Advertising Platform Research Articles

A Hybrid Model for Commercial Brand Marketing Prediction Based on Multiple Features with Image Processing

Recently, deep learning has been employed in automatic feature extraction and has made remarkable achievements in the fields of computer vision, speech recognition, natural language processing, and artificial intelligence. Compared with the traditional shallow model, deep learning can automatically extract more complex features from simple features, which reduces the intervention of artificial feature engineering to a certain extent. With the development of the Internet and e-commerce, picture advertising, as an important form of display advertising, has the characteristics of high visibility, strong readability, and easy-to-obtain user recognition. An increasing number of Internet companies are paying attention to what kind of advertising pictures can attract more clicks. Based on deep learning technology, this paper studies the prediction model of click-through rate (CTR) for advertising and proposes an end-to-end CTR prediction depth model for display advertising, which integrates the feature extraction of display advertising and CTR prediction to directly predict the probability of an advertisement image being clicked by users. This paper studies the deep-seated nonlinear characteristics through the multilayer network structure of the deep network and carries out several groups of experiments on the private display advertising data set of a commercial advertising platform. The results show that the model proposed in this paper can effectively improve the prediction accuracy of CTR compared with other benchmark models and predict whether an advertisement is clicked or not by given advertisement information and user information. By establishing a reasonable advertising click-through rate prediction model, it can help the platform estimate future revenue so as to make cooperative decisions with advertisers. For advertisers, it is necessary to evaluate the price by predicting the click-through rate and estimate the bidding price of their own advertisements.

The Perceived Effectiveness of Visual Communication Elements in Cinema Video Advertisements: Demographic Factors Analysis​

The evolution in advertising has shifted the reliance of advertisers from printed materials to video advertisements. Globally, various industries especially the cinema industry, have adopted this form of advertising to meet their marketing goals. Yet, video advertisements are still considered ineffective. Thus, the study set out to find out the perceptions of the cinema viewers on visual communication elements in cinema video advertisements, analyse the effectiveness of the cinema video and determine the mean differences in the effectiveness of the elements in video advertisements across gender, education level and age. It is a cross-sectional survey study using the causal-comparative approach. 240 Universiti Teknologi MARA (UiTM) Shah Alam and Management and Science University (MSU) students participated in the study. TGV Cinemas and Golden Screen Cinemas were selected as the sampled video advertisements. The questionnaire used was adapted from [2], [3] and [33]. It was revealed that the image and video shots were positively perceived as the most effective communication elements in delivering the messages compared to other visual communication elements and the cinema video advertisements were comprehensible, yet they were not highly effective as an advertising platform since the advertising objectives were not achieved. In addition, the analysis of the demographic factors showed that there were significant mean differences between the four visual communication elements and age groups. Thus, the study suggests advertisers incorporate more creativity in producing interactive and immersive video advertisements to optimise the effectiveness of their advertisements.

Is a “6-second” advertisement reasonable? Acceptable mobile advertisement length for consumers

The increasing number of mobile video advertisements has led consumers declining to watch them, making it essential that companies work to reverse this trend. A short-length advertisement platform has been launched to address this problem, but reducing the advertisement length would reduce the effectiveness of the advertising. As such, this study quantitatively analyzes the acceptable length of an advertisement, the attributes affecting consumer acceptance, and the extent to which they affect consumer acceptance. We use a mixed logit model with data collected from a choice experiment, and analyze consumers’ willingness to receive monetary rewards to watch mobile advertisements using the concept of willingness to accept (WTA). The results show that consumers’ marginal WTA for watching a single advertisement is KRW 2.389 (USD 0.00212) and WTA for one second of advertisement is KRW 0.484 (USD 0.00043). Therefore, we can derive that the appropriate length of one mobile advertisement, from the consumer’s perspective, is 4.94 s. Furthermore, we analyze the effect of advertisement length on consumers’ utility using individual characteristics. Based on the results, advertising companies can provide customized advertisement lengths to meet consumer characteristics, reducing consumer avoidance behavior and improving the efficiency of advertisements.

