Hasil untuk "Advertising"

L. Sweeney

Google ads, black names and white names, racial discrimination, and click advertising.

S. Chan, William Chui, K. Lo et al.

J. Kees, Christopher Berry, Scot Burton et al.

Christian Hughes, V. Swaminathan, Gillian Brooks

Influencer marketing is prevalent in firm strategies, yet little is known about the factors that drive success of online brand engagement at different stages of the consumer purchase funnel. The findings suggest that sponsored blogging affects online engagement (e.g., posting comments, liking a brand) differently depending on blogger characteristics and blog post content, which are further moderated by social media platform type and campaign advertising intent. When a sponsored post occurs on a blog, high blogger expertise is more effective when the advertising intent is to raise awareness versus increase trial. However, source expertise fails to drive engagement when the sponsored post occurs on Facebook. When a sponsored post occurs on Facebook, posts high in hedonic content are more effective when the advertising intent is to increase trial versus raise awareness. The effectiveness of campaign incentives depends on the platform type, such that they can increase (decrease) engagement on blogs (Facebook). The empirical evidence for these findings comes from real in-market customer response data and is supplemented with data from an experiment. Taken together, the findings highlight the critical interplay of platform type, campaign intent, source, campaign incentives, and content factors in driving engagement.

C. Cobb-Walgren, C. A. Ruble, Naveen Donthu

W. Comanor, T. Wilson

Leah R. Rosenzweig, Parrish Bergquist, Katherine Hoffmann Pham et al.

Survey research in the Global South has traditionally required large budgets and lengthy fieldwork. The expansion of digital connectivity presents an opportunity for researchers to engage global subject pools and study settings where in-person contact is challenging. This paper evaluates Facebook advertisements as a tool to recruit diverse survey samples in the Global South. Using Facebook’s advertising platform, we quota-sample respondents in Mexico, Kenya, and Indonesia and assess how well these samples perform on a range of survey indicators, identify sources of bias, replicate a canonical experiment, and highlight trade-offs for researchers to consider. This method can quickly and cheaply recruit respondents, but these samples tend to be more educated than corresponding national populations. Weighting ameliorates sample imbalances. This method generates comparable data to a commercial online sample for a fraction of the cost. Our analysis demonstrates the potential of Facebook advertisements to cost-effectively conduct research in diverse settings.

Dildar Ali, Suman Banerjee, Yamuna Prasad

The billboard advertisement has emerged as an effective out-of-home advertisement technique where the objective is to choose a limited number of slots to play some advertisement content (e.g., animation, video, etc.) with the hope that the content will be visible to a large number of travelers, and this will be helpful to earn more revenue. In this paper, we study a variant of the influential slot selection problem where the advertiser wants to promote multiple products. Formally, we call this problem the \textsc{Multi-Product Influence Maximization Problem for the Balanced Popularity} Problem. The input to our problem is a trajectory and a billboard database, as well as a budget for each product. The goal here is to choose a subset of slots for each product such that the aggregated influence of all the products gets maximized subject to the following two constraints: total selection cost for each product is less than or equal to the allocated budget for that product, and the difference between the influence for any two products is less than or equal to a given threshold. We show that the problem is NP-hard to solve optimally. We formulate this problem as a linear programming problem and use linear programming relaxation with randomized rounding. Further, we propose a greedy-based heuristic with balance correction to solve this problem. We conduct a number of experiments with real-world trajectory and billboard datasets, and the results are reported. From the reported results, we observe that the proposed solution approaches lead to more influence compared to many baseline methods.

Briti Gangopadhyay, Zhao Wang, Shingo Takamatsu

Forecasting click volume is a key task in digital advertising, influencing both revenue and campaign strategy. Traditional time series models rely solely on numerical data, often overlooking rich contextual information embedded in textual elements, such as keyword updates. We present a multimodal forecasting framework that combines click data with textual logs from real-world ad campaigns and generates human-interpretable explanations alongside numeric predictions. Reinforcement learning is used to improve comprehension of textual information and enhance fusion of modalities. Experiments on a large-scale industry dataset show that our method outperforms baselines in both accuracy and reasoning quality.

Youngjin Hong, In Kyung Kim, Kyoo il Kim

This paper empirically examines the extent to which a favorable view of a firm, shaped by its social contributions, influences consumer choices and firm sales. Using a favorability rating that reflects media exposure of each firm's corporate social responsibility (CSR) activities in the Korean instant noodles market during the 2010s, we find evidence that improvements in the corporate image of Ottogi - one of the country's largest instant noodle producers - positively affected consumer utility for the firm's products. Notably, Ottogi's annual sales of its major brands increased by an average of 23.7 million packages, or 6.7%, as a result of CSR activities and the associated rise in consumer favorability. This effect is comparable in magnitude to that of a nearly 60% increase in advertising spending. Our findings suggest that CSR can foster firm growth by boosting product sales.

Helen Lambert, Jody Gordon, Laura Jackson et al.

