Hasil untuk "Advertising"

Carl Obermiller, Eric R. Spangenberg

M. Mooij

Foreword by Geert Hofstede Preface Summary of the Book Chapter 1: The Paradoxes In Global Marketing Communications The value paradox The global-local paradox The technology paradox The media paradox Paradoxes in global marketing theory Local markets are people, global markets are products Focus on a unique individual Globalization Convergence and divergence of consumer behavior The global-local dilemma in global marketing Global communities Global or local? The standardization-adaptation debate Review of a 50 year debate The variables that influence the standardization-adaptation decision Effect on performance Chapter 2: Global Branding Global branding Branding The brand concept and branding models Brand equity Brand architecture The global brand Perception of global brands by consumers Global brand strategies The global company's brand portfolio Global brand communications The importance of culture for global communications The brand as an association nework Chapter 3: Values and Culture The value concept Values are enduring The value paradox: The desirable and the Desired Culture defined Cultural universals Selective perception Stereotyping Manifestations of culture Signs, symbols and body language Imagery and music Thinking patterns and intellectual styles Language Comparing cultures Comparing nations Chapter 4: Dimensions of Culture Classifying cultures High- and low-context cultures Dimensions of time Closure Time orientation towards the past, present or future Time is linear or circular Monochronic and polychronic time Cause and effect Relationship of man with nature Hofstede's five dimensions of national culture Power distance (PDI) Individualism/collectivism (IDV) Masculinity/femininity (MAS) Uncertainty avoidance (UAI) Long-term orientation (LTO) Configurations of dimensions The USA The Netherlands Japan Chapter 5: Culture and Consumer Behavior Consumer behavior Consumer attributes The concept of self Personality Personality traits Identity and image Personality and identity in marketing Attitude Lifestyle Social processes Needs Motivation Buying motives Emotion Emotions in advertising Group processes Opinion leaders Mental processes Language, perception and memory Locus of control Information processing Decision making Consumer decision making styles Business decision making Consumer behavior domains Product acquisition, ownership and usage Complaining behavior Brand loyalty Diffusion of innovations Chapter 6: Researching and Applying Cultural Values Value research Value priorities vary Mixing terminal and instrumental values Value shift Culture-specific values Belgian values Dutch values Indian values Japanese values Important values don't translate Measuring cultural values Measuring the desired versus the desirable Individual- and culture-level Equivalence of survey data Sample equivalence Linguistic and conceptual equivalence Metric equivalence Comparing dimensional models Applying the Hofstede dimensions to marketing and advertising Understanding manifestations of culture Comparing groups of cultures Cause-effect Commercial value and lifestyle research Value structure maps Chapter 7: Culture and Communication Communication and culture Interpersonal communication styles Interpersonal communication and the electronic media Mass communication styles Advertising styles The purpose of marketing communication Informational versus emotional Measuring advertising: Persuasion of likeability How advertising works The hierarchy of effects High- and low involvement Visuals in advertising Appreciation of advertising in general Public relations Web site design Design: Logo, product, package and retail design Chapter 8: Culture and the Media An ever changing media landscape Media usage across cultures Television IPTV Radio Press media The mobile phone The world-wide web E-commerce Search marketing The social and entertainment roles of the internet Social networks The blog Internet advertising Ad format acceptability and effectiveness Viral marketing Online video advertising Mobile marketing and advertising Will the worldwide web facilitate standardization? The organization of international media planning Chapter 9: Culture and Advertising Appeals Appeals in advertising The value paradox as an effective advertising instrument Equality paradox Dependence and freedom paradoxes Success paradoxes The innovation and global paradox Examples of appeals by dimension Power distance Individualism/Collectivism Masculinity/Femininity Uncertainty avoidance Long-term orientation Consequences for advertising concepts Do great ideas travel? The country-of-origin appeal Why humor doesn't travel Chapter 10: Executional Style and Culture Classifications of advertising forms Seven basic advertising forms worldwide Announcement Association transfer Lesson Drama Entertainment Imagination Special effects Relationship basic form, culture and product category Chapter 11: From Value Paradox to Strategy A company's mission and vision Corporate identity Product/market development across cultures Branding and culture Brand positioning across cultures External aspects: Product usage and brand image Product usage Brand image Internal aspects: Brand identity & personality and brand values Brand identity and personality Brand values Brand positioning matrix Marketing communication strategy Fully standardized: One product or brand, display Semi-standardized: One brand, One advertising form, and Standard execution One brand, one form, varying standard executional elements One or different brand names, one advertising form, different executions One or different brand names, one concept, different executions based on culture-fit advertising styles Cultural segmentation: act global, think local Communication strategy by stage of market development Stage 1: Global products, global marketing communications Stage 2: Global products, adapted marketing communications Stage 3: Local products, local marketing communications Appendix A: GNP/capita 2007 (US$) and Hofstede Country scores for 66 countries Appendix B: Data sources Index About the author

