Towards a User Privacy-Aware Mobile Gaming App Installation Prediction Model

Over the past decade, programmatic advertising has received a great deal of attention in the online advertising industry. A real-time bidding (RTB) system is rapidly becoming the most popular method to buy and sell online advertising impressions. Within the RTB system, demand-side platforms (DSP) aim to spend advertisers' campaign budgets efficiently while maximizing profit, seeking impressions that result in high user responses, such as clicks or installs. In the current study, we investigate the process of predicting a mobile gaming app installation from the point of view of a particular DSP, while paying attention to user privacy, and exploring the trade-off between privacy preservation and model performance. There are multiple levels of potential threats to user privacy, depending on the privacy leaks associated with the data-sharing process, such as data transformation or de-anonymization. To address these concerns, privacy-preserving techniques were proposed, such as cryptographic approaches, for training privacy-aware machine-learning models. However, the ability to train a mobile gaming app installation prediction model without using user-level data, can prevent these threats and protect the users' privacy, even though the model's ability to predict may be impaired. Additionally, current laws might force companies to declare that they are collecting data, and might even give the user the option to opt out of such data collection, which might threaten companies' business models in digital advertising, which are dependent on the collection and use of user-level data. We conclude that privacy-aware models might still preserve significant capabilities, enabling companies to make better decisions, dependent on the privacy-efficacy trade-off utility function of each case.Comment: 11 pages, 3 figure

Multi-touch attribution in the mobile gaming industry

User acquisition spend is a big investment for mobile gaming companies. Because of the large scale, even small improvements in how this spend is allocated can provide big returns. To allocate advertising spend well; it is important that the credit of a conversion be attributed as accurately as possible. The current attribution model - standard to the industry - is a last-touch attribution model, which attributes 100% of the credit to the last touch-point. However, before a user installs a game they might see ads from multiple channels that might all affect the user’s propensity to install. With the last-touch attribution model, the uplift of these ads is not observed which skews the returns on advertising spent for different channels. This study looks at how install probability develops as impressions per user increase, how long the effect of an ad lasts and attempts to find better attribution models that attribute credit better than the last-touch model. Three multi-touch attribution models are proposed; two based on the Shapley value and one based on the ad effect time decay of different channels. The data for this study comes from a mobile gaming company and consists of impressions seen by both installed and non-installed users as well as impression channels, impression time and install time. The data was collected during a 38-day period and has data from 44,719,217 users who were divided into a training set and a test set with a 70%/30% split. The test set is used to validate the proposed models against the last-touch attribution model by using the models trained on the training set to generate predictions on install probability for user paths in the test data set. The study finds that the ad effect of all channels declines very quickly after the first day and is almost zero at seven days after the impression. The study also attempts to find the correlation between install probability and the amount of impressions a user has seen. Regarding this objective, the study is inconclusive. This correlation behaves very differently between different channels and because the amount of impressions per users could not be controlled for, it is difficult to deduce causation. Out of the three proposed attribution models, only one is able to outperform the last-touch model when it comes to predicting install probabilities from the training set’s paths. The model that outperformed is a Shapley value based model that considers the times of impressions for each path when calculating credit attribution. Finally, the study finds that only 9.5% of observed installs had impressions from more than one channel during a seven-day attribution window. This combined with the difficulty of validating attribution models based on return on advertising spend means that developing a multi-touch attribution model probably is not a very low hanging fruit for performance marketers. What would be worth looking into would be to test optimizing the frequency of ads shown to users