Abstract Animals are frequently used in the UK for film, television, and advertising, but numerous stakeholders, including charities and industry representatives, have significant concerns regarding their on- and off-set welfare. However, it is unclear to what extent the public can identify animal welfare issues on screen or whether they know where to report any incidents. This survey sought to answer three research questions: (1) What are the levels of awareness and concern around animals used in TV, film, and advertising among the general public? (2) Do people know where or how to report any concerns? (3) What would reassure people that animal welfare is safeguarded? We have analysed and presented the results from the 911 responses to paint a more detailed picture of where the public is on this issue and what mechanisms for improvement they would support. Overall, the results show a clear concern for the welfare of animals used in the media, especially once they had read the eight realistic scenarios of animal use in the survey. Yet the public feel disempowered to report these concerns and lack trust that positive change will result. There is clearly public appetite for better protection of animals used in the media, particularly stronger regulations, independent on-set monitoring of animals, qualified animal trainers, and an accreditation scheme.

Shuai Yang, Hao Yang, Zhuang Zou et al.

In the e-commerce advertising scenario, estimating the true probabilities (known as a calibrated estimate) on Click-Through Rate (CTR) and Conversion Rate (CVR) is critical. Previous research has introduced numerous solutions for addressing the calibration problem. These methods typically involve the training of calibrators using a validation set and subsequently applying these calibrators to correct the original estimated values during online inference. However, what sets e-commerce advertising scenarios apart is the challenge of multi-field calibration. Multi-field calibration requires achieving calibration in each field. In order to achieve multi-field calibration, it is necessary to have a strong data utilization ability. Because the quantity of pCTR specified range for a single field-value (such as user ID and item ID) sample is relatively small, this makes the calibrator more difficult to train. However, existing methods have difficulty effectively addressing these issues. To solve these problems, we propose a new method named Deep Ensemble Shape Calibration (DESC). In terms of business understanding and interpretability, we decompose multi-field calibration into value calibration and shape calibration. We introduce innovative basis calibration functions, which enhance both function expression capabilities and data utilization by combining these basis calibration functions. A significant advancement lies in the development of an allocator capable of allocating the most suitable calibrators to different estimation error distributions within diverse fields and values. We achieve significant improvements in both public and industrial datasets. In online experiments, we observe a +2.5% increase in CVR and +4.0% in GMV (Gross Merchandise Volume). Our code is now available at: https://github.com/HaoYang0123/DESC.

Ali Goli, Jason Huang, David Reiley et al.

A randomized experiment with almost 35 million Pandora listeners enables us to measure the sensitivity of consumers to advertising, an important topic of study in the era of ad-supported digital content provision. The experiment randomized listeners into nine treatment groups, each of which received a different level of audio advertising interrupting their music listening, with the highest treatment group receiving more than twice as many ads as the lowest treatment group. By maintaining consistent treatment assignment for 21 months, we measure long-run demand effects and find ad-load sensitivity three times greater than what we would have obtained from a month-long experiment. We show the negative impact on the number of hours listened, days listened, and probability of listening at all in the final month. Using an experimental design that separately varies the number of commercial interruptions per hour and the number of ads per commercial interruption, we find that listeners primarily respond to the total number of ads per hour, with a slight preference for more frequent but shorter ad breaks. Lastly, we find that increased ad load led to an increase in the number of paid ad-free subscriptions to Pandora. Importantly, we show that observational methods often lead to biased or even directionally incorrect estimates of these effects, highlighting the value of experimental data.

Yang Zhao, Chang Zhou, Jin Cao et al.

This paper explores multi-scenario optimization on large platforms using multi-agent reinforcement learning (MARL). We address this by treating scenarios like search, recommendation, and advertising as a cooperative, partially observable multi-agent decision problem. We introduce the Multi-Agent Recurrent Deterministic Policy Gradient (MARDPG) algorithm, which aligns different scenarios under a shared objective and allows for strategy communication to boost overall performance. Our results show marked improvements in metrics such as click-through rate (CTR), conversion rate, and total sales, confirming our method's efficacy in practical settings.

Sooyeon Choi, Seungho Shin

Cryptocurrency is gaining worldwide recognition. This research examines the psychological determining factors of consumers’ cryptocurrency adoption behavior based on the theory of planned behavior. 452 samples are collected from U.S consumers and the data are analyzed by PLS-SEM. The findings reveal that consumer innovativeness has positive influences on the attitude and perceived behavioral control for cryptocurrency and in turn affects the intention to use cryptocurrency. Subjective norm is a significant predictor of cryptocurrency intention and the LOHAS lifestyle moderates the influence of attitude on the intention. This research offers theoretical and practical implications for the cryptocurrency market.

Liyao Jiang, Chenglin Li, Haolan Chen et al.

