis. The Exchange should consider pursuing full market automation by enabling online and internet securities trading and use of mobile money transfer platforms in paying for stock transactions, in addition to the adoption of margin trading and a hybrid trading system (call and continuous) – to enhance liquidity and transparency in trading. Keywords: Automated Trading System, Central Depository System, Adaptive Market Efficiency, Market Microstructure, Nairobi Securities Exchange. JEL Classification: G14; G15 DOI: 10.7176/EJBM/12-15-06 Publication date:May 31st 202

Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting

The most significant progress in recent years in online display advertising is what is known as the Real-Time Bidding (RTB) mechanism to buy and sell ads. RTB essentially facilitates buying an individual ad impression in real time while it is still being generated from a user’s visit. RTB not only scales up the buying process by aggregating a large amount of available inventories across publishers but, most importantly, enables direct targeting of individual users. As such, RTB has fundamentally changed the landscape of digital marketing. Scientifically, the demand for automation, integration and optimisation in RTB also brings new research opportunities in information retrieval, data mining, machine learning and other related fields. In this monograph, an overview is given of the fundamental infrastructure, algorithms, and technical solutions of this new frontier of computational advertising. The covered topics include user response prediction, bid landscape forecasting, bidding algorithms, revenue optimisation, statistical arbitrage, dynamic pricing, and ad fraud detection

Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting

The most significant progress in recent years in online display advertising is what is known as the Real-Time Bidding (RTB) mechanism to buy and sell ads. RTB essentially facilitates buying an individual ad impression in real time while it is still being generated from a user’s visit. RTB not only scales up the buying process by aggregating a large amount of available inventories across publishers but, most importantly, enables direct targeting of individual users. As such, RTB has fundamentally changed the landscape of digital marketing. Scientifically, the demand for automation, integration and optimisation in RTB also brings new research opportunities in information retrieval, data mining, machine learning and other related fields. In this monograph, an overview is given of the fundamental infrastructure, algorithms, and technical solutions of this new frontier of computational advertising. The covered topics include user response prediction, bid landscape forecasting, bidding algorithms, revenue optimisation, statistical arbitrage, dynamic pricing, and ad fraud detection