Ad serving method and apparatus for asynchronous advertising in time and space shifted media network

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Trieu Nguyen

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Ad serving method and apparatus for asynchronous advertising in time and space shifted media network

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Part 1: Why should every business need to deploy a CDP ? 1. Big data is the reality of business today 2. What are technologies to manage customer data ? 3. The rise of first-party data and new technologies for Digital Marketing 4. How to apply USPA mindset to build your CDP for data-driven business Part 2: How to use LEO CDP for your business 1. Core functions of LEO CDP for marketers and IT managers 2. Data Unification for Customer 360 Analytics 3. Data Segmentation 4. Customer Personalization 5. Customer Data Activation Part 3: Case study in O2O Retail and Ecommerce 1. How to build customer journey map for ecommerce and retail 2. How to do customer analytics to find ideal customer profiles The ideal customer profile in a B2B context The ideal customer profile in a B2C context 3. Manage product catalog for customer personalization 4. Monitoring Data of Customer Experience (CX Analytics) CX Data Flow CX Rating plugin is embedded in the website, to collect feedback data An overview of CX Report A CX Report in a customer profile 5. Monitoring data with real-time event tracking reports Event Data Flow Summary Event Data Report Event Data Report in a Customer Profile Part 4: How to setup an instance of LEO CDP for free 1. Technical architecture 2. Server infrastructure 3. Setup middlewares: Nginx, ArangoDB, Redis, Java and Python Network requirements Software requirements for new server ArangoDB Nginx Proxy SSL for Nginx Server Java 8 JVM Redis Install Notes for Linux Server Clone binary code for new server Set DNS hosts for LEO CDP workers 4. Setup data for testing and system verification Part 5: Summary all key ideas

[Notes] Customer 360 Analytics with LEO CDP

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Why should you invest in LEO CDP ? Purpose: Big data and AI democracy for SMEs companies Problem: Customer Analytics and Customer Personalization Solutions: CDP + CX + Personalization Engine Product demo: LEO CDP for Ecommerce and Fintech Business model: Freemium → Ecosystem → Subscription Market size: 20 billion USD in 2026 and CAGR 34.6% Differentiation: cloud-native software Go-to-market approach: Community → Free → Paid Team: 1 full-stack dev, 1 data scientist and 12,000 fans of BigDataVietnam.org Community Need 150,000 USD for scaling business (you get 20% share)

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1) Hiểu bài toán số hoá trải nghiệm khách hàng 2) Nghiên cứu giải pháp LEO CDP 3) Lộ trình triển khai Phát triển / số hoá điểm chạm khách hàng Xây dựng bản đồ hành trình khách hàng Định nghĩa các metrics và KPI quan trọng Xây dựng web portal và mobile data hub Xây dựng kế hoạch Digital Marketing Triển khai CDP và Marketing Automation Xây dựng đội Analytics để phân tích dữ liệu

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Outline of this course 1. Digital Media Models in the age of marketing 4.0 2. Strategic Thought as It Relates to Digital Marketing 3. Web: The Center of Digital Marketing Delivery Mix 4. Content Management System (CMS) and headless CMS 5. Search Engine Marketing 6. Email Marketing 7. Social Media and Mobile Marketing 8. Introduction to Advertising Technology (Ad Tech) 9. Introduction to Customer Database and Customer Data Platform (CDP) 10. Legal Issues: Data privacy, Security, and Intellectual Property 11. Case study: IKEA - from business strategy to digital marketing strategy 12. Recommended books for self-study

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Transform your marketing and sales capabilities with Big Data and A.I 1) Why is Customer Data Platform (CDP) ? Case study: Enhancing the revenue of your restaurant with CDP and mobile app marketing Question: Why can CDP disrupt business model for restaurant industry (B2C) ? 2) How would CDP work in practice ? Introducing USPA.tech as logical framework for implementing CDP in practice How Can a Customer Data Platform Enhance Your Account-Based Marketing Strategy (B2B) ? 3) How can we implement CDP for business? Introducing the CDP as customer-first marketing platform for all industries (my key idea in this slide)