Online advertisements are important elements in e-commerce sites, social media platforms, and search engines. With the increasing popularity of mobile browsing, many online ads are displayed with visual information in the form of a cover image in addition to text descriptions to grab the attention of users. Various recent studies have focused on predicting the click rates of online advertisements aware of visual features or composing optimal advertisement elements to enhance visibility. In this paper, we propose Advertisement Style Editing and Attractiveness Enhancement (AdSEE), which explores whether semantic editing to ads images can affect or alter the popularity of online advertisements. We introduce StyleGAN-based facial semantic editing and inversion to ads images and train a click rate predictor attributing GAN-based face latent representations in addition to traditional visual and textual features to click rates. Through a large collected dataset named QQ-AD, containing 20,527 online ads, we perform extensive offline tests to study how different semantic directions and their edit coefficients may impact click rates. We further design a Genetic Advertisement Editor to efficiently search for the optimal edit directions and intensity given an input ad cover image to enhance its projected click rates. Online A/B tests performed over a period of 5 days have verified the increased click-through rates of AdSEE-edited samples as compared to a control group of original ads, verifying the relation between image styles and ad popularity. We open source the code for AdSEE research at https://github.com/LiyaoJiang1998/adsee.

Faten Khamis

Celebrities are well-known personalities in society who enjoy charisma and credibility and are considered as a source of confidence for the audiences. Advertisers are working to take advantage of these features by using celebrities in their ads to increase the effectiveness and reliability of the advertisement.This study identifies the factors that influence celebrities on the audience and examines the impact of celebrity endorsement in advertising.The study focuses on social advertising, the way in which the audience interacts with the advertising message, the extent of the response achieved through the influence of the Celebrities presenting the advertisement, and the social impact it creates.The purpose of this paper is to help non-profit organizations and government media understand the importance of social advertising as well as the best criteria for selecting celebrities to reach their target audience.This study will assist non-profit organizations in making the right choice for celebrities to use in supporting ads as well as looking at the impact of ads.The research uses the questionnaire as a tool to collect data and analyze it to find out how celebrity endorsers affect advertising and affect audience response and its impact on society.Accordingly, the main factors that must be considered while selecting a celebrity to support the awareness campaigns should be identified, as celebrities can add a lot of value to the advertising campaign, but the wrong partnership can lead to widespread embarrassment and reduce the value of the advice provided in the campaign, With the right celebrities, you'll be able to target those specific audiences who are more likely to hear the celebrity’s recommendation and act upon them.Drug abuse is severely affecting society and causes major losses in terms of treatment and rehabilitation costs, lost productivity and an increased crime rate.Recently, governmental and non-governmental organizations have utilized social marketing to extend Anti-drug instruction in expansion as progressing treatment and rehabilitation methods for drug abusers, as the Voluntary Fund for the Control and Treatment of Addiction and Abuse of the Ministry of Social Solidarity organized an awareness initiative against the risks of addiction under the title “You are stronger than Drugs », the initiative aimed to coach youngsters about the negative effects of drug use, the champions of the advertising campaign were the famous football player "Mohamed Salah" and the famous actor "Mohamed Ramadan". The research will study this campaign with study and analysis.

KYGUOLIENĖ Asta, BRAZIULYTĖ Reda

Augmented reality is considered to be one of the fastest growing and most promising trends in marketing. This paper analyses the use of augmented reality for advertising purposes by integrating augmented reality into product packaging. There are solutions for the application of augmented reality in product packaging around the world, but in Lithuania, the application of augmented reality in product packaging is still rare. Research problem is formulated as the question: what are the challenges and development opportunities for the application of augmented reality in product packaging? Expert opinion and the results of a customer survey, which show at which stages of the AIDA model using augmented reality in product packaging could be effective, are presented in this article.

Umberto Minora, Claudio Bosco, Stefano Iacus et al.

This work contributes to the discussion on how innovative data can support a fast crisis response. We use operational data from Facebook to gain useful insights on where people fleeing Ukraine following the Russian invasion are likely to be displaced, focusing on the European Union. In this context, it is extremely important to anticipate where these people are moving so that local and national authorities can better manage challenges related to their reception and integration. By means of the Ukrainian-speaking Monthly Active Users estimates provided by Facebook advertising platform, we analyse the flows of people fleeing the country towards the European Union. At the fifth week since the beginning of the war, our results indicate an increase in the number of Ukrainian-speaking Facebook users in all the EU countries, with Poland registering the highest percentage share ($33\%$) of the overall increase, followed by Germany ($17\%$), and Czechia ($15\%$). We assess the reliability of prewar Facebook estimates by comparison with official statistics on the Ukrainian diaspora, finding a strong correlation between the two data sources (Pearson's $r=0.93$, $p<0.0001$). We then compare our results with data on arrivals in Poland and Hungary reported by the UNHCR, and we observe a similarity in their trend. In conclusion, we show how Facebook advertising data could offer timely insights on international mobility during crisis, supporting initiatives aimed at providing humanitarian assistance to the displaced people, as well as local and national authorities to better manage their reception and integration.