M. Nerlove, K. Arrow

Marsha L. Richins

Guy Cook

Isabel Buil, L. Chernatony, Eva Martínez

O. Chapelle, Eren Manavoglu, Rómer Rosales

Xingye Chen, Wei Feng, Zhenbang Du et al.

In web data, advertising images are crucial for capturing user attention and improving advertising effectiveness. Most existing methods generate background for products primarily focus on the aesthetic quality, which may fail to achieve satisfactory online performance. To address this limitation, we explore the use of Multimodal Large Language Models (MLLMs) for generating advertising images by optimizing for Click-Through Rate (CTR) as the primary objective. Firstly, we build targeted pre-training tasks, and leverage a large-scale e-commerce multimodal dataset to equip MLLMs with initial capabilities for advertising image generation tasks. To further improve the CTR of generated images, we propose a novel reward model to fine-tune pre-trained MLLMs through Reinforcement Learning (RL), which can jointly utilize multimodal features and accurately reflect user click preferences. Meanwhile, a product-centric preference optimization strategy is developed to ensure that the generated background content aligns with the product characteristics after fine-tuning, enhancing the overall relevance and effectiveness of the advertising images. Extensive experiments have demonstrated that our method achieves state-of-the-art performance in both online and offline metrics. Our code and pre-trained models are publicly available at: https://github.com/Chenguoz/CAIG.

Qi Yang, Marlo Ongpin, Sergey Nikolenko et al.

The opaqueness of modern digital advertising, exemplified by platforms such as Meta Ads, raises concerns regarding their autonomous control over audience targeting, pricing structures, and ad relevancy assessments. Locked in their leading positions by network effects, ``Metas and Googles of the world'' attract countless advertisers who rely on intuition, with billions of dollars lost on ineffective social media ads. The platforms' algorithms use huge amounts of data unavailable to advertisers, and the algorithms themselves are opaque as well. This lack of transparency hinders the advertisers' ability to make informed decisions and necessitates efforts to promote transparency, standardize industry metrics, and strengthen regulatory frameworks. In this work, we propose novel ways to assist marketers in optimizing their advertising strategies via machine learning techniques designed to analyze and evaluate content, in particular, predict the click-through rates (CTR) of novel advertising content. Another important problem is that large volumes of data available in the competitive landscape, e.g., competitors' ads, impede the ability of marketers to derive meaningful insights. This leads to a pressing need for a novel approach that would allow us to summarize and comprehend complex data. Inspired by the success of ChatGPT in bridging the gap between large language models (LLMs) and a broader non-technical audience, we propose a novel system that facilitates marketers in data interpretation, called SODA, that merges LLMs with explainable AI, enabling better human-AI collaboration with an emphasis on the domain of digital marketing and advertising. By combining LLMs and explainability features, in particular modern text-image models, we aim to improve the synergy between human marketers and AI systems.

Mehdi Sebbar, Corentin Odic, Mathieu Léchine et al.

In the past years, many proposals have emerged in order to address online advertising use-cases without access to third-party cookies. All these proposals leverage some privacy-enhancing technologies such as aggregation or differential privacy. Yet, no public and rich-enough ground truth is currently available to assess the relevancy of aforementioned private advertising frameworks. We are releasing the largest, in terms of number of features, bidding dataset specifically built in alignment with the design of major browser vendors proposals such as Chrome Privacy Sandbox. This dataset, coined CriteoPrivateAds, stands for an anonymised version of Criteo production logs and provides sufficient data to learn bidding models commonly used in online advertising under many privacy constraints (delayed reports, display and user-level differential privacy, user signal quantisation or aggregated reports). We ensured that this dataset, while being anonymised, is able to provide offline results close to production performance of adtech companies including Criteo - making it a relevant ground truth to design private advertising systems. The dataset is available in Hugging Face: https://huggingface.co/datasets/criteo/CriteoPrivateAd.

