Search Results (13)

This study examines the efficacy of a novel form of consumer-generated content (CGC) digital advertising, termed “directed” consumer-generated content (DCGC), in comparison to traditional brand-created social media advertisements. The analysis focuses on performance metrics and return on ad spend (ROAS). Data were gathered from social media campaigns incorporating both DCGC and non-CGC through a field experiment, followed by a rigorous statistical analysis to identify the most effective advertising strategies. Findings indicate that DCGC typically results in significantly higher conversion rates, increased conversions, and superior ROAS. Overall, DCGC advertisements demonstrate enhanced performance relative to non-CGC campaigns, suggesting that they represent a more strategic allocation of a brand’s marketing resources, particularly when the primary objective is to drive sales and achieve elevated conversion rates. This research contributes to the academic discourse and practical implementation of social media advertising by highlighting the advantages of DCGC as a cost-effective and efficient advertising approach for brands. Full article

The recommender system (RS) has been widely adopted in many applications, including online advertisements. Predicting the conversion rate (CVR) can help in evaluating the effects of advertisements on users and capturing users’ features, playing an important role in RS. In real-world scenarios, implicit rather than explicit feedback data are more abundant. Thus, directly training the RS with collected data may lead to suboptimal performance due to selection bias inherited from the nature of implicit feedback. Methods such as reweighting have been proposed to tackle selection bias; however, these methods omit delayed feedback, which often occurs due to limited observation times. We propose a novel likelihood approach combining the assumed parametric model for delayed feedback and the reweighting method to address selection bias. Specifically, the proposed methods minimize the likelihood-based loss using the multi-task learning method. The proposed methods are evaluated on the real-world Coat and Yahoo datasets. The proposed methods improve the AUC by 5.7% on Coat and 3.7% on Yahoo compared with the best baseline models. The proposed methods successfully debias the CVR prediction model in the presence of delayed implicit feedback. Full article

Word of mouth (WOM) is crucial in customers’ purchasing decisions and affects companies’ long-term profits. This study examines the long-term trends in companies’ dynamic pricing and profits by using the Hamiltonian function method and dynamic simulation to construct a dynamic equation. It takes into account the intensity of word of mouth faced by companies and analyzes the level of publicity and consumers’ predictions of product quality. In this paper, we also discuss the interactive processes between WOM and advertising levels, the two most prominent market factors, and their ultimate impact on companies. The experimental results show that elevated levels of external advertising can potentially prompt companies to establish higher product pricing strategies, particularly in scenarios where the intensity of word of mouth is pronounced. In the initial phases of market development, the saturation level of consumers within the market exerts a negligible influence on companies’ long-term profit margins. Conversely, the rate of natural attrition from consumers’ upper threshold of product quality expectations distinctly impacts companies’ profitability. Full article

Classifieds pose numerous challenges for recommendation methods, including the temporary visibility of ads, the anonymity of most users, and the fact that typically only one user can consume an advertised item. In this work, we address these challenges by choosing models and evaluation procedures that are considered accurate, diverse, and efficient (in terms of memory and time consumption during training and prediction). This paper aims to benchmark various recommendation methods for job classifieds, using OLX Jobs as an example, to enhance the conversion rate of advertisements and user satisfaction. In our research, we implement scalable methods and represent different approaches to the recommendations: Alternating Least Square (ALS), LightFM, Prod2Vec, RP3Beta, and Sparse Linear Methods (SLIM). We conducted A/B tests by sending millions of messages with recommendations to perform online evaluations of selected methods. In addition, we have published the dataset created for our research. To the best of our knowledge, this is the first dataset of its kind. It contains 65,502,201 events performed on OLX Jobs by 3,295,942 users who interacted with (displayed, replied to, or bookmarked) 185,395 job ads over two weeks in 2020. We demonstrate that RP3Beta, SLIM, and ALS perform significantly better than Prod2Vec and LightFM when tested in a laboratory setting. Online A/B tests also show that sending messages with recommendations generated by the ALS and RP3Beta models increases the number of users contacting advertisers. Additionally, RP3Beta had a 20% more significant impact on this metric than ALS. Full article

