Billboard Advertising has emerged as an effective out-of-home advertising technique, where the goal is to select a limited number of slots and play advertisement content there, with the hope that it will be observed by many people and, effectively, a significant number of them will be influenced towards the brand. Given a trajectory and a billboard database and a positive integer $k$, how can we select $k$ highly influential slots to maximize influence? In this paper, we study a variant of this problem where a commercial house wants to make a promotion of multiple products, and there is an influence demand for each product. We have studied two variants of the problem. In the first variant, our goal is to select $k$ slots such that the respective influence demand of each product is satisfied. In the other variant of the problem, we are given with $\ell$ integers $k_1,k_2, \ldots, k_{\ell}$, the goal here is to search for $\ell$ many set of slots $S_1, S_2, \ldots, S_{\ell}$ such that for all $i \in [\ell]$, $|S_{i}| \leq k_i$ and for all $i \neq j$, $S_i \cap S_j=\emptyset$ and the influence demand of each of the products gets satisfied. We model the first variant of the problem as a multi-submodular cover problem and the second variant as its generalization. To solve the common-slot variant, we formulate the problem as a multi-submodular cover problem and design a bi-criteria approximation algorithm based on the continuous greedy framework and randomized rounding. For the disjoint-slot variant, we proposed a sampling-based approximation approach along with an efficient primal-dual greedy algorithm that enforces disjointness naturally. Extensive experiments with real-world trajectory and billboard datasets highlight the effectiveness and efficiency of the proposed solution approaches.
Mónika Imreh-Tóth, Vinod Sharma, Sanjib Bhattacharjee
et al.
This study explores how social media platforms (for example, Instagram) and Reels-sponsored ads influence what makes consumers stop, watch and decide to buy. Using Consumer Engagement Theory (CET) as a lens, it looks at how people emotionally, cognitively and behaviourally respond to short-form video content. Four key factors were examined: engaging content, scenario-based experiences, user participation and perceived usefulness. Data from 393 active social media platform users in India’s National Capital Region revealed that all four elements positively shaped consumer attitudes, significantly influencing purchase intentions. Notably, relatable and emotionally engaging content had the most substantial impact. Attitude played a central role, bridging how consumers feel about a Reel and what they choose to do next. For marketers, the takeaway is clear: Reels that are visually appealing, useful and invite interaction are more likely to turn engagement into action. The study offers timely insights into how brands can connect meaningfully through short-form video.
The rapid advancement of Artificial Intelligence Generated Content (AIGC) techniques has unlocked opportunities in generating diverse and compelling advertising images based on referenced product images and textual scene descriptions. This capability substantially reduces human labor and production costs in traditional marketing workflows. However, existing AIGC techniques either demand extensive fine-tuning for each referenced image to achieve high fidelity, or they struggle to maintain fidelity across diverse products, making them impractical for e-commerce and marketing industries. To tackle this limitation, we first construct AdProd-100K, a large-scale advertising image generation dataset. A key innovation in its construction is our dual data augmentation strategy, which fosters robust, 3D-aware representations crucial for realistic and high-fidelity image synthesis. Leveraging this dataset, we propose RefAdGen, a generation framework that achieves high fidelity through a decoupled design. The framework enforces precise spatial control by injecting a product mask at the U-Net input, and employs an efficient Attention Fusion Module (AFM) to integrate product features. This design effectively resolves the fidelity-efficiency dilemma present in existing methods. Extensive experiments demonstrate that RefAdGen achieves state-of-the-art performance, showcasing robust generalization by maintaining high fidelity and remarkable visual results for both unseen products and challenging real-world, in-the-wild images. This offers a scalable and cost-effective alternative to traditional workflows. Code and datasets are publicly available at https://github.com/Anonymous-Name-139/RefAdgen.
Talent management (TM) is conceptualized as an organization’s ability to acquire and retain skilled employees for academic competitive advantage. Both organizations and employees tend to have a competitive advantage when organizations attract the right employees and develop strategies to meet the organization’s vision and mission. However, there is a dearth of studies on TM models for faith-based Higher Educational institutions (HEIs). Therefore, this study aimed to develop a TM model because many organizations emphasize effective TM to retain high value for organizational productivity and employee efficiency. The study is anchored on attractive quality theory. A qualitative single-case study design was employed to understand how faith-based HEIs identify and retain talented employees. Purposeful sampling was used to select ten participants, including administrators, faculty, staff, and students. The data were collected using semi-structured interviews conducted in person, audio-recorded, and transcribed verbatim. The data were analyzed with the assistance of HyperRESEARCH, a qualitative data analysis software. The findings suggest that faith-based HEIs attract and retain talented employees through an appealing institutional mission and effective advertising.
We consider a class of optimal advertising problems under uncertainty for the introduction of a new product into the market, on the line of the seminal papers of Vidale and Wolfe, 1957, and Nerlove and Arrow, 1962. The main features of our model are that, on one side, we assume a carryover effect (i.e. the advertisement spending affects the goodwill with some delay); on the other side we introduce, in the state equation and in the objective, some mean field terms which take into account the presence of other agents. We take the point of view of a planner who optimizes the average profit of all agents, hence we fall into the family of the so-called "Mean Field Control" problems. The simultaneous presence of the carryover effect makes the problem infinite dimensional hence belonging to a family of problems which are very difficult in general and whose study started only very recently, see Cosso et Al, 2023. Here we consider, as a first step, a simple version of the problem providing the solutions in a simple case through a suitable auxiliary problem.
With the increase in the fluency of ad texts automatically created by natural language generation technology, there is high demand to verify the quality of these creatives in a real-world setting. We propose AdTEC (Ad Text Evaluation Benchmark by CyberAgent), the first public benchmark to evaluate ad texts from multiple perspectives within practical advertising operations. Our contributions are as follows: (i) Defining five tasks for evaluating the quality of ad texts, as well as building a Japanese dataset based on the practical operational experiences of building a Japanese dataset based on the practical operational experiences of advertising agencies, which are typically kept in-house. (ii) Validating the performance of existing pre-trained language models (PLMs) and human evaluators on the dataset. (iii) Analyzing the characteristics and providing challenges of the benchmark. The results show that while PLMs have already reached practical usage level in several tasks, humans still outperform in certain domains, implying that there is significant room for improvement in this area.