Predicting Audio Advertisement Quality

Online audio advertising is a particular form of advertising used abundantly in online music streaming services. In these platforms, which tend to host tens of thousands of unique audio advertisements (ads), providing high quality ads ensures a better user experience and results in longer user engagement. Therefore, the automatic assessment of these ads is an important step toward audio ads ranking and better audio ads creation. In this paper we propose one way to measure the quality of the audio ads using a proxy metric called Long Click Rate (LCR), which is defined by the amount of time a user engages with the follow-up display ad (that is shown while the audio ad is playing) divided by the impressions. We later focus on predicting the audio ad quality using only acoustic features such as harmony, rhythm, and timbre of the audio, extracted from the raw waveform. We discuss how the characteristics of the sound can be connected to concepts such as the clarity of the audio ad message, its trustworthiness, etc. Finally, we propose a new deep learning model for audio ad quality prediction, which outperforms the other discussed models trained on hand-crafted features. To the best of our knowledge, this is the first large-scale audio ad quality prediction study.Comment: WSDM '18 Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, 9 page

Opinion mining and sentiment analysis in marketing communications: a science mapping analysis in Web of Science (1998–2018)

Opinion mining and sentiment analysis has become ubiquitous in our society, with applications in online searching, computer vision, image understanding, artificial intelligence and marketing communications (MarCom). Within this context, opinion mining and sentiment analysis in marketing communications (OMSAMC) has a strong role in the development of the field by allowing us to understand whether people are satisfied or dissatisfied with our service or product in order to subsequently analyze the strengths and weaknesses of those consumer experiences. To the best of our knowledge, there is no science mapping analysis covering the research about opinion mining and sentiment analysis in the MarCom ecosystem. In this study, we perform a science mapping analysis on the OMSAMC research, in order to provide an overview of the scientific work during the last two decades in this interdisciplinary area and to show trends that could be the basis for future developments in the field. This study was carried out using VOSviewer, CitNetExplorer and InCites based on results from Web of Science (WoS). The results of this analysis show the evolution of the field, by highlighting the most notable authors, institutions, keywords, publications, countries, categories and journals.The research was funded by Programa Operativo FEDER Andalucía 2014‐2020, grant number “La reputación de las organizaciones en una sociedad digital. Elaboración de una Plataforma Inteligente para la Localización, Identificación y Clasificación de Influenciadores en los Medios Sociales Digitales (UMA18‐ FEDERJA‐148)” and The APC was funded by the same research gran

Strategic corporate communication in the digital age

This chapter presents a systematic review of over thirty (30) types of online marketing methods. It describes different methods like email marketing, social network marketing, in-game marketing and augmented reality marketing, among other approaches. The researchers discuss that the rationale for using these online marketing strategies is to increase brand awareness, customer centric marketing and consumer loyalty. They shed light on various personalization methods including recommendation systems and user generated content in their taxonomy of online marketing terms. Hence, they explain how these online marketing methods are related to each other. The researchers contend that the boundaries between online marketing methods have not been clarified enough within the academic literature. Therefore, this chapter provides a better understanding of different online marketing methods. A review of the literature suggests that the ‘oldest’ online marketing methods including the email and the websites are still very relevant for today’s corporate communication. In conclusion, the researchers put forward their recommendations for future research about contemporary online marketing methods.peer-reviewe

Effectiveness, Efficiency, and Ethics of Marketing Analytics

Abstract The concept of big data has influenced the marketing field in numerous ways. By having access to more information about their consumers than ever before, marketers are presented with a unique opportunity to make the marketing process more streamlined and effective than ever; however, this also creates a challenge in understanding how this targeted advertising affects the brand’s perception by consumers. This study looks at the concepts of data marketing and re-targeted ads from three aspects. First, are marketers being as effective as possible to ensure they are sending the right advertisement, to the right customer, at the right time? Second, are marketers being as efficient as possible when choosing the correct platform to reach their target customers? Third, are companies remembering the ethical components of collecting this information on consumers, and ensuring they understand when consumers feel specialized advertising becomes an invasion of their privacy? To answer these questions, I first performed secondary research in the form of a literature review. From surveying the scope of the subject, I then performed primary research by conducting in-depth interviews and a survey. The results show that there are two distinct type of consumers: one group who is accepting of these re-targeted advertisements and welcoming of the specialized marketing, and a second group who is skeptical of this form of marketing and concerned over privacy issues. Marketers must be aware of these two distinct types of consumers and ensure they are choosing their advertising methods carefully to ensure an efficient utilization of resources and to make sure they are not presenting a detriment to their brand for the consumers who do not want catered advertisements

