Optimal Real-Time Bidding for Display Advertising

Real-Time Bidding (RTB) is revolutionising display advertising by facilitating a real-time auction for each ad impression. As they are able to use impression-level data, such as user cookies and context information, advertisers can adaptively bid for each ad impression. Therefore, it is important that an advertiser designs an effective bidding strategy which can be abstracted as a function - mapping from the information of a specific ad impression to the bid price. Exactly how this bidding function should be designed is a non-trivial problem. It is a problem which involves multiple factors, such as the campaign-specific key performance indicator (KPI), the campaign lifetime auction volume and the budget. This thesis is focused on the design of automatic solutions to this problem of creating optimised bidding strategies for RTB auctions: strategies which are optimal, that is, from the perspective of an advertiser agent - to maximise the campaign's KPI in relation to the constraints of the auction volume and the budget. The problem is mathematically formulated as a functional optimisation framework where the optimal bidding function can be derived without any functional form restriction. Beyond single-campaign bid optimisation, the proposed framework can be extended to multi-campaign cases, where a portfolio-optimisation solution of auction volume reallocation is performed to maximise the overall profit with a controlled risk. On the model learning side, an unbiased learning scheme is proposed to address the data bias problem resulting from the ad auction selection, where we derive a "bid-aware'' gradient descent algorithm to train unbiased models. Moreover, the robustness of achieving the expected KPIs in a dynamic RTB market is solved with a feedback control mechanism for bid adjustment. To support the theoretic derivations, extensive experiments are carried out based on large-scale real-world data. The proposed solutions have been deployed in three commercial RTB systems in China and the United States. The online A/B tests have demonstrated substantial improvement of the proposed solutions over strong baselines

Maximizing User Engagement In Short Marketing Campaigns Within An Online Living Lab: A Reinforcement Learning Perspective

ABSTRACT MAXIMIZING USER ENGAGEMENT IN SHORT MARKETING CAMPAIGNS WITHIN AN ONLINE LIVING LAB: A REINFORCEMENT LEARNING PERSPECTIVE by ANIEKAN MICHAEL INI-ABASI August 2021 Advisor: Dr. Ratna Babu Chinnam Major: Industrial & Systems Engineering Degree: Doctor of Philosophy User engagement has emerged as the engine driving online business growth. Many firms have pay incentives tied to engagement and growth metrics. These corporations are turning to recommender systems as the tool of choice in the business of maximizing engagement. LinkedIn reported a 40% higher email response with the introduction of a new recommender system. At Amazon 35% of sales originate from recommendations, while Netflix reports that ‘75% of what people watch is from some sort of recommendation,’ with an estimated business value of $1 billion per year. While the leading companies have been quite successful at harnessing the power of recommenders to boost user engagement across the digital ecosystem, small and medium businesses (SMB) are struggling with declining engagement across many channels as competition for user attention intensifies. The SMBs often lack the technical expertise and big data infrastructure necessary to operationalize recommender systems. The purpose of this study is to explore the methods of building a learning agent that can be used to personalize a persuasive request to maximize user engagement in a data-efficient setting. We frame the task as a sequential decision-making problem, modelled as MDP, and solved using a generalized reinforcement learning (RL) algorithm. We leverage an approach that eliminates or at least greatly reduces the need for massive amounts of training data, thus moving away from a purely data-driven approach. By incorporating domain knowledge from the literature on persuasion into the message composition, we are able to train the RL agent in a sample efficient and operant manner. In our methodology, the RL agent nominates a candidate from a catalog of persuasion principles to drive higher user response and engagement. To enable the effective use of RL in our specific setting, we first build a reduced state space representation by compressing the data using an exponential moving average scheme. A regularized DQN agent is deployed to learn an optimal policy, which is then applied in recommending one (or a combination) of six universal principles most likely to trigger responses from users during the next message cycle. In this study, email messaging is used as the vehicle to deliver persuasion principles to the user. At a time of declining click-through rates with marketing emails, business executives continue to show heightened interest in the email channel owing to higher-than-usual return on investment of$42 for every dollar spent when compared to other marketing channels such as social media. Coupled with the state space transformation, our novel regularized Deep Q-learning (DQN) agent was able to train and perform well based on a few observed users’ responses. First, we explored the average positive effect of using persuasion-based messages in a live email marketing campaign, without deploying a learning algorithm to recommend the influence principles. The selection of persuasion tactics was done heuristically, using only domain knowledge. Our results suggest that embedding certain principles of persuasion in campaign emails can significantly increase user engagement for an online business (and have a positive impact on revenues) without putting pressure on marketing or advertising budgets. During the study, the store had a customer retention rate of 76% and sales grew by a half-million dollars from the three field trials combined. The key assumption was that users are predisposed to respond to certain persuasion principles and learning the right principles to incorporate in the message header or body copy would lead to higher response and engagement. With the hypothesis validated, we set forth to build a DQN agent to recommend candidate actions from a catalog of persuasion principles most likely to drive higher engagement in the next messaging cycle. A simulation and a real live campaign are implemented to verify the proposed methodology. The results demonstrate the agent’s superior performance compared to a human expert and a control baseline by a significant margin (~ up to 300%). As the quest for effective methods and tools to maximize user engagement intensifies, our methodology could help to boost user engagement for struggling SMBs without prohibitive increase in costs, by enabling the targeting of messages (with the right persuasion principle) to the right user