Online Advertising Platforms and Consumer Buying Behaviour towards Consumer Goods in Aba, Abia State, Nigeria

The study examined online advertising platforms and consumer buying behaviour towards consumer goods in Aba, Abia State. The study specifically examined how social media advertising, e-mail advertising, and social networks advertising affect consumer buying behaviour. The study adopted a survey research design and a simple sampling technique. A population of 339,076 customers between 15 – 84 years of age was used for the study. Taro Yamane formula were used to determine a sample of 400 from the population. However, 366 questionnaires were returned from the respondents, forming the study's sample size. Primary data was sourced from the administration of questionnaires to customers in Aba North and Aba South, Abia State. A test-re-test and Cronbach Alpha test were conducted to ensure validity and reliability of the data. The data were presented using simple percentages and frequencies. The hypotheses were tested using simple and multiple regression models (SPSS). The descriptive result shows that 62.3% of the respondents strongly agree that advertising through Facebook pages ignites customers' desire to purchase a product. The majority of the respondents (63.1%) strongly agree that advertisements done on yahoo mail platforms with catchy slogans or phrases appealed to customers' interest and emotions. Most of the respondents (60.7%) strongly agree that Advertisement on Google Play and Google search engine increases customer demand for products. The empirical result revealed that Facebook advertising and WhatsApp advertising positively and significantly affect consumers' desire for a product in Aba, Abia State. Instagram advertising does not significantly affect consumers' desire for a product in Aba, Abia State. E-mail advertising significantly affects consumers' trial purchase of a product in Aba, Abia State. More so, Search engine advertising and Blog advertising positively and significantly affect consumers' decision to adopt a product. Junk message advertising does not significantly affect consumers' decision to adopt a product, Aba, Abia State. In conclusion, social media advertising significantly affects consumer buying behaviour. This is because, Social media often become the second or even first place that customers will look when wanting to learn more about a brand, so it is important that all information and the brand's voice match up across the website and social media channels. It was recommended that businesses should fully embrace social media advertising as social media has come to stay, and advertising via the media is becoming a "global best practice". They will not only reap the benefits of advertising via social media but also be at par with their counterparts in Diaspora.

Influencer Marketing on Instagram: A Sequential Mediation Model of Storytelling Content and Audience Engagement via Relatability and Trust

Storytelling content is where the facts are conveyed by emotion and that make people more engaged and want to take action or change their surroundings. Stories fascinate people and can easily be remembered compared to the facts alone. The much-hyped feature “stories” of Instagram, a trendy social media platform, has become a game-changer for influencer marketing. The present study extends reactance theory in the context of Instagram’s millennial users. Previous researchers have tested the effectiveness of the stories feature of this particular social media platform. Therefore, in line with the earlier studies, we propose a sequential mediation model that investigates the effect of storytelling content (made by Instagram Influencers) on audience engagement using two sequential mediation mechanisms of relatability and trust. Data were obtained using a cross-sectional study design from 273 millennial users of Instagram. Our results justify the direct and indirect hypothesized relationship through Process Macros. We found that relatability and trust play a significant role in building a strong relationship between storytelling content and audience engagement. Ultimately, the research findings suggest that professionals should be more creative while making the content on Instagram to engage the millennial market. Moreover, this research has tried to fill the gap in the literature on Instagram “stories” as an advertising platform.

Smart Out-of-Home Advertising Using Artificial Intelligence and GIS Data

This demonstration paper introduces the Smart Out-of-Home Advertising Platform (SOAP), which leverages Geographic Information Systems (GIS) data and state-of-the-art Artificial Intelligence (AI) approaches to provide: (i) a documented, data-informed pricing model for billboards, which can be used to justify billboard prices to advertisers; and (ii) a set of non-dominated solutions (each corresponding to a different allocation of billboards to a given campaign) that explores the trade-offs between multiple conflicting objectives (e.g., cost and coverage). To the best of our knowledge, SOAP is the first to tackle such challenges in the context of Multi Objective Optimization (MOO).

