Major advertising platforms recently increased privacy protections by limiting advertisers' access to individual-level data. Instead of providing access to granular raw data, the platforms only allow a limited number of aggregate queries to a dataset, which is further protected by adding differentially private noise. This paper studies whether and how advertisers can design effective targeting policies within these restrictive privacy preserving data environments. To achieve this, I develop a probabilistic machine learning method based on Bayesian optimization, which facilitates dynamic data exploration. Since Bayesian optimization was designed to sample points from a function to find its maximum, it is not applicable to aggregate queries and to targeting. Therefore, I introduce two innovations: (i) integral updating of posteriors which allows to select the best regions of the data to query rather than individual points and (ii) a targeting-aware acquisition function that dynamically selects the most informative regions for the targeting task. I identify the conditions of the dataset and privacy environment that necessitate the use of such a "smart" querying strategy. I apply the strategic querying method to the Criteo AI Labs dataset for uplift modeling (Diemert et al., 2018) that contains visit and conversion data from 14M users. I show that an intuitive benchmark strategy only achieves 33% of the non-privacy-preserving targeting potential in some cases, while my strategic querying method achieves 97-101% of that potential, and is statistically indistinguishable from Causal Forest (Athey et al., 2019): a state-of-the-art non-privacy-preserving machine learning targeting method.

Large Language Models (LLMs) have revolutionized the process of customer engagement, campaign optimization, and content generation, in marketing management. In this paper, we explore the transformative potential of LLMs along with the current applications, future directions, and strategic recommendations for marketers. In particular, we focus on LLMs major business drivers such as personalization, real-time-interactive customer insights, and content automation, and how they enable customers and business outcomes. For instance, the ethical aspects of AI with respect to data privacy, transparency, and mitigation of bias are also covered, with the goal of promoting responsible use of the technology through best practices and the use of new technologies businesses can tap into the LLM potential, which help growth and stay one step ahead in the turmoil of digital marketing. This article is designed to give marketers the necessary guidance by using best industry practices to integrate these powerful LLMs into their marketing strategy and innovation without compromising on the ethos of their brand.

In the field of telecommunications and cloud communications, accurately and in real-time detecting whether a human or an answering machine has answered an outbound call is of paramount importance. This problem is of particular significance during campaigns as it enhances service quality, efficiency and cost reduction through precise caller identification. Despite the significance of the field, it remains inadequately explored in the existing literature. This paper presents an innovative approach to answering machine detection that leverages transfer learning through the YAMNet model for feature extraction. The YAMNet architecture facilitates the training of a recurrent-based classifier, enabling real-time processing of audio streams, as opposed to fixed-length recordings. The results demonstrate an accuracy of over 96% on the test set. Furthermore, we conduct an in-depth analysis of misclassified samples and reveal that an accuracy exceeding 98% can be achieved with the integration of a silence detection algorithm, such as the one provided by FFmpeg.

Artificial Intelligence (AI) has revolutionized food marketing by providing advanced techniques for personalized recommendations, consumer behavior prediction, and campaign optimization. This paper explores the shift from traditional advertising methods, such as TV, radio, and print, to AI-driven strategies. Traditional approaches were successful in building brand awareness but lacked the level of personalization that modern consumers demand. AI leverages data from consumer purchase histories, browsing behaviors, and social media activity to create highly tailored marketing campaigns. These strategies allow for more accurate product recommendations, prediction of consumer needs, and ultimately improve customer satisfaction and user experience. AI enhances marketing efforts by automating labor-intensive processes, leading to greater efficiency and cost savings. It also enables the continuous adaptation of marketing messages, ensuring they remain relevant and engaging over time. While AI presents significant benefits in terms of personalization and efficiency, it also comes with challenges, particularly the substantial investment required for technology and skilled expertise. This paper compares the strengths and weaknesses of traditional and AI-driven food marketing techniques, offering valuable insights into how marketers can leverage AI to create more effective and targeted marketing strategies in the evolving digital landscape.