Information Leakage Attacks and Countermeasures

The scientific community has been consistently working on the pervasive problem of information leakage, uncovering numerous attack vectors, and proposing various countermeasures. Despite these efforts, leakage incidents remain prevalent, as the complexity of systems and protocols increases, and sophisticated modeling methods become more accessible to adversaries. This work studies how information leakages manifest in and impact interconnected systems and their users. We first focus on online communications and investigate leakages in the Transport Layer Security protocol (TLS). Using modern machine learning models, we show that an eavesdropping adversary can efficiently exploit meta-information (e.g., packet size) not protected by the TLS’ encryption to launch fingerprinting attacks at an unprecedented scale even under non-optimal conditions. We then turn our attention to ultrasonic communications, and discuss their security shortcomings and how adversaries could exploit them to compromise anonymity network users (even though they aim to offer a greater level of privacy compared to TLS). Following up on these, we delve into physical layer leakages that concern a wide array of (networked) systems such as servers, embedded nodes, Tor relays, and hardware cryptocurrency wallets. We revisit location-based side-channel attacks and develop an exploitation neural network. Our model demonstrates the capabilities of a modern adversary but also presents an inexpensive tool to be used by auditors for detecting such leakages early on during the development cycle. Subsequently, we investigate techniques that further minimize the impact of leakages found in production components. Our proposed system design distributes both the custody of secrets and the cryptographic operation execution across several components, thus making the exploitation of leaks difficult

Can Upward Brand Extensions be an Opportunity for Marketing Managers During the Covid-19 Pandemic and Beyond?

Early COVID-19 research has guided current managerial practice by introducing more products across different product categories as consumers tried to avoid perceived health risks from food shortages, i.e. horizontal brand extensions. For example, Leon, a fast-food restaurant in the UK, introduced a new range of ready meal products. However, when the food supply stabilised, availability may no longer be a concern for consumers. Instead, job losses could be a driver of higher perceived financial risks. Meanwhile, it remains unknown whether the perceived health or financial risks play a more significant role on consumers’ consumptions. Our preliminary survey shows perceived health risks outperform perceived financial risks to positively influence purchase intention during COVID-19. We suggest such a result indicates an opportunity for marketers to consider introducing premium priced products, i.e. upward brand extensions. The risk-as�feelings and signalling theories were used to explain consumer choice under risk may adopt affective heuristic processing, using minimal cognitive efforts to evaluate products. Based on this, consumers are likely to be affected by the salient high-quality and reliable product cue of upward extension signalled by its premium price level, which may attract consumers to purchase when they have high perceived health risks associated with COVID-19. Addressing this, a series of experimental studies confirm that upward brand extensions (versus normal new product introductions) can positively moderate the positive effect between perceived health risks associated with COVID-19 and purchase intention. Such an effect can be mediated by affective heuristic information processing. The results contribute to emergent COVID-19 literature and managerial practice during the pandemic but could also inform post-pandemic thinking around vertical brand extensions

