Real-time bidding campaigns optimization using user profile settings

Real-time bidding is nowadays one of the most promising systems in the online advertising ecosystem. In this study, the performance of RTB campaigns is improved by optimising the parameters of the users\u27 profiles and the publishers\u27 websites. Most studies concerning optimising RTB campaigns are focused on the bidding strategy, i.e., estimating the best value for each bid. However, this research focuses on optimising RTB campaigns by finding out configurations that maximise both the number of impressions and the average profitability of the visits. An online campaign configuration generally consists of a set of parameters along with their values such as {Browser = Chrome , Country = Germany , Age = 20–40 and Gender = Woman }. The experiments show that when advertisers\u27 required visits are low, it is easy to find configurations with high average profitability. Still, as the required number of visits increases, the average profitability diminishes. Additionally, configuration optimisation has been combined with other interesting strategies to increase, even more, the campaigns\u27 profitability. In particular, the presented study considers the following complementary strategies to increase profitability: (1) selecting multiple configurations with a small number of visits rather than a unique configuration with a large number of visits, (2) discarding visits according to certain cost and profitability thresholds, (3) analysing a reduced space of the dataset and extrapolating the solution over the whole dataset, and (4) increasing the search space by including solutions below the required number of visits. RTB and other advertising platforms could offer advertisers the developed campaign optimisation methodology to make their campaigns more profitable

Click fraud : how to spot it, how to stop it?

Online search advertising is currently the greatest source of revenue for many Internet giants such as Google™, Yahoo!™, and Bing™. The increased number of specialized websites and modern profiling techniques have all contributed to an explosion of the income of ad brokers from online advertising. The single biggest threat to this growth is however click fraud. Trained botnets and even individuals are hired by click-fraud specialists in order to maximize the revenue of certain users from the ads they publish on their websites, or to launch an attack between competing businesses. Most academics and consultants who study online advertising estimate that 15% to 35% of ads in pay per click (PPC) online advertising systems are not authentic. In the first two quarters of 2010, US marketers alone spent $5.7 billion on PPC ads, where PPC ads are between 45 and 50 percent of all online ad spending. On average about$1.5 billion is wasted due to click-fraud. These fraudulent clicks are believed to be initiated by users in poor countries, or botnets, who are trained to click on specific ads. For example, according to a 2010 study from Information Warfare Monitor, the operators of Koobface, a program that installed malicious software to participate in click fraud, made over $2 million in just over a year. The process of making such illegitimate clicks to generate revenue is called click-fraud. Search engines claim they filter out most questionable clicks and either not charge for them or reimburse advertisers that have been wrongly billed. However this is a hard task, despite the claims that brokers\u27 efforts are satisfactory. In the simplest scenario, a publisher continuously clicks on the ads displayed on his own website in order to make revenue. In a more complicated scenario. a travel agent may hire a large, globally distributed, botnet to click on its competitor\u27s ads, hence depleting their daily budget. We analyzed those different types of click fraud methods and proposed new methodologies to detect and prevent them real time. While traditional commercial approaches detect only some specific types of click fraud, Collaborative Click Fraud Detection and Prevention (CCFDP) system, an architecture that we have implemented based on the proposed methodologies, can detect and prevents all major types of click fraud. The proposed solution analyzes the detailed user activities on both, the server side and client side collaboratively to better describe the intention of the click. Data fusion techniques are developed to combine evidences from several data mining models and to obtain a better estimation of the quality of the click traffic. Our ideas are experimented through the development of the Collaborative Click Fraud Detection and Prevention (CCFDP) system. Experimental results show that the CCFDP system is better than the existing commercial click fraud solution in three major aspects: 1) detecting more click fraud especially clicks generated by software; 2) providing prevention ability; 3) proposing the concept of click quality score for click quality estimation. In the CCFDP initial version, we analyzed the performances of the click fraud detection and prediction model by using a rule base algorithm, which is similar to most of the existing systems. We have assigned a quality score for each click instead of classifying the click as fraud or genuine, because it is hard to get solid evidence of click fraud just based on the data collected, and it is difficult to determine the real intention of users who make the clicks. Results from initial version revealed that the diversity of CF attack Results from initial version revealed that the diversity of CF attack types makes it hard for a single counter measure to prevent click fraud. Therefore, it is important to be able to combine multiple measures capable of effective protection from click fraud. Therefore, in the CCFDP improved version, we provide the traffic quality score as a combination of evidence from several data mining algorithms. We have tested the system with a data from an actual ad campaign in 2007 and 2008. We have compared the results with Google Adwords reports for the same campaign. Results show that a higher percentage of click fraud present even with the most popular search engine. The multiple model based CCFDP always estimated less valid traffic compare to Google. Sometimes the difference is as high as 53%. Detection of duplicates, fast and efficient, is one of the most important requirement in any click fraud solution. Usually duplicate detection algorithms run in real time. In order to provide real time results, solution providers should utilize data structures that can be updated in real time. In addition, space requirement to hold data should be minimum. In this dissertation, we also addressed the problem of detecting duplicate clicks in pay-per-click streams. We proposed a simple data structure, Temporal Stateful Bloom Filter (TSBF), an extension to the regular Bloom Filter and Counting Bloom Filter. The bit vector in the Bloom Filter was replaced with a status vector. Duplicate detection results of TSBF method is compared with Buffering, FPBuffering, and CBF methods. False positive rate of TSBF is less than 1% and it does not have false negatives. Space requirement of TSBF is minimal among other solutions. Even though Buffering does not have either false positives or false negatives its space requirement increases exponentially with the size of the stream data size. When the false positive rate of the FPBuffering is set to 1% its false negative rate jumps to around 5%, which will not be tolerated by most of the streaming data applications. We also compared the TSBF results with CBF. TSBF uses only half the space or less than standard CBF with the same false positive probability. One of the biggest successes with CCFDP is the discovery of new mercantile click bot, the Smart ClickBot. We presented a Bayesian approach for detecting the Smart ClickBot type clicks. The system combines evidence extracted from web server sessions to determine the final class of each click. Some of these evidences can be used alone, while some can be used in combination with other features for the click bot detection. During training and testing we also addressed the class imbalance problem. Our best classifier shows recall of 94%. and precision of 89%, with F1 measure calculated as 92%. The high accuracy of our system proves the effectiveness of the proposed methodology. Since the Smart ClickBot is a sophisticated click bot that manipulate every possible parameters to go undetected, the techniques that we discussed here can lead to detection of other types of software bots too. Despite the enormous capabilities of modern machine learning and data mining techniques in modeling complicated problems, most of the available click fraud detection systems are rule-based. Click fraud solution providers keep the rules as a secret weapon and bargain with others to prove their superiority. We proposed validation framework to acquire another model of the clicks data that is not rule dependent, a model that learns the inherent statistical regularities of the data. Then the output of both models is compared. Due to the uniqueness of the CCFDP system architecture, it is better than current commercial solution and search engine/ISP solution. The system protects Pay-Per-Click advertisers from click fraud and improves their Return on Investment (ROI). The system can also provide an arbitration system for advertiser and PPC publisher whenever the click fraud argument arises. Advertisers can gain their confidence on PPC advertisement by having a channel to argue the traffic quality with big search engine publishers. The results of this system will booster the internet economy by eliminating the shortcoming of PPC business model. General consumer will gain their confidence on internet business model by reducing fraudulent activities which are numerous in current virtual internet world