Is TikTok a public sphere for democracy in China? A Political Economy Approach

This study aims to investigate whether TikTok can be regarded as a new public sphere for democracy in the Chinese media context. Previous studies focused on the investigation of Weibo (a Chinese counterpart of Twitter) as a public sphere. However, Jia and Han (2020) argue that Weibo is not an online space for public discussion anymore but a platform for marketing and advertising. With the commercialization of social media, plenty of researchers paid attention to TikTok’s commodification and its commercial implications, while research on the role of TikTok as a public sphere is still limited. By adopting theoretical frameworks from “public sphere” and “political economy”, this study questions: 1) why users participate in public issues on TikTok? 2) how do citizens use TikTok to participate online? 3) does TikTok contribute to the creation of a public sphere? The empirical method, 20 semi-structured interviews around China, is utilized to understand citizens’ views and participation behavior. This study argues that the reason why users participate in public issues on TikTok is entertainment. Users are attracted by the platform which offers creative and humorous videos to disseminate public information. Getting interested in its entertainment feature, users utilize TikTok to view pubic-related videos. According to respondents of the interview, 18 out of 20 users indicate they seldom use the “search button” or “create button” on TikTok, rather, they merely browse videos there. In this sense, TikTok is not a public sphere because of lacking critical interactions. In contrast to Habermas’s claim that social media is a “pseudo-public sphere” (Habermas, 1989), this study describes TikTok as a “limited public sphere” which do, to some extent, generate public discussions and debates about socio-political issues directly or indirectly. Nevertheless, the social impact of this sphere is restricted, that is, online public engagement is confined to liking, sharing, and short commenting lacking in critical discussions, and is ineffective in transferring online political engagement to offline participation (Kim and Ellison, 2021). Thus, TikTok facilitates citizens’ political engagement superficially and it is harder for that engagement to have any subversive impact on democracy.

Predicting Actions of Users Using Heterogeneous Online Signals.

Advertising platforms have a growing need for improving prediction quality, as missing out on ad opportunities can have a negative effect on their performance. To that end, prediction tasks such as conversion prediction need to be continuously advanced through the inclusion of data from new sources or through algorithmic development that tackles existing challenges. The introduction of different data sources naturally brings unwanted noise, whereas underexplored areas still exist in modeling approaches, such as temporal information of events in sequences. In this study, we propose extensions for modeling online user activity trails that address two very important aspects of activities-time and noise, through dedicated layers that can be used in existing deep sequence-learning approaches. Our proposed method exhibited area under the receiver operating characteristic curve improvement of up to 3% and 1.75% compared with production and best baseline approaches, respectively, across two major advertiser data sets and several predictive tasks.

The end of social media? How data attraction model in the algorithmic media reshapes the attention economy

Douyin, which is also known as the Chinese version of Tiktok, is currently the most valuable digital advertisement platform in China. One of the most significant features of this short-video platform is the heavy reliance on algorithmic production and distribution of media. In this emergent configuration, algorithms and data shape the production and circulation of media beyond social networks. Such a system develops by meshing grassroots and professionally generated content, leading to the audience engaging in the production of commercial content for profit. My essay explores the political context and economic logic that underpins these developments. It draws specifically on official reports from Douyin, as well as interviews with users, including individual users and Multiple Channel Network (MCN) employees. This essay proposes the idea of the ‘data attraction model’ based on the investigation of the emergence of new forms of algorithmic production and distribution. It argues that the data attraction model is characterised by an extreme logic of flexible accumulation, which is radically transforming the content production of participatory media in China

APLIKASI DIGITAL MARKETING UNTUK MENINGKATKAN OMZET PENJUALAN PRODUK KERAMIK HANDMADE NARUNA

The ceramic products discussed in this paper are hand-processed ceramic tableware with a competitive advantage in using the glazing technique at Naruna Ceramic Studio (NCS), Salatiga, Central Java, Indonesia. This technique is a coloring method after the first combustion of a ceramic kiln that can give a characteristic to the product giving an attractive and elegant color impression that can increase sales turnover and competitiveness of NCS compared to similar. The COVID-19 pandemic that continues to exist in Indonesia forces the number of customer sales to not be able to interact directly with NCS, so it is necessary to change a new marketing strategy carried out by NCS to increase turnover. The digital marketing method in this paper is applied by the author and NCS to market handmade ceramic tableware products with a finishing process using the glazing technique. The Digital Marketing System applied by NCS and researchers is to build an advertising platform that can influence consumer behavior so that it can make a significant contribution to increasing the turnover of this ceramic product seller. The ads that posted on Facebook, Instagram, YouTube, TikTok, and Website will be done will be based on the landing page which ends in the marketplace (Shopee, Tokopedia) and the WhatsApp application which is managed by NCS customer service. The final results up to October 2020 showed an increase in turnover of 65% after the implementation of this DM was implemented

The interplay of migration and cultural similarity between countries: Evidence from Facebook data on food and drink interests.

