The growth in the use of online advertising to foster brand awareness over recent years is largely attributable to the ubiquity of social media. One pivotal technology contributing to the success of online brand advertising is frequency capping, a mechanism that enables marketers to control the number of times an ad is shown to a specific user. However, the very foundation of this technology is being scrutinized as the industry gravitates towards advertising solutions that prioritize user privacy. This paper delves into the issue of reach measurement and optimization within the context of $k$-anonymity, a privacy-preserving model gaining traction across major online advertising platforms. We outline how to report reach within this new privacy landscape and demonstrate how probabilistic discounting, a probabilistic adaptation of traditional frequency capping, can be employed to optimize campaign performance. Experiments are performed to assess the trade-off between user privacy and the efficacy of online brand advertising. Notably, we discern a significant dip in performance as long as privacy is introduced, yet this comes with a limited additional cost for advertising platforms to offer their users more privacy.

In online advertising, once an ad campaign is deployed, the automated bidding system dynamically adjusts the bidding strategy to optimize Cost Per Action (CPA) based on the number of ad conversions. For ads with a long conversion delay, relying solely on the real-time tracked conversion number as a signal for bidding strategy can significantly overestimate the current CPA, leading to conservative bidding strategies. Therefore, it is crucial to predict the number of long-delayed conversions. Nonetheless, it is challenging to predict ad conversion numbers through traditional regression methods due to the wide range of ad conversion numbers. Previous regression works have addressed this challenge by transforming regression problems into bucket classification problems, achieving success in various scenarios. However, specific challenges arise when predicting the number of ad conversions: 1) The integer nature of ad conversion numbers exacerbates the discontinuity issue in one-hot hard labels; 2) The long-tail distribution of ad conversion numbers complicates tail data prediction. In this paper, we propose the Long-Delayed Ad Conversions Prediction model for bidding strategy (LDACP), which consists of two sub-modules. To alleviate the issue of discontinuity in one-hot hard labels, the Bucket Classification Module with label Smoothing method (BCMS) converts one-hot hard labels into non-normalized soft labels, then fits these soft labels by minimizing classification loss and regression loss. To address the challenge of predicting tail data, the Value Regression Module with Proxy labels (VRMP) uses the prediction bias of aggregated pCTCVR as proxy labels. Finally, a Mixture of Experts (MoE) structure integrates the predictions from BCMS and VRMP to obtain the final predicted ad conversion number.

As soon as Apple announced its plans to inject generative AI into the iPhone, it was as good as official: The technology is now all but unavoidable. AI has already colonized web search, appearing in Google and Bing. OpenAI, the 80 billion start-up that has partnered with Apple and Microsoft, feels ubiquitous; the auto-generated products of its ChatGPTs and DALL-Es are everywhere. Rarely has a technology risen--or been forced--into prominence amid such controversy and consumer anxiety. Certainly, some Americans are excited about AI, though a majority said in a recent survey, for instance, that they are concerned AI will increase unemployment; in another, three out of four said they believe it will be abused to interfere with the upcoming presidential election.

Levi Strauss announced plans to test customized AI-generated models with the goal of multiplying the number and diversity of models that customers can see. The denim giant partnered on the test with LaLaLand.ai, Currently, shoppers on Levi.com typically only see one model for each product. The AI technology, according to Levi's, "can potentially assist us by supplementing models and unlocking a future where we can enable customers to see our products on more models that look like themselves, creating a more personal and inclusive shopping experience." The project supports Levi's broader diversity, equity and inclusion objectives.

In cost-per-click (CPC) or cost-per-impression (CPM) advertising campaigns, advertisers always run the risk of spending the budget without getting enough conversions. Moreover, the bidding on advertising inventory has few connections with propensity one that can reach to target cost-per-acquisition (tCPA) goals. To address this problem, this paper presents a bid optimization scenario to achieve the desired tCPA goals for advertisers. In particular, we build the optimization engine to make a decision by solving the rigorously formalized constrained optimization problem, which leverages the bid landscape model learned from rich historical auction data using non-parametric learning. The proposed model can naturally recommend the bid that meets the advertisers' expectations by making inference over advertisers' historical auction behaviors, which essentially deals with the data challenges commonly faced by bid landscape modeling: incomplete logs in auctions, and uncertainty due to the variation and fluctuations in advertising bidding behaviors. The bid optimization model outperforms the baseline methods on real-world campaigns, and has been applied into a wide range of scenarios for performance improvement and revenue liftup.

Artificial intelligence (AI) is a process of imbibing learning, reasoning, and self-correction in computer systems to make machines act autonomously in a humanely intelligent way. Industries are evolving and catching up to the technology trends. Alike, advertising is to rewriting the traditional means. Generative Adversarial Networks models of AI have enabled image synthesis & generation. We can generate realistic and high-resolution images which look like genuine photographs.

Agencies today are leveraging artificial intelligence technologies in many different ways and to varying degrees of success. A big question that has been on the minds of industry leaders in recent years is, "What is the best way for our organization to invest in AI?" From automating processes to serving as a conduit for better customer care and communication, agencies are embracing AI capabilities they hope will streamline operations and improve results for clients. Below, members of Forbes Agency Council draw upon their industry insights and personal experiences to explore how agencies can incorporate AI and what kinds of results they can expect. Forbes Agency Council members share ways to leverage artificial intelligence to improve an agency's workflow. At our company, we're using AI in a few areas, including for website SEO, where it has propelled a more than 50% increase in page views per visitor, year over year.

Artificial Intelligence is having positive impacts on business marketing nowadays. In fact, there're great AI-powered marketing tools you can use. Using the right marketing tools is essential nowadays. This is because you will be able to reach the right audience and also get better returns on your marketing efforts. As a business owner, your main goal is to make sales and this is where marketing can help.

Artificial intelligence has come a long way, especially in terms of operations. According to Salesforce, around 60% of market leaders suggest that AI can be helpful for various program campaigns. With digital transformation taking over the world, running programmatic campaigns becomes easy. According to Accenture, the communications and information industry, AI capabilities will help to generate around $4.7 trillion by 2035 for coalescence. Artificial intelligence has been bringing significant changes across the advertising industry, especially in advertising across the sales industry.

Wendy Gonzalez is the CEO of Sama, the provider of accurate data for ambitious AI. Once associated with big New York City offices, patriarchal workplace culture and multi-million dollar budgets, the advertising industry has evolved considerably in the past century. Now diversified and modernized, remnants of the mid-century Madison Avenue advertising ecosystem are few and far between. But what's caused this shift? Industry leaders will be quick to tell you there's at least one tool that's been especially vital to this evolution: artificial intelligence (AI).