When we hear about Artificial Intelligence (AI), the very first thought comes into our mind as it's being our personal home, office, or driving assistant. Because of the existing media representation on AI and the current progress of AI in the technology market, it is undoubtedly obvious to have such expectations from it. The involvement of AI in the technology space has been driven to a certain extent since the proliferation, especially in data analytics, and in which market data analytics to be precise. Many market researchers and data analysts believe that AI is an essential factor driving better performance efficiency and customer satisfaction, which eventually helps companies get better sales and revenues. According to one market survey, around 93% of market researchers consider AI as an industry opportunity, and 80% agree on AI driving a positive impact on the market.
Companies developing artificial intelligence (AI)-powered marketing tools typically claim that their solutions drive strategic decision-making better than software without an algorithmic component. But -- as is often the case -- the reality is more complicated. AI learns to make predictions from large amounts of high-quality data, and so can be hamstrung (e.g., make mistakes) if that data is not available. The complex nature of marketing stacks, which sprawl across disparate, disconnected systems, can put up logistical roadblocks to implementation. Brew, a Tel Aviv, Israel-based strategic marketing platform, claims its approach is different from the rest in that it's more holistic.
Many businesses that functioned well offline had no idea about the digital world, but fitting in was the only option they had. Global eCommerce took a leap of 26.7 Trillion dollar rise according to UN trade and development experts. And hence, this led to making the digital market a necessity for global suppliers and consumers. As our planet is slowly gaining the momentum of going back to the'normal', every business is now prepared for online and offline marketing. Strategizing your business is the key to blossoming.
This special issue interrogates the meaning and impacts of "tech ethics": the embedding of ethics into digital technology research, development, use, and governance. In response to concerns about the social harms associated with digital technologies, many individuals and institutions have articulated the need for a greater emphasis on ethics in digital technology. Yet as more groups embrace the concept of ethics, critical discourses have emerged questioning whose ethics are being centered, whether "ethics" is the appropriate frame for improving technology, and what it means to develop "ethical" technology in practice. This interdisciplinary issue takes up these questions, interrogating the relationships among ethics, technology, and society in action. This special issue engages with the normative and contested notions of ethics itself, how ethics has been integrated with technology across domains, and potential paths forward to support more just and egalitarian technology. Rather than starting from philosophical theories, the authors in this issue orient their articles around the real-world discourses and impacts of tech ethics--i.e., tech ethics in action.
Click-Through Rate (CTR) prediction, is an essential component of online advertising. The mainstream techniques mostly focus on feature interaction or user interest modeling, which rely on users' directly interacted items. The performance of these methods are usally impeded by inactive behaviours and system's exposure, incurring that the features extracted do not contain enough information to represent all potential interests. For this sake, we propose Neighbor-Interaction based CTR prediction, which put this task into a Heterogeneous Information Network (HIN) setting, then involves local neighborhood of the target user-item pair in the HIN to predict their linkage. In order to enhance the representation of the local neighbourhood, we consider four types of topological interaction among the nodes, and propose a novel Graph-masked Transformer architecture to effectively incorporates both feature and topological information. We conduct comprehensive experiments on two real world datasets and the experimental results show that our proposed method outperforms state-of-the-art CTR models significantly.
Digital transformation has become a critical need for businesses due to reasons' like improving customers loyalty, driving sales, and gaining meaningful insights. All of these aspects motivate companies to embrace digital transformation strategies and invest in modern technologies. In the current world, the importance of digital transformation can't be understated. It's time to take it from a concept and make it real in your company by investing in new technologies or processes that will better serve customers' needs, which are what will help attract them even more so than before because you're providing an experience they need instead just giving away something for free as many companies do nowadays! Are you wondering why I should embrace digital transformation?
'Business is an art and business leaders are artists', a well said a statement that is proving to be true every time a top leader takes amazing decisions for his organization. Although businesses rise and fall as times change, leaders never fail to be at the forefront to give their best. However, the key to long-term sustained success is great leadership and the ability of an executive to embrace the evolving trends. While talking about trends, the first thing that comes to our mind is artificial intelligence and disruptive technologies that are driving the next generation towards major digitization. The idea of technology came to practical usage when men thought that they needed machines to replace human activities. The core of such machines is to mimic or outperform human cognition. Although the concept of artificial intelligence came into existence in the 1950s, it didn't get fruition till the 1990s when technology hit the mainstream applications. Since then, the rise of technology has been enabled by exponentially faster and more powerful computers and large, complex datasets. Today, we have many futuristic technologies like machine learning, autonomous systems, data analytics, data science, and AR/VR in play. On the other hand, the enormous inflow of data has also contributed to this growth. In the digital world, development is highly reliant on technological advancement. Organizations across diverse industries are processing data to find insights and data-driven answers. Apart from laymen and consumers, it is the business leaders and corporate executives who have joined the bandwagon of the population to use artificial intelligence to the fullest. These trailblazing leaders are now increasingly using technology to optimize performance and experiment with new explorations. Their success story is what the world needs to hear. Analytics Insight has listed the top 100 such interviews that describe the journey of tech leaders and companies. Engineering and mining companies have faced a growing range of pressures in recent years, including price volatility, the need to drill down deeper to find new resources, and an industry-wide skills shortage. To address these challenges, many mining companies have embraced digital technology to enhance engineering design and develop smart mines'. Ausenco is a tech-savvy engineering company that delivers innovative, value-add consulting services, project delivery, asset operations, and maintenance solutions to the mining and metals, oil and gas, and industrial sectors….
In October 2017, Facebook altered the Instagram API to make it harder for users to search its giant database of photos. The change was a small element of the company's response to the Cambridge Analytica scandal, but it was a significant problem for parts of the Digital marketing industry. Not long before, New York-based influencer marketing agency Amra & Elma had developed a platform that ingested data from Instagram, and allowed its client to use AI image classifiers to find very specific influencers. For instance, they could find an influencer with, say, between 10,000 and 50,000 followers who had posted photos of themselves in a Jeep. Facebook's move killed this capability in a keystroke. Another day in the digital duel between the AIs deployed by digital marketers, and those deployed by the social media platforms.
Millennials and generation Z are contributing to some of the biggest changes in marketing and business. Companies are forced to create major shifts in marketing because these two demographic age groups are the biggest consumers and contributors to the economy. Two cousins found the perfect way to profit off of this shift as well as add insight to marketing in the newest landscape. Zohaib Patoli and Bilal Patoli have dominated a number of industries for almost seven years. At twenty-two and twenty, respectively, the duo has more experience with startups and business success than most seasoned business leaders.
Since 2019, most ad exchanges and sell-side platforms (SSPs), in the online advertising industry, shifted from second to first price auctions. Due to the fundamental difference between these auctions, demand-side platforms (DSPs) have had to update their bidding strategies to avoid bidding unnecessarily high and hence overpaying. Bid shading was proposed to adjust the bid price intended for second-price auctions, in order to balance cost and winning probability in a first-price auction setup. In this study, we introduce a novel deep distribution network for optimal bidding in both open (non-censored) and closed (censored) online first-price auctions. Offline and online A/B testing results show that our algorithm outperforms previous state-of-art algorithms in terms of both surplus and effective cost per action (eCPX) metrics. Furthermore, the algorithm is optimized in run-time and has been deployed into VerizonMedia DSP as production algorithm, serving hundreds of billions of bid requests per day. Online A/B test shows that advertiser's ROI are improved by +2.4%, +2.4%, and +8.6% for impression based (CPM), click based (CPC), and conversion based (CPA) campaigns respectively.