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1) Introduction to the key Big Data concepts 1.1 The Origins of Big Data 1.2 What is Big Data ? 1.3 Why is Big Data So Important ? 1.4 How Is Big Data Used In Practice ? 2) Introduction to the key principles of Big Data Systems 2.1 How to design Data Pipeline in 6 steps 2.2 Using Lambda Architecture for big data processing 3) Practical case study : Chat bot with Video Recommendation Engine 4) FAQ for student

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1. Các ứng dụng Big Data thực tiễn trên thế giới 2. Các lĩnh vực đang ứng dụng Big Data ở Việt Nam 3. Các bài toán Big Data tiêu biểu ở Vietnam a. Quản lý chăm sóc khách hàng (CRM) b. Tối ưu hoá trải nghiệm truyền hình Internet c. Quảng cáo trực tuyến AdsPlay.net 4. Giới thiệu về công việc và thị trường việc làm Big Data ở Việt Nam 5. Kiến thức nền tảng cho các bạn sinh viên

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[Notes] Customer 360 Analytics with LEO CDP

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What are drone anti-jamming systems? The drone anti-jamming systems and anti-spoof technology protect against interference, jamming, and spoofing of the UAVs. To protect their security, countries are beginning to research drone anti-jamming systems, also known as drone strike weapons. The anti-jam and anti-spoof technology protects against interference, jamming and spoofing. A drone strike weapon is a drone attack weapon that can attack and destroy enemy drones. So what is so unique about this amazing system?

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The presentation explores the development and application of artificial intelligence (AI) from its inception to its current status in the modern world. The term "artificial intelligence" was first coined by John McCarthy in 1956 to describe efforts to develop computer programs capable of performing tasks that typically require human intelligence. This concept was first introduced at a conference held at Dartmouth College, where programs demonstrated capabilities such as playing chess, proving theorems, and interpreting texts. In the early stages, Alan Turing contributed to the field by defining intelligence as the ability of a being to respond to certain questions intelligently, proposing what is now known as the Turing Test to evaluate the presence of intelligent behavior in machines. As the decades progressed, AI evolved significantly. The 1980s focused on machine learning, teaching computers to learn from data, leading to the development of models that could improve their performance based on their experiences. The 1990s and 2000s saw further advances in algorithms and computational power, which allowed for more sophisticated data analysis techniques, including data mining. By the 2010s, the proliferation of big data and the refinement of deep learning techniques enabled AI to become mainstream. Notable milestones included the success of Google's AlphaGo and advancements in autonomous vehicles by companies like Tesla and Waymo. A major theme of the presentation is the application of generative AI, which has been used for tasks such as natural language text generation, translation, and question answering. Generative AI uses large datasets to train models that can then produce new, coherent pieces of text or other media. The presentation also discusses the ethical implications and the need for regulation in AI, highlighting issues such as privacy, bias, and the potential for misuse. These concerns have prompted calls for comprehensive regulations to ensure the safe and equitable use of AI technologies. Artificial intelligence has also played a significant role in healthcare, particularly highlighted during the COVID-19 pandemic, where it was used in drug discovery, vaccine development, and analyzing the spread of the virus. The capabilities of AI in healthcare are vast, ranging from medical diagnostics to personalized medicine, demonstrating the technology's potential to revolutionize fields beyond just technical or consumer applications. In conclusion, AI continues to be a rapidly evolving field with significant implications for various aspects of society. The development from theoretical concepts to real-world applications illustrates both the potential benefits and the challenges that come with integrating advanced technologies into everyday life. The ongoing discussion about AI ethics and regulation underscores the importance of managing these technologies responsibly to maximize their their benefits while minimizing potential harms.

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Enterprise Knowledge’s Urmi Majumder, Principal Data Architecture Consultant, and Fernando Aguilar Islas, Senior Data Science Consultant, presented "Driving Behavioral Change for Information Management through Data-Driven Green Strategy" on March 27, 2024 at Enterprise Data World (EDW) in Orlando, Florida. In this presentation, Urmi and Fernando discussed a case study describing how the information management division in a large supply chain organization drove user behavior change through awareness of the carbon footprint of their duplicated and near-duplicated content, identified via advanced data analytics. Check out their presentation to gain valuable perspectives on utilizing data-driven strategies to influence positive behavioral shifts and support sustainability initiatives within your organization. In this session, participants gained answers to the following questions: - What is a Green Information Management (IM) Strategy, and why should