Social media marketing has become a crucial component of contemporary business strategies, significantly influencing brand visibility, customer engagement, and sales growth. The aim of this study is to investigate and determine the key factors guiding customer attitudes towards social media influencers, and, on that basis, to explore their effects on purchase intentions regarding advertised products or services. A total of 376 filled-in questionnaires from an online survey were analysed. The main characteristics of digital influencers’ behaviour that affect consumer perceptions have been systematized and categorized through a combination of both traditional and advanced data analysis methods. Structural equation modelling (SEM), machine learning and multi-criteria decision-making (MCDM) methods were selected to uncover the hidden dependencies between variables from the perspective of social media users. The developed models elucidate the underlying relationships that shape the acceptance mechanism of influencers’ messages. The obtained results provide specific recommendations for stakeholders across the social media marketing value chain. Marketers can make informed decisions and optimize influencer marketing strategies to enhance user experience and increase conversion rates. Working collaboratively, marketers and influencers can create impactful and successful marketing campaigns that resonate with the target audience and drive meaningful results. Customers benefit from more tailored and engaging influencer content that aligns with their interests and preferences, fostering a stronger connection with brands and potentially affecting their purchase decisions. As the perception of customer satisfaction is an individual and evolving process, stakeholders should organize regular evaluations of influencer marketing data and explore the possibilities to ensure the continuous improvement of this e-marketing channel. Full article

Real-time bidding has become a major means for online advertisement exchange. The goal of a real-time bidding strategy is to maximize the benefits for stakeholders, e.g., click-through rates or conversion rates. However, in practise, the optimal bidding strategy for real-time bidding is constrained by at least three aspects: cost-effectiveness, the dynamic nature of market prices, and the issue of missing bidding values. To address these challenges, we propose Imagine and Imitate Bidding (IIBidder), which includes Strategy Imitation and Imagination modules, to generate cost-effective bidding strategies under partially observable price landscapes. Experimental results on the iPinYou and YOYI datasets demonstrate that IIBidder reduces investment costs, optimizes bidding strategies, and improves future market price predictions. Full article

Short video platforms have problems with increased competition and low advertising conversion rates. Although social presence is closely related to consumer engagement, research regarding the impact of social presence on consumer engagement in short video-embedded advertisements is sparse. We developed a theoretical model, namely a social presence–flow experience–advertising avoidance–advertising engagement model, and explored the mechanism underlying advertising engagement from a psychological and behavioral perspective. The analysis of 563 short video users revealed that the model exhibited excellent explanatory power for advertising engagement (R² = 41.3%). Social presence can increase consumers’ advertising engagement by enhancing flow experience and reducing advertising avoidance. Meanwhile, the flow experience, by diminishing advertising avoidance, generates a serial mediation effect between social presence and advertising engagement. This study emphasizes social presence’s applicability and influence mechanism in short video-embedded advertisements, a unidirectional information delivery. It provides new theoretical perspectives and practical advice for relevant practitioners. Full article

The growing popularity of e-commerce has prompted researchers to take a greater interest in deeper understanding online shopping behavior, consumer interest patterns, and the effectiveness of advertising campaigns. This paper presents a fresh approach for targeting high-value e-shop clients by utilizing clickstream data. We propose the new algorithm to measure customer engagement and recognizing high-value customers. Clickstream data is employed in the algorithm to compute a Customer Merit (CM) index that measures the customer’s level of engagement and anticipates their purchase intent. The CM index is evaluated dynamically by the algorithm, examining the customer’s activity level, efficiency in selecting items, and time spent in browsing. It combines tracking customers browsing and purchasing behaviors with other relevant factors: time spent on the website and frequency of visits to e-shops. This strategy proves highly beneficial for e-commerce enterprises, enabling them to pinpoint potential buyers and design targeted advertising campaigns exclusively for high-value customers of e-shops. It allows not only boosts e-shop sales but also minimizes advertising expenses effectively. The proposed method was tested on actual clickstream data from two e-commerce websites and showed that the personalized advertising campaign outperformed the non-personalized campaign in terms of click-through and conversion rate. In general, the findings suggest, that personalized advertising scenarios can be a useful tool for boosting e-commerce sales and reduce advertising cost. By utilizing clickstream data and adopting a targeted approach, e-commerce businesses can attract and retain high-value customers, leading to higher revenue and profitability. Full article

Using big data analysis, we study the consumer life cycle based on the following four aspects: customer acquisition, participation, profit, and return visit rate. The Google Merchandise store is selected as a case study to collect data during January–December 2022. Thirteen traffic source dimension elements of the four layers were summarized and analyzed, and the following results were obtained. Consumers complete a conversion rate of 57 million. The late contact point affects the conversion rate, which is much higher than that in the early and middle periods. Reducing the number of touchpoints in the conversion increases the revenue. Understanding customers’ shopping habits helps improve advertising results. Thus, website managers need to introduce Google Analytics 4 analytics at different stages for site quality and business effectiveness. Full article