Migration has been proposed as one of the factors that shape cultural similarities across countries. However, studying the relationship between culture and migration has been challenging, in part because culture is difficult to quantify. The traditionally used survey questionnaires have a number of drawbacks, including that they are costly and difficult to scale to a large number of countries. To complement survey data, we propose the use of passively-collected digital traces from social media. We focus on food and drink as markers of a country’s culture. We then measure similarities between countries in terms of food and drink interests using aggregated data from the Facebook Advertising Platform. Methodologically, we offer approaches to measure the similarity between countries with both symmetric and asymmetric indices. Substantively, we assess the association between migration cultural similarity between countries by comparing our measure of cultural similarity with international migration data. In most countries, larger immigrant populations are associated with more similar food and drink preferences between their country of origin and the country of destination. Our results suggest that immigrants contribute to bringing the culture of their home countries to new countries. Moreover, our study identifies considerable variability in this pattern across countries.

Case Study Application of Business Intelligence in Digital Advertising

This paper demonstrates the application of business intelligence in decision-making in digital advertising through a case study. Data used for analysis was collected during a test phase of an advertising platform. The study analyzes multiple types of traffic, related to countries, browsers, household incomes, and days of a week. Beside tabular reports, the paper presents how to visualize those results using Python libraries to make them more visually appealing. Furthermore, logistic regression was used to build models to detect relationships between the number of impressions and clicks. Finally, the authors propose multiple combinations of data that could be used to create different reports that lead to smarter decision-making and cost-effectiveness.

Return on investment and return on ad spend at the action level of AIDA using last touch attribution method on digital advertising platforms

Return on investment and return on ad spend at the action level of AIDA using last touch attribution method on digital advertising platforms

Return on investment and return on ad spend at the action level of AIDA using last touch attribution method on digital advertising platforms

Return on investment and return on ad spend at the action level of AIDA using last touch attribution method on digital advertising platforms

The New Media Marketing Strategies of Chinese Low-Budget Art House Films

Marketing is a vital part of the film industry; film marketing uses various methods to let audiences know about a film and then go to the cinema to watch it. Compared with the western film industry, the marketing consciousness in the Chinese film industry started late. Also, due to the relatively special art form, the low-budget art house films always failed to carry out effective marketing with cost restrictions as a result. However, emerging social media offers new opportunities for the marketing of low-budget art house films, which provides filmmakers with a convenient and inexpensive advertising platform. New media film marketing is more and more involved in the whole process of film production and distribution, and it affects the box office results to a great extent. Therefore, the new media film marketing plays an important role in the development of the film industry. The research on how Chinese low-budget art house films use new media for marketing is very useful for the Chinese film industry.

A Framework for Estimating Migrant Stocks Using Digital Traces and Survey Data: An Application in the United Kingdom.

An accurate estimation of international migration is hampered by a lack of timely and comprehensive data, and by the use of different definitions and measures of migration in different countries. In an effort to address this situation, we complement traditional data sources for the United Kingdom with social media data: our aim is to understand whether information from digital traces can help measure international migration. The Bayesian framework proposed is used to combine data from the Labour Force Survey (LFS) and the Facebook Advertising Platform to study the number of European migrants in the United Kingdom, with the aim of producing more accurate estimates of the numbers of European migrants. The overarching model is divided into a Theory-Based Model of migration and a Measurement Error Model. We review the quality of the LFS and Facebook data, paying particular attention to the biases of these sources. The results indicate visible yet uncertain differences between model estimates using the Bayesian framework and individual sources. Sensitivity analysis techniques are used to evaluate the quality of the model. The advantages and limitations of this approach, which can be applied in other contexts, are discussed. We cannot necessarily trust any individual source, but combining them through modeling offers valuable insights.

Commercial Companies in Party Networks: Digital Advertising Firms in US Elections from 2006-2016

ABSTRACT Previous research has found that digital advertising companies such as Facebook and Google function similarly to political consultants, influencing the messaging choices of political clients. This paper situates those insights in the theory of parties as extended networks and presents the first quantitative descriptive analysis of all companies that have provided federal political committees with digital advertising services in national elections. Network analysis measures of political groups registered with the Federal Election Committee in the United States (n = 2,064) and the types of companies they hired for digital political advertising services (political agencies, commercial agencies, digital advertising platforms, or other; n = 1,022) over three midterm and general elections (2006–2016) show that the number of political committees and companies have both dramatically increased since 2008 and that Facebook and Google have become the two most central members of the network. As influencers of the targeting and content of campaign messages, these companies should be considered consequential members of electoral party 0networks. This study contributes to research on political consulting and to the theory of parties as extended networks by demonstrating how opening the inclusion criteria for subject selection can uncover unexpected players, such as the private, previously considered nonpartisan, nonpolitical companies present here.