Albin Uruqi, Iosif Viktoratos

This study explores the application of spiking neural networks (SNNs) for click-through rate (CTR) prediction in personalized online advertising systems, introducing a novel hybrid model, the Temporal Rate Spike with Attention Neural Network (TRA–SNN). By leveraging the biological plausibility and energy efficiency of SNNs, combined with attention-based mechanisms, the TRA–SNN model captures temporal dynamics and rate-based patterns to achieve performance comparable to state-of-the-art Artificial Neural Network (ANN)-based models, such as Deep & Cross Network v2 (DCN-V2) and FinalMLP. The models were trained and evaluated on the Avazu and Digix datasets, using standard metrics like AUC-ROC and accuracy. Through rigorous hyperparameter tuning and standardized preprocessing, this study ensures fair comparisons across models, highlighting SNNs’ potential for scalable, sustainable deployment in resource-constrained environments like mobile devices and large-scale ad platforms. This work is the first to apply SNNs to CTR prediction, setting a new benchmark for energy-efficient predictive modeling and opening avenues for future research in hybrid SNN–ANN architectures across domains like finance, healthcare, and autonomous systems.

R. Terlutter, M. Capella

Jesús Romero, Ángel Cuevas, Rubén Cuevas

Integrating Google's Topics API into the digital advertising ecosystem represents a significant shift toward privacy-conscious advertising practices. This article analyses the implications of implementing Topics API on ad networks, focusing on competition dynamics and ad space accessibility. Through simulations based on extensive datasets capturing user behavior and market share data for ad networks, we evaluate metrics such as Ad Placement Eligibility, Low Competition Rate, and solo competitor. The findings reveal a noticeable impact on ad networks, with larger players strengthening their dominance and smaller networks facing challenges securing ad spaces and competing effectively. Moreover, the study explores the potential environmental implications of Google's actions, highlighting the need to carefully consider policy and regulatory measures to ensure fair competition and privacy protection. Overall, this research contributes valuable insights into the evolving dynamics of digital advertising and highlights the importance of balancing privacy with competition and innovation in the online advertising landscape.

Zhen Gong, Lvyin Niu, Yang Zhao et al.

Online bidding and auction are crucial aspects of the online advertising industry. Conventionally, there is only one slot for ad display and most current studies focus on it. Nowadays, multi-slot display advertising is gradually becoming popular where many ads could be displayed in a list and shown as a whole to users. However, multi-slot display advertising leads to different cost-effectiveness. Advertisers have the incentive to adjust bid prices so as to win the most economical ad positions. In this study, we introduce bid shading into multi-slot display advertising for bid price adjustment with a Multi-task End-to-end Bid Shading(MEBS) method. We prove the optimality of our method theoretically and examine its performance experimentally. Through extensive offline and online experiments, we demonstrate the effectiveness and efficiency of our method, and we obtain a 7.01% lift in Gross Merchandise Volume, a 7.42% lift in Return on Investment, and a 3.26% lift in ad buy count.

Prof Dr Mohamad Haniki Nik Mohamed

Adolescence is an important developmental period1,2 characterized by engagement in risky behaviours, including the use of tobacco products such as cigarettes and electronic nicotine delivery systems such as e-cigarettes (e-cigs)3. The use of tobacco in the form of cigarettes and e-cigs is indeed of interest to the public health community and the nation at large. E-cigs are defined as devices that deliver aerosolized or vaporised nicotine form heating of liquids (e-juice) with constituents including nicotine, propylene glycol, glycerol, and other flavouring agents. It has been reported that 90% of smokers start smoking before the age of 18 years4. The Tobacco & E-Cigarette Survey among Malaysian Adolescents (TECMA) 2016, a nationwide school-based survey, found 11.7% current cigarette smokers among students between 10 to 19 years old. 78.7% of ever cigarette smokers tried their first cigarette before the age of 14. In addition, 9.1% of the students were current e-cigarettes users, with 40.9% vaping once a day and 33.9% doing it 2 to 5 times per day. Alarmingly, data from the 2022 Adolescent Health Survey found a sharp increase in adolescent vaping prevalence among adolescents aged 13-17 years old, reaching a high of 14.9% in 2022.6 E-cigs are heavily promoted directly to users include advertising and promotion at combustible cigarette point-of-sale (e.g., behind cashier’s counter). E-cigs are also promoted via physical and online shops, internet, social media, events, etc. According to TECMA, 10.6% of school-going adolescents aged 10-19 years were offered a free trial session of e-cigarette/vape while 7.9% were offered a free e-cigarette/ vape liquid (e-liquid)5. With proliferation of e-cig promotions via the social media, internet, and vape shops (some under the guise of selling electronic products, handphones, etc.,), using celebrities and others, the number of dual users and vapers among non-smokers, especially adolescents in Malaysia can be even higher now. In early adolescence, development of executive function and neurocognitive processes in the brain has not fully matured. Adolescence is a sensitive period for maturation of brain circuits that regulate cognition and emotion, with resulting vulnerability to the effects of nicotine and tobacco. The rapidly changing, immature adolescent brain has differing sensitivity to drugs such as nicotine and tobacco, and drug exposure during this time can lead to long-term changes in neural circuitry and behaviour6. The American Academy of Pediatrics produced a policy statement showing evidence regarding the effects of nicotine on the developing brain. Nicotine has neurotoxic effects on the developing brain, an effect on the brain as a “gateway” drug for cocaine and other illicit drugs. The gateway theory postulates that smoking, especially among adolescence, increases the risk of substance use due to effects of nicotine, shown to be a neuroteratogen that exerts long-term, maturational effects at critical stages of brain development7.