Supply Side Optimisation in Online Display Advertising

On the Internet there are publishers (the supply side) who provide free contents (e.g., news) and services (e.g., email) to attract users. Publishers get paid by selling ad displaying opportunities (i.e., impressions) to advertisers. Advertisers then sell products to users who are converted by ads. Better supply side revenue allows more free content and services to be created, thus, benefiting the entire online advertising ecosystem. This thesis addresses several optimisation problems for the supply side. When a publisher creates an ad-supported website, he needs to decide the percentage of ads first. The thesis reports a large-scale empirical study of Internet ad density over past seven years, then presents a model that includes many factors, especially the competition among similar publishers, and gives an optimal dynamic ad density that generates the maximum revenue over time. This study also unveils the tragedy of the commons in online advertising where users' attention has been overgrazed which results in a global sub-optimum. After deciding the ad density, the publisher retrieves ads from various sources, including contracts, ad networks, and ad exchanges. This forms an exploration-exploitation problem when ad sources are typically unknown before trail. This problem is modelled using Partially Observable Markov Decision Process (POMDP), and the exploration efficiency is increased by utilising the correlation of ads. The proposed method reports 23.4% better than the best performing baseline in the real-world data based experiments. Since some ad networks allow (or expect) an input of keywords, the thesis also presents an adaptive keyword extraction system using BM25F algorithm and the multi-armed bandits model. This system has been tested by a domain service provider in crowdsourcing based experiments. If the publisher selects a Real-Time Bidding (RTB) ad source, he can use reserve price to manipulate auctions for better payoff. This thesis proposes a simplified game model that considers the competition between seller and buyer to be one-shot instead of repeated and gives heuristics that can be easily implemented. The model has been evaluated in a production environment and reported 12.3% average increase of revenue. The documentation of a prototype system for reserve price optimisation is also presented in the appendix of the thesis

The utilization of artificial intelligence in online advertising and its perceived effectiveness

This study explores the utilization of Artificial Intelligence in online advertising process and the impact of using AI each stage in that process with the overall perceived effectiveness. It also provides a better understanding of the magnitude of using AI in the four stages of advertising online: namely consumer insights, ad creation, media planning and buying, and finally ad evaluation. Process model of AI utilization in online advertising is the conceptual model of the study, which is developed from the previous literature. A triangulation methodology is implemented to enhance the credibility of the research study and leads to a more comprehensive understanding of the topic. Online survey is conducted with digital advertisers worldwide from both agency and client side. Nonrandom sampling (N=60) was implemented to test 5 constructs from the perspective of the respondents. Three in-depth interviews were also conducted before and after the online questionnaire to analyze the findings and results and demonstrate insights on the five proposed research questions. Findings of the study showed beyond doubt that AI is stepping strongly and progressively in the four stages of the data-based online advertising process. Moreover, it significantly showed that there is a relationship between AI utilization in each stage and the following one. Finally, results indicated that using AI in each advertising stage promotes the perceived effectiveness of the overall online ad process