This paper presents the Customer Experience (CX) Simulator, a We focus on the potential of LLMs to solve this issue. LLMs have novel framework designed to assess the effects of untested webmarketing been applied not only for natural language processing tasks [12] but campaigns through user behavior simulations. The proposed also for common sense reasoning in multi-modal data [34]. We believe framework leverages large language models (LLMs) to represent that the ability of LLMs, especially to represent the high-level various events in a user's behavioral history, such as viewing an semantics of complex event descriptions with compact embedded item, applying a coupon, or purchasing an item, as semantic embedding vectors (i.e., LLM embeddings) [15], can also be advantageous for vectors. We train a model to predict transitions between events web marketing applications from their LLM embeddings, which can even generalize to unseen In this work, we propose a novel framework named CXSimulator events by learning from diverse training data.

As AI-generated images spread across entertainment, marketing, social media and other industries that shape cultural norms, The Washington Post set out to understand how this technology defines one of society's most indelible standards: female beauty. Every image in this story shows something that doesn't exist in the physical world and was generated using one of three text-to-image artificial intelligence models: DALL-E, Midjourney or Stable Diffusion. Using dozens of prompts on three of the leading image tools -- MidJourney, DALL-E and Stable Diffusion -- The Post found that they steer users toward a startlingly narrow vision of attractiveness. Prompted to show a "beautiful woman," all three tools generated thin women, without exception. Just 2 percent of the images showed visible signs of aging.

In this paper, we explore a new way for user targeting, where non-expert marketers could select their target users solely given demands in natural language form. The key to this issue is how to transform natural languages into practical structured logical languages, i.e., the structured understanding of marketer demands. Considering the impressive natural language processing ability of large language models (LLMs), we try to leverage LLMs to solve this issue. Past research indicates that the reasoning ability of LLMs can be effectively enhanced through chain-of-thought (CoT) prompting. But existing methods still have some limitations: (1) Previous methods either use simple "Let's think step by step" spells or provide fixed examples in demonstrations without considering compatibility between prompts and questions, making LLMs ineffective in some complex reasoning tasks such as structured language transformation. (2) Previous methods are often implemented in closed-source models or excessively large models, which is not suitable in industrial practical scenarios. Based on these, we propose ARALLM (i.e., Analogical Reasoning Augmented Large Language Models) consisting of two modules: Analogical Reasoning based Prompting and Reasoning-Augmented Multi-Task Model Distillation.

Natasha Kearslake, director of HR consultancy Organic P&O Solutions, told DailyMail.com January is deemed the most popular month for job searching, meaning people must make their resumes stand out among the masses. While experience, skills and education are standard, a recruitment expert has revealed six things many job seekers leave out. Natasha Kearslake, director of HR consultancy Organic P&O Solutions, told DailyMail.com Kearslake's six suggestions include adding details from the company's mission statement and forgoing fancy-looking graphics and images - she said her tips would optimize resumes to be read by both humans and AI.

This paper delves into the transformative power of Generative AI-driven storytelling in the realm of marketing. Generative AI, distinct from traditional machine learning, offers the capability to craft narratives that resonate with consumers on a deeply personal level. Through real-world examples from industry leaders like Google, Netflix and Stitch Fix, we elucidate how this technology shapes marketing strategies, personalizes consumer experiences, and navigates the challenges it presents. The paper also explores future directions and recommendations for generative AI-driven storytelling, including prospective applications such as real-time personalized storytelling, immersive storytelling experiences, and social media storytelling. By shedding light on the potential and impact of generative AI-driven storytelling in marketing, this paper contributes to the understanding of this cutting-edge approach and its transformative power in the field of marketing.

With the growing popularity of various mobile devices, user targeting has received a growing amount of attention, which aims at effectively and efficiently locating target users that are interested in specific services. Most pioneering works for user targeting tasks commonly perform similarity-based expansion with a few active users as seeds, suffering from the following major issues: the unavailability of seed users for newcoming services and the unfriendliness of black-box procedures towards marketers. In this paper, we design an Entity Graph Learning (EGL) system to provide explainable user targeting ability meanwhile applicable to addressing the cold-start issue. EGL System follows the hybrid online-offline architecture to satisfy the requirements of scalability and timeliness. Specifically, in the offline stage, the system focuses on the heavyweight entity graph construction and user entity preference learning, in which we propose a Three-stage Relation Mining Procedure (TRMP), breaking loose from the expensive seed users. At the online stage, the system offers the ability of user targeting in real-time based on the entity graph from the offline stage. Since the user targeting process is based on graph reasoning, the whole process is transparent and operation-friendly to marketers. Finally, extensive offline experiments and online A/B testing demonstrate the superior performance of the proposed EGL System.