The Web has provided an increasing proportion of use as a medium for e-commerce in addition to various recommender systems. It can be used for analyzing recommendation system-based feedback (e.g., a form in which a user inputs their preferences for various items as numerical values into a specific evaluation system) to estimate customer interest; in addition, analyzing multi-modal types of feedback (e.g., product purchase traces, inquiry lists, inquiry times, and comments) with deep learning can also be used to estimate user interest. As many companies around the world promote their products through micro-influencers on the Web, related research has continued to predict the purchase conversion rate of the influencer through a variety of technologies. In this work, we present a multi-modal micro-influencer analysis scheme for a marketing maximization strategy. Our scheme uses the multi-modal data stored in Mecha Solution’s own shopping mall of Korea, as well as famous Korean Internet platforms, and Coupang, Naver, and Oliveyoung’s data such as article posting comments and statistics information. By extracting the main characteristics of the real article postings from real users as opposed to those from factitious influencers posting articles and comments and identifying articles other than advertisements, influencer scores are obtained, assuming that articles other than advertisements can further increase the purchase conversion rate. Based on influencer score, we propose a multi-modal micro-influencer analysis scheme that recommends influencers use content-based collaborative filtering and user-based collaborative filtering for items that the influencer has not yet reviewed. The experiment was implemented to prove that the proposed scheme successfully achieves this goal. Full article

With many outdated hotels in urgent need of refurbishment in China, chain hotel groups are under mounting pressure to expand their market share by strengthening advertising performance. This study aims to explore the effects of sender types and anonymous clues on advertising exposure as well as the impacts of the above factors and content narratives on service conversion (e.g., link clicks) for hotel franchise promotion. In addition to increasing exposure action, use of the AA-IDA model can effectively increase the possibility of hotel advertising conversion. Two experiments were employed to examine the impacts of advertising design factors on exposure and conversion rates of hotel franchise promotion. A behavioral experiment and a field experiment were carried out to examine the critical effect of advertising design factors on advertising exposure and conversion. The Wald tests for parameters show that the effect of anonymity on advertising conversion was significant (β = 0.479, p < 0.01). Objective content narratives had a significant positive impact on advertising conversion (β = 0.594, p < 0.01). Furthermore, The ANOVA results show that hoteliers in groups with different design elements applied had significant differences in post-conversion service usage (F = 33.809, p < 0.001). The AA-IDA model provides a new framework for future hotel franchise promotion research. Additionally, the important design factors of promotional ads and their reorganization (e.g., sender types, anonymous clues, and content narratives) had a significant impact on the view action and conversion action. Full article

Digital advertising on social media officially surpassed traditional advertising and became the largest marketing media in many countries. However, how to maximize the value of the overall marketing budget is one of the most concerning issues of all enterprises. The content of the Facebook photo post needs to be analyzed effectively so that the social media companies and managers can concentrate on handling their fan pages. This research aimed to use text mining techniques to find the audience accurately. Therefore, we built a topic model recommendation system (TMRS) to analyze Facebook posts by sorting the target posts according to the recommended scores. The TMRS includes six stages, such as data preprocessing, Chinese word segmentation, word refinement, TF-IDF word vector conversion, creating model via Latent Semantic Indexing (LSI), or Latent Dirichlet Allocation (LDA), and calculating the recommendation score. In addition to automatically selecting posts to create advertisements, this model is more effective in using marketing budgets and getting more engagements. Based on the recommendation results, it is verified that the TMRS can increase the engagement rate compared to the traditional engagement rate recommended method (ERRM). Ultimately, advertisers can have the chance to create ads for the post with potentially high engagements under a limited budget. Full article

With the development of online advertising technology, the accurate targeted advertising based on user preferences is obviously more suitable both for the market and users. The amount of conversion can be properly increased by predicting the user’s purchasing intention based on the advertising Conversion Rate (CVR). According to the high-dimensional and sparse characteristics of the historical behavior sequences, this paper proposes a LSLM_LSTM model, which is for the advertising CVR prediction based on large-scale sparse data. This model aims at minimizing the loss, utilizing the Adaptive Moment Estimation (Adam) optimization algorithm to mine the nonlinear patterns hidden in the data automatically. Through the experimental comparison with a variety of typical CVR prediction models, it is found that the proposed LSLM_LSTM model can utilize the time series characteristics of user behavior sequences more effectively, as well as mine the potential relationship hidden in the features, which brings higher accuracy and trains faster compared to those with consideration of only low or high order features. Full article