Cognitive performance application.

This work shows that combining the techniques of neural networking and predictive analytics with the fundamental concepts of computing performance optimization is genuine in many ways. It has the potentials to: (1) reduce infrastructure upgrade costs (2) reduce human interactions, by enabling the system to learn, analyze, and make decisions on its own, and (3) generalize the solutions to other performance problems. This paper attempts to tackle a JVM performance optimization from a different dimension and in a way that can be scaled to other common utilized resources, such as file systems, static contents, search engines, web services...etc. It shows how to build a framework that monitors the performance metrics to determine patterns leading to bottleneck incidents and then benchmark these performance metrics. The framework uses artificial neural network in its core to accomplish this first steps with immediate benefit of eliminating the need to a domain expert analyzing which of these metrics is more important or has more weight on constituting the bottleneck condition, and hence enable the system to deal with more ambiguous situations. The framework uses an analytics engine, to establish predictive patterns between the system bottleneck and library of factors to establish an early alert system and thus enhancing the weight of the bottleneck signal. Finally, the framework acts in defense when the deadlock signal is triggered from the learning and/or the analytics engine through streaming down concurrent transactions into a temporarily queuing data structure. We put our model into a test and built a simulation to quantify the added benefit of each component of our framework. The results are proven to demonstrate the immediate benefit of our framework and open doors for other future work

A grounded theory of affiliate marketing performance measurement in the tourism and hospitality context

Although the measurement of offline and online marketing is extensively researched, the area of online performance measurement still presents a number of unaddressed gaps, such as fragmented research and predominance of practitioner-driven measurement approaches. With a focus on affiliate marketing in tourism and hospitality, this thesis addressed these gaps and evaluates the effectiveness of practitioner-led online performance assessment. More precisely, the study explores a potential shift in affiliate marketing measurement practices, and develops a theory of affiliate marketing performances measurement in tourism and hospitality. Relying on a grounded theory research strategy, the work undertakes qualitative analysis of 72 online forum discussions, 37 interviews and 40 questionnaires with the major affiliate marketing stakeholder groups from the tourism and hospitality industry - merchants, affiliates, affiliate networks and affiliate agencies. The findings of the thesis add value to both theory and practice. The theoretical contribution of the research is twofold. First, the work furthers the broader marketing theory and in particular the distribution and promotion literature by exploring an under-researched online marketing channel - affiliate marketing - that can be employed for both promotion and distribution purposes. The study provides a detailed description of an affiliate marketing ecosystem and defines the key affiliate marketing constructs. Second, the work contributes to the performance measurement research by developing a substantiative theory of affiliate marketing performance measurement in tourism and hospitality. From the practitioner perspective, the work brings value by proposing a change in existing performance measurement practices and offering a process-oriented model of performance measurement in affiliate marketing, which details the phases and steps that managers can undertake in assessing performance. To further the findings, future research can explore the applicability of the proposed model to other industry sectors and online channels, and can develop the proposed substantive theory to a formal theory by employing other research methods, for example case studies and action research