BotSpot++: A Hierarchical Deep Ensemble Model for Bots Install Fraud Detection in Mobile Advertising

Mobile advertising has undoubtedly become one of the fastest-growing industries in the world. The influx of capital attracts increasing fraudsters to defraud money from advertisers. Fraudsters can leverage many techniques, where bots install fraud is the most difficult to detect due to its ability to emulate normal users by implementing sophisticated behavioral patterns to evade from detection rules defined by human experts. Therefore, we proposed BotSpot 1 for bots install fraud detection previously. However, there are some drawbacks in BotSpot, such as the sparsity of the devices’ neighbors, weak interactive information of leaf nodes, and noisy labels. In this work, we propose BotSpot++ to improve these drawbacks: (1) for the sparsity of the devices’ neighbors, we propose to construct a super device node to enrich the graph structure and information flow utilizing domain knowledge and a clustering algorithm; (2) for the weak interactive information, we propose to incorporate a self-attention mechanism to enhance the interaction of various leaf nodes; and (3) for the noisy labels, we apply a label smoothing mechanism to alleviate it. Comprehensive experimental results show that BotSpot++ yields the best performance compared with six state-of-the-art baselines. Furthermore, we deploy our model to the advertising platform of Mobvista, 2 a leading global mobile advertising company. The online experiments also demonstrate the effectiveness of our proposed method.

A digital platform for ethical advertising and hybrid business models for news organizations: are they greening methods for ‘news deserts’?

This article suggests that the adoption of hybrid business models coupled with the establishment of an ethical advertising platform could help counter the emergence of news deserts. The latter are geographic areas and policy issues lacking coverage because of the crisis of the commercial model of news provision. Three clusters of characteristics are used to streamline and compare business models: (1) revenue models, (2) patrons and their motives, and (3) legal frameworks for the incorporation of the organization. Hybrid business models are designed by mixing the principles underpinning the Benefit Corporation or the Low-profit Limited Liability Company (L3C), with the basic characteristics of commercial and non-profit news organizations. The term ethical advertising refers to promotional activities that non-profits and other organizations dedicated to social goals normally undertake, including marketing, fundraising, or public-awareness campaigns. Based on data published by the Internal Revenue Services, this article argues that a digital platform for ethical advertising could face a demand worth over $1 billion a year. Additionally, this platform could effectively match non-profits’ demand for an audience with a pro-social attitude with non-profit and hybrid news organizations’ need for additional revenue streams.

Analysing global professional gender gaps using LinkedIn advertising data

Although women’s participation in tertiary education and the labour force has expanded over the past decades, women continue to be underrepresented in technical and managerial occupations. We analyse if gender inequalities also manifest themselves in online populations of professionals by leveraging audience estimates from LinkedIn’s advertisement platform to explore gender gaps among LinkedIn users across countries, ages, industries and seniorities. We further validate LinkedIn gender gaps against ground truth professional gender gap indicators derived from the International Labour Organization’s (ILO) Statistical Database, and examine the feasibility and biases of predicting global professional gender gap indicators using gender gaps computed from LinkedIn’s online population. We find that women are significantly underrepresented relative to men on LinkedIn in countries in Africa, the Middle East and South Asia, among older individuals, in Science, Technology, Engineering and Mathematics (STEM) fields and higher-level managerial positions. Furthermore, a simple, aggregate indicator of the female-to-male ratio of LinkedIn users, which we term the LinkedIn Gender Gap Index (GGI), shows strong positive correlations with ILO ground truth professional gender gaps. A parsimonious regression model using the LinkedIn GGI to predict ILO professional gender gaps enables us to expand country coverage of different ILO indicators, albeit with better performance for general professional gender gaps than managerial gender gaps. Nevertheless, predictions generated using the LinkedIn population show some distinctive biases. Notably, we find that in countries where there is greater gender inequality in internet access, LinkedIn data predict greater gender equality than the ground truth, indicating an overrepresentation of high status women online in these settings. Our work contributes to a growing literature seeking to harness the ‘data revolution’ for global sustainable development by evaluating the potential of a novel data source for filling gender data gaps and monitoring key indicators linked to women’s economic empowerment.