Safety‐oriented discrete event model for airport A‐SMGCS reliability assessment

A detailed analysis of State of the Art Technologies and Procedures into Airport Advanced-Surface Movement Guidance and Control Systems has been provided in this thesis, together with the review ofStatistical Monte Carlo Analysis, Reliability Assessment and Petri Nets theories. This practical and theoretical background has lead the author to the conclusion that there is a lack of linkage in between these fields. At the same of time the rapid increasing of Air Traffic all over the world, has brought in evidence the urgent need of practical instruments able to identify and quantify the risks connected with Aircraft operations on the ground, since the Airport has shown to be the actual ‘bottle neck’ of the entire Air Transport System. Therefore, the only winning approach to such a critical matter has to be multi-disciplinary, sewing together apparently different subjects, coming from the most disparate areas of interest and trying to fulfil the gap. The result of this thesis work has come to a start towards the end, when a Timed Coloured Petri Net (TCPN) model of a ‘sample’ Airport A-SMGCS has been developed, that is capable of taking into account different orders of questions arisen during these recent years and tries to give them some good answers. The A-SMGCS Airport model is, in the end, a parametric tool relying on Discrete Event System theory, able to perform a Reliability Analysis of the system itself, that: • uses a Monte Carlo Analysis applied to a Timed Coloured Petri Net, whose purpose is to evaluate the Safety Level of Surface Movements along an Airport • lets the user to analyse the impact of Procedures and Reliability Indexes of Systems such as Surface Movement Radars, Automatic Dependent Surveillance-Broadcast, Airport Lighting Systems, Microwave Sensors, and so on… onto the Safety Level of Airport Aircraft Transport System • not only is a valid instrument in the Design Phase, but it is useful also into the Certifying Activities an in monitoring the Safety Level of the above mentioned System with respect to changes to Technologies and different Procedures.This TCPN model has been verified against qualitative engineering expectations by using simulation experiments and occupancy time schedules generated a priori. Simulation times are good, and since the model has been written into Simulink/Stateflow programming language, it can be compiled to run real-time in C language (Real-time workshop and Stateflow Coder), thus relying on portable code, able to run virtually on any platform, giving even better performances in terms of execution time. One of the most interesting applications of this work is the estimate, for an Airport, of the kind of A-SMGCS level of implementation needed (Technical/Economical convenience evaluation). As a matter of fact, starting from the Traffic Volume and choosing the kind of Ground Equipment to be installed, one can make predictions about the Safety Level of the System: if the value is compliant with the TLS required by ICAO, the A-SMGCS level of Implementation is sufficiently adequate. Nevertheless, even if the Level of Safety has been satisfied, some delays due to reduced or simplified performances (even if Safety is compliant) of some of the equipment (e.g. with reference to False Alarm Rates) can lead to previously unexpected economical consequences, thus requiring more accurate systems to be installed, in order to meet also Airport economical constraints. Work in progress includes the analysis of the effect of weather conditions and re-sequencing of a given schedule. The effect of re-sequencing a given schedule is not yet enough realistic since the model does not apply inter arrival and departure separations. However, the model might show some effect on different sequences based on runway occupancy times. A further developed model containing wake turbulence separation conditions would be more sensitive for this case. Hence, further work will be directed towards: • The development of On-Line Re-Scheduling based on the available actual runway/taxiway configuration and weather conditions. • The Engineering Safety Assessment of some small Italian Airport A-SMGCSs (Model validation with real data). • The application of Stochastic Differential Equations systems in order to evaluate the collision risk on the ground inside the Place alone on the Petri Net, in the event of a Short Term Conflict Alert (STCA), by adopting Reich Collision Risk Model. • Optimal Air Traffic Control Algorithms Synthesis (Adaptive look-ahead Optimization), by Dynamically Timed Coloured Petri Nets, together with the implementation of Error-Recovery Strategies and Diagnosis Functions

Advertising media strategy and planning: exploration of the strategy making approaches undertaken in the digital environment.

The aim of this thesis is to explore the practices being used to guide advertising media strategy and planning and identify if new approaches are emerging in the increasingly digitally orientated environment. Exploration of the literature identifies that the profession has changed significantly over the last three decades, with the role of media planners being elevated from that of an implementational scheduler to a more strategic role within the communication planning team. The literature also highlights a number of models designed to guide the media planning process. These indicate practitioners employ a classical deductive planning approach to evaluate, identify and propose the optimal media selection. Reflecting the newer strategic role, more recent frameworks identify a two-stage process where the development of a media strategy precedes implementation and optimisation, but this is still represented as a deductive process. Further exploration of the strategic management literature identifies how strategy making approaches in general have responded to the conditions of turbulence and uncertainty driven by the digital environment and indicated that this deductive approach might not be appropriate, leading to the hypothesis that the media strategy making approaches described through the literature do not reflect current practice. To explore this, primary research was undertaken, consisting of two concurrent studies.For study 1, fifteen in-depth interviews were conducted with senior UK media practitioners exploring current approaches to media strategy making. Study 2 complemented this with a content analysis of data collected from 93 practitioners to identify what information and data sources are used or required. The study identifies that the media strategy and planning approach has changed to become more emergent and iterative. This corroborates similar findings within strategic management literature. It also identifies that the role of media planners has been elevated further still, to be viewed as a business partner with the client. The culmination of this study results in the formation of a revised media planning process framework that makes the emergent and iterative approach more explicit, together with an accompanying ‘briefing checklist’ of the information that should be shared between clients and their agency strategists if the strategy and plan are to be effective