Paid Search Advertising como meio de desenvolvimento de Brand Awareness para a conversão de Leads

A inovação tecnológica trouxe mudanças teóricas na maneira como o marketing é aplicado, passando a haver um maior interesse das empresas pela prática de paid search advertising, devido à maior eficácia na entrega de uma mensagem. Simultaneamente, a concorrência cada vez mais acirrada em muitos dos sectores de serviços B2B, tem levado as organizações a procurar uma vantagem competitiva por meio da notoriedade da marca. Diante desses desafios, a SGS Academy enquanto departamento de formação profissional do grupo SGS, passou a procurar novas formas de comunicar digitalmente, de modo a promover uma maior aproximação do negócio ao seu mercado e converter um maior número de leads através dos seus canais digitais. Para tal, delineou-se uma estratégia assente na criação e monitorização de uma campanha promocional com recurso a anúncios pay-per-click na plataforma Google Ads. O presente documento académico apoia-se numa técnica de análise de conteúdo e comprova que a introdução de uma campanha comunicacional de paid search advertising, assente na criação e monitorização de anúncios PPC, atuou como um meio eficaz para o aumento da brand awareness da SGS Academy e consequente conversão de leads no ecossistema digital da marca.The technological innovation has brought theoretical changes in the way marketing is applied, and companies are now more interested in the practice of paid search advertising, due to the greater effectiveness in delivering a message. Simultaneously, the increasingly fierce competition in many of the B2B service sectors has led organizations to seek competitive advantage through brand awareness. Facing these challenges, SGS Academy, as a professional training department of the SGS group, started looking for new ways to communicate digitally, in order to promote a closer approach of the business to its market and convert a greater number of leads through their digital channels. To this end, it was outlined a strategy based on the creation and monitoring of a promotional campaign using pay-per-click ads on the Google Ads platform. This academic document is supported by a content analysis technique and proves that the introduction of a paid search advertising campaign, based on the creation and monitoring of PPC ads, acted as an effective means to increase SGS Academy brand awareness and consequent conversion of leads in the digital ecosystem of the brand

Security, Privacy and Economics of Online Advertising

Online advertising is at the core of today’s Web: it is the main business model, generating large annual revenues expressed in tens of billions of dollars that sponsor most of the online content and services. Online advertising consists of delivering marketing messages, embedded into Web content, to a targeted audience. In this model, entities attract Web traffic by offering the content and services for free and charge advertisers for including advertisements in this traffic (i.e., advertisers pay for users’ attention and interests). Online advertising is a very successful form of advertising as it allows for advertisements (ads) to be targeted to individual users’ interests; especially when advertisements are served on users’ mobile devices, as ads can be targeted to users’ locations and the corresponding context. However, online advertising also introduces a number of problems. Given the high ad revenue at stake, fraudsters have economic incentives to exploit the ad system and generate profit from it. Unfortunately, to achieve this goal, they often compromise users’ online security (e.g., via malware, phishing, etc.). For the purpose of maximizing the revenue by matching ads to users’ interests, a number of techniques are deployed, aimed at tracking and profiling users’ digital footprints, i.e., their behavior in the digital world. These techniques introduce new threats to users’ privacy. Consequently, some users adopt ad-avoidance tools that prevent the download of advertisements and partially thwart user profiling. Such user behavior, as well as exploits of ad systems, have economic implications as they undermine the online advertising business model. Meddling with advertising revenue disrupts the current economic model of the Web, the consequences of which are unclear. Given that today’s Web model relies on online advertising revenue in order for users to have access and consume content and services for “free”, coupled with the fact that there are many threats that could jeopardize this model, in this thesis we address the security, privacy and economic issues stemming from this fundamental element of the Web. In the first part of the thesis, we investigate the vulnerabilities of online advertising systems. We identify how an adversary can exploit the ad system to generate profit for itself, notably by performing inflight modification of ad traffic. We provide a proof-of-concept implementation of the identified threat on Wi-Fi routers. We propose a collaborative approach for securing online advertising and Web browsing against such threats. By investigating how a certificate-based authentication is deployed in practice, we assess the potential of relying on certificate-based authentication as a building block of a solution to protect the ad revenue. We propose a multidisciplinary approach for improving the current state of certificate-based authentication on the Web. In the second part of the thesis, we study the economics of ad systems’ exploits and certain potential countermeasures. We evaluate the potential of different solutions aimed at protecting ad revenue being implemented by the stakeholders (e.g., Internet Service Providers or ad networks) and the conditions under which this is likely to happen. We also study the economic ramifications of ad-avoidance technologies on the monetization of online content. We use game-theory to model the strategic behavior of involved entities and their interactions. In the third part of the thesis, we focus on privacy implications of online advertising. We identify a novel threat to users’ location privacy that enables service providers to geolocate users with high accuracy, which is needed to serve location-targeted ads for local businesses. We draw attention to the large scale of the threat and the potential impact on users’ location privacy