Zhoufutu Wen, Xinyu Zhao, Zhipeng Jin et al.

In the multimedia era, image is an effective medium in search advertising. Dynamic Image Advertising (DIA), a system that matches queries with ad images and generates multimodal ads, is introduced to improve user experience and ad revenue. The core of DIA is a query-image matching module performing ad image retrieval and relevance modeling. Current query-image matching suffers from limited and inconsistent data, and insufficient cross-modal interaction. Also, the separate optimization of retrieval and relevance models affects overall performance. To address this issue, we propose a vision-language framework consisting of two parts. First, we train a base model on large-scale image-text pairs to learn general multimodal representation. Then, we fine-tune the base model on advertising business data, unifying relevance modeling and retrieval through multi-objective learning. Our framework has been implemented in Baidu search advertising system "Phoneix Nest". Online evaluation shows that it improves cost per mille (CPM) and click-through rate (CTR) by 1.04% and 1.865%.

Dirk Bergemann, Alessandro Bonatti, Nicholas Wu

We present a model of digital advertising with three key features: (i) advertisers can reach consumers on and off a platform, (ii) additional data enhances the value of advertiser-consumer matches, and (iii) bidding follows auction-like mechanisms. We contrast data-augmented auctions, which leverage the platform's data advantage to improve match quality, and managed campaign mechanisms that automate match formation and price-setting. The platform-optimal mechanism is a managed campaign that conditions on-platform prices for sponsored products on the off-platform prices set by all advertisers. This mechanism yields the efficient on-platform allocation but inefficient off-platform allocations due to high product prices; it attains the vertical integration profit for the platform and advertisers; and it increases off-platform product prices and decreases consumer surplus, relative to data-augmented auctions.

Salim Chouaki, Oana Goga, Hamed Haddadi et al.

We present the first extensive measurement of the privacy properties of the advertising systems used by privacy-focused search engines. We propose an automated methodology to study the impact of clicking on search ads on three popular private search engines which have advertising-based business models: StartPage, Qwant, and DuckDuckGo, and we compare them to two dominant data-harvesting ones: Google and Bing. We investigate the possibility of third parties tracking users when clicking on ads by analyzing first-party storage, redirection domain paths, and requests sent before, when, and after the clicks. Our results show that privacy-focused search engines fail to protect users' privacy when clicking ads. Users' requests are sent through redirectors on 4% of ad clicks on Bing, 86% of ad clicks on Qwant, and 100% of ad clicks on Google, DuckDuckGo, and StartPage. Even worse, advertising systems collude with advertisers across all search engines by passing unique IDs to advertisers in most ad clicks. These IDs allow redirectors to aggregate users' activity on ads' destination websites in addition to the activity they record when users are redirected through them. Overall, we observe that both privacy-focused and traditional search engines engage in privacy-harming behaviors allowing cross-site tracking, even in privacy-enhanced browsers.

AboAlsamh Hoda Mahmoud, Siddiqui Kamran, Alahmadi Marwah

See the retraction notice E3S Web of Conferences 420, 00001 (2023), https://doi.org/10.1051/e3sconf/202342000001

Yakov Bart, A. Stephen, M. Sarvary