Machine learning for targeted display advertising: Transfer learning in action

This paper presents a detailed discussion of problem formulation and data representation issues in the design, deployment, and operation of a massive-scale machine learning system for targeted display advertising. Notably, the machine learning system itself is deployed and has been in continual use for years, for thousands of advertising campaigns (in contrast to simply having the models from the system be deployed). In this application, acquiring sufficient data for training from the ideal sampling distribution is prohibitively expensive. Instead, data are drawn from surrogate domains and learning tasks, and then transferred to the target task. We present the design of this multistage transfer learning system, highlighting the problem formulation aspects. We then present a detailed experimental evaluation, showing that the different transfer stages indeed each add value. We next present production results across a variety of advertising clients from a variety of industries, illustrating the performance of the system in use. We close the paper with a collection of lessons learned from the work over half a decade on this complex, deployed, and broadly used machine learning system.Statistics Working Papers Serie

Inefficiencies in Digital Advertising Markets

Digital advertising markets are growing and attracting increased scrutiny. This article explores four market inefficiencies that remain poorly understood: ad effect measurement, frictions between and within advertising channel members, ad blocking, and ad fraud. Although these topics are not unique to digital advertising, each manifests in unique ways in markets for digital ads. The authors identify relevant findings in the academic literature, recent developments in practice, and promising topics for future research

Analysis of online advertisement performance using Markov chains

The measurement and performance analysis of online marketing is far from simple as it is usually conducted in multiple channels which results depend on each other. The results of the performance analysis can vary drastically depending on the attribution model used. An online marketing attribution analysis is needed to make better decisions on where to allocate marketing budgets. This thesis aims to provide a framework for more optimal budget alloca- tion by conducting a data-driven attribution model analysis to the case company’s dataset and comparing the results with the de-facto last-click attribution model’s results. The frame- work is currently utilized in the case company to improve the online marketing budget allo- cation and to gain better understanding of the marketing efforts. The thesis begins with literature review to online marketing, measurement techniques and most used attribution modeling models in the industry. The Markov’s attribution model was chosen to the analysis because of its promising results in other research and the ease of implementation with the dataset available. The dataset used in the analysis contains 582 111 user paths collected during 7 months period from the case company’s website. The analysis was conducted using R programming language and open source ChannelAttribution package that includes tools for fitting a k-order Markovian model in to a dataset and analyzing the results and the model’s reliability. The performance of the attribution model was analyzed using a ROC curve to evaluate the prediction accuracy of the model. The results of the research indicate the Markov’s model gives more reliable results on where to allocate the marketing budget than then last-click attribution model that is widely used in the industry. Overall the objectives of this thesis were achieved, and this study pro- vides a solid framework for marketing managers to analyze their marketing efforts and real- locate their marketing budgets in more optimal way. However, more research is needed to improve the prediction accuracy of the model and to improve the understanding of the effects of budget reallocation

Personalization in Digital Advertising

Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Marketing Research and CRMThis research studies the impact a well-planned digital advertisement campaign and user-friendly online experience can have on Brands awareness and sales. As the Digital landscape evolves, so has Online Advertisement and Data collection, making it possible for Advertisers to know more about who is navigating online, where they are, and how to approach them. With the gathering of online users’ insights, brands can impact whom they want and how they want to, making their communication more relevant, therefore creating less noise and more conversation. This work aims at proving that these segmented and personalized campaigns lead to more engagement and sales than the ones that just target anybody with no defined criteria. It will also consider the opinions of online users regarding online advertisement and the fact that brands can use their navigating information to plan and implement digital campaigns. With this in mind, it would be possible to detect a relation between advertisement quality and user experience with the increase or decrease of Ad Blockers downloads, especially amongst Generation Z, that is the generation more comfortable with digital technology and will be the consumers of the future. By the end of the study we should be able to understand better the environment of actual digital advertisement and the way it can and should evolve in the future regarding all the insights we are able to collect from this research