Understanding and supporting mobile application usage

In recent years mobile phones have evolved significantly. While the very first cellular phones only provided functionality for conducting phone calls, smartphones nowadays provide a rich variety of functionalities. Additional hardware capabilities like new sensors (e.g.~for location) and touch screens as new input devices gave rise to new use cases for mobile phones, such as navigation support, taking pictures or making payments. Mobile phones not only evolved with regard to technology, they also became ubiquitous and pervasive in people\u27s daily lives by becoming capable of supporting them in various tasks. Eventually, the advent of mobile application stores for the distribution of mobile software enabled the end-users themselves to functionally customize their mobile phones for their personal purposes and needs. So far, little is known about how people make use of the large variety of applications that are available. Thus, little support exists for end-users to make effective and efficient use of their smartphones given the huge numbers of applications that are available. This dissertation is motivated by the evolution of mobile phones from mere communication devices to multi-functional tool sets, and the challenges that have arisen as a result. The goal of this thesis is to contribute systems that support the use of mobile applications and to ground these systems\u27 designs in an understanding of user behavior gained through empirical observations. The contribution of this dissertation is twofold: First, this work aims to understand how people make use of, organize, discover and multitask between the various functionalities that are available for their smartphones. Findings are based on observations of user behavior by conducting studies in the wild. Second, this work aims to assist people in leveraging their smartphones and the functionality that is available in a more effective and efficient way. This results in tools and improved user interfaces for end-users. Given that the number of available applications for smartphones is rapidly increasing, it is crucial to understand how people make use of such applications to support smartphone use in everyday life with better designs for smartphone user interfaces.Mobiltelefone haben sich innerhalb der letzten Jahre signifikant weiterentwickelt. Während erste Modelle lediglich Sprachtelefonie zur Verfügung stellten, ermöglichen heutige Smartphones vielseitige Dienste. Technologische Fortschritte, wie beispielsweise GPS-Lokalisierung und berührungsempfindliche Displays, haben neue Einsatzbereiche für Mobiltelefone eröffnet, wie solche als Navigationsgerät oder als Fotoapparat. Doch nicht nur in Bezug auf die Technologie haben sich Mobiltelefone weiterentwickelt, sondern auch in der Verbreitung ist die Anzahl der Geräte enorm gestiegen. Sie werden allgegenwärtig im täglichen Leben genutzt, da sie ihre Anwender bei verschiedensten Aufgaben unterstützen können. Das Aufkommen von Vetriebsplattformen für die Verbreitung mobiler Software erlaubt es dem Anwender selbstständig Modifikationen an der Funktionalität seines Geräts vorzunehmen und dieses an persönliche Zwecke und Ansprüche anzupassen. Bisher ist wenig darüber bekannt, wie sich Anwender die Vielfalt zu Verfügung stehender Applikationen zu Nutze machen. Als Folge daraus gibt es bisher nur rudimentäre Unterstützung für Anwender, die Vielfalt von Applikationen effektiv und effizient einzusetzen. Diese Dissertation ist durch den Wandel des Mobiltelefons vom reinen Kommunikationsgerät hin zum multifunktionalen Werkzeug motiviert. Das Ziel dieser Arbeit ist es, Systeme für die Unterstützung einer besseren mobilen Applikationsnutzung zu entwickeln, deren Design auf dem neuen Verständnis von Benutzerverhalten beruht, das durch empirische Studien gewonnen wird. Diese Dissertation hat einen zweiteiligen Beitrag: Zum einen werden theoretische Erkenntnisse dazu erarbeitet, wie Anwender die Applikationsvielfalt nutzen, installierte Applikationen auf ihren Geräten organisieren, neue Applikationen entdecken und zwischen diesen in der Ausführung wechseln. Die Erkenntnisse hierzu beruhen auf der empirischen Beobachtung von Nutzungsverhalten. Zum anderen hat diese Arbeit ingenieurwissenschaftliche Ziele dahingehend, die Anwender von Applikationen dabei zu unterstützen, ihre Smartphones sowie deren Funktionsvielfalt effektiver und effizienter einzusetzen. Dieser Beitrag resultiert in der Beschreibung implementierter Systeme und verbesserter Benutzerschnittstellen für Anwender. Angesichts der rapide wachsenden Zahl zur Verfügung stehender mobiler Applikationen ist es wichtig, zu verstehen wie Endanwender diese nutzen, denn nur so kann die Nutzung von Smartphones gebrauchstauglicher und einfacher gestaltet werden

Essays on the interaction between users and information systems

The role of information systems has evolved from providing decision support into enabling the majority of our daily operations, and the way users interact with information systems has changed dramatically as a result. The goal of this dissertation is to study phenomena that stem from the close interaction between users and information systems using empirical methodologies. The first essay of this dissertation focuses on the issue of sentiment manipulation. We show that strategic players might be incentivized to manufacture content on social media platforms and opinion forums, in the context of the movie industry. We then identify unusual patterns on Twitter that are consistent with sentiment manipulation. We study the effectiveness of social media advertising in the second essay. Advertisers on popular social media platforms such as Facebook are able to publish ads with popularity and social information. We design and conduct a randomized field experiment to study the extent to which these types of information have an effect on ad performance. In the third essay we study how individuals might be biased toward contents that appear to be written more politely. We use data from an online question answering platform, StackExchange, to show that an individual who posts a question on the platform tends to prefer polite answers to clear answers.Information, Risk, and Operations Management (IROM