Christiano Ronaldo is one of the most liked and followed humans on Instagram -- but why would he be so suitable for a purse campaign in India? Tel Aviv, Isrel-based Ai-powered platform Humanz thinks he would be perfect for the job. It ran its algorithm on the footballer and identified some key components that would make him the perfect person for a marketing campaign there: Over one in three (37%) of his audience of authentic users are in India. Four of the top five interest categories of his audience are: Lifestyle. And a product that cuts across all of these categories is'handbags, or purses'.
Digital marketing is a term that has emerged to describe the usage of the Internet and digital media to support marketing. By using digital technologies, marketing teams achieve marketing objectives defined in the organization. As more consumers, customers and prospects are present and active on different online platforms, digital marketing needs to encompass different kinds of media channels. Paid media, earned media and owned media needs to be considered in the competitive and complex buying environment of today's market. Paid media are channels where investment is put forward to pay for visitors through ads or search ranking.
We are looking for a Technical Data Analyst and Program Manager to build out our extended data collection and performance analysis activities. Your job will be to gather and analyze large amounts of raw information from both internal and external sources such as Salesforce, AWS, StackOverflow, Couchbase, GitHub, Google Analytics or custom APIs. You will establish routine reporting and analysis derived from that data, evaluating the trends of our KPI's such that we remain informed as we evolve our objectives. We will rely on you to extract valuable business insights from this work as well as lead cross-functional projects and discussions as program manager for teams that are influenced by this information. In this role, you should be highly analytical with a background in analysis, math and statistics.
How can we assess the value of data objectively, systematically and quantitatively? Pricing data, or information goods in general, has been studied and practiced in dispersed areas and principles, such as economics, marketing, electronic commerce, data management, data mining and machine learning. In this article, we present a unified, interdisciplinary and comprehensive overview of this important direction. We examine various motivations behind data pricing, understand the economics of data pricing and review the development and evolution of pricing models according to a series of fundamental principles. We discuss both digital products and data products. We also consider a series of challenges and directions for future work.
This versatile composition of research derivatives pertaining to diverse concurrent developments in the global Big Data and Machine Learning in Telecom market is poised to induce forward-looking perspectives favoring unfaltering growth stance. The new research report assessing market developments in the global Big Data and Machine Learning in Telecom market is a 360 degree reference guide, highlighting core information on holistic competitive landscape, besides rendering high voltage information on market size and dimensions with references of value- and volume based market details, indispensable for infallible decision making in global Big Data and Machine Learning in Telecom market. Understanding Big Data and Machine Learning in Telecom market Segments: an Overview: The report is aimed at improving the decision-making capabilities of readers with due emphasis on growth planning, resource use that boost growth trajectory. Additional insights on government initiatives, regulatory framework, growth policies and resource utilization have all been highlighted for healthy growth journey. Besides understanding the revenue generation potential of each of the segments, the report also takes note of the multifarious vendor initiatives towards segment betterment that play a crucial role in growth enablement.
Data threats never rest, nor should the protection of your sensitive information. That's the driving principle behind confidential computing, which seeks to plug a potentially crippling hole in data security. Confidential computing provides a secure platform for multiple parties to combine, analyze and learn from sensitive data without exposing their data or machine learning algorithms to the other party. This technique goes by several names -- multiparty computing, federated learning and privacy-preserving analytics, among them -- and confidential computing can enable this type of collaboration while preserving privacy and regulatory compliance. Data exists in three states: in transit when it is moving through the network; at rest when stored; and in use as it's being processed.
Personalization and recommendations are now accepted as core competencies in just about every online setting, ranging from media platforms to e-commerce to social networks. While the challenge of estimating user preferences has garnered significant attention, the operational problem of using such preferences to construct personalized offer sets to users is still a challenge, particularly in modern settings where a massive number of items and a millisecond response time requirement mean that even enumerating all of the items is impossible. Faced with such settings, existing techniques are either (a) entirely heuristic with no principled justification, or (b) theoretically sound, but simply too slow to work. Thus motivated, we propose an algorithm for personalized offer set optimization that runs in time sub-linear in the number of items while enjoying a uniform performance guarantee. Our algorithm works for an extremely general class of problems and models of user choice that includes the mixed multinomial logit model as a special case. We achieve a sub-linear runtime by leveraging the dimensionality reduction from learning an accurate latent factor model, along with existing sub-linear time approximate near neighbor algorithms. Our algorithm can be entirely data-driven, relying on samples of the user, where a `sample' refers to the user interaction data typically collected by firms. We evaluate our approach on a massive content discovery dataset from Outbrain that includes millions of advertisements. Results show that our implementation indeed runs fast and with increased performance relative to existing fast heuristics.
We consider a variant of the novel contextual bandit problem with corrupted context, which we call the contextual bandit problem with corrupted context and action correlation, where actions exhibit a relationship structure that can be exploited to guide the exploration of viable next decisions. Our setting is primarily motivated by adaptive mobile health interventions and related applications, where users might transitions through different stages requiring more targeted action selection approaches. In such settings, keeping user engagement is paramount for the success of interventions and therefore it is vital to provide relevant recommendations in a timely manner. The context provided by users might not always be informative at every decision point and standard contextual approaches to action selection will incur high regret. We propose a meta-algorithm using a referee that dynamically combines the policies of a contextual bandit and multi-armed bandit, similar to previous work, as wells as a simple correlation mechanism that captures action to action transition probabilities allowing for more efficient exploration of time-correlated actions. We evaluate empirically the performance of said algorithm on a simulation where the sequence of best actions is determined by a hidden state that evolves in a Markovian manner. We show that the proposed meta-algorithm improves upon regret in situations where the performance of both policies varies such that one is strictly superior to the other for a given time period. To demonstrate that our setting has relevant practical applicability, we evaluate our method on several real world data sets, clearly showing better empirical performance compared to a set of simple algorithms.
Developing efficient sequential bidding strategies for repeated auctions is an important practical challenge in various marketing tasks. In this setting, the bidding agent obtains information, on both the value of the item at sale and the behavior of the other bidders, only when she wins the auction. Standard bandit theory does not apply to this problem due to the presence of action-dependent censoring. In this work, we consider second-price auctions and propose novel, efficient UCB-like algorithms for this task. These algorithms are analyzed in the stochastic setting, assuming regularity of the distribution of the opponents' bids. We provide regret upper bounds that quantify the improvement over the baseline algorithm proposed in the literature. The improvement is particularly significant in cases when the value of the auctioned item is low, yielding a spectacular reduction in the order of the worst-case regret. We further provide the first parametric lower bound for this problem that applies to generic UCB-like strategies. As an alternative, we propose more explainable strategies which are reminiscent of the Explore Then Commit bandit algorithm. We provide a critical analysis of this class of strategies, showing both important advantages and limitations. In particular, we provide a minimax lower bound and propose a nearly minimax-optimal instance of this class.
People in all kinds of industries are debating artificial intelligence (AI) pros and cons. AI pros and cons are going to shape future generations – and, indeed, the whole future of our species. Today, however, science fiction is still just a story. When you're thinking about AI and what it can do, it's important to recognize its limitations. But it's also vital to remember just how quickly it has moved forward. Seeing both sides of the coin lets you really understand where AI is excellent and where it still has serious shortcomings. One thing is for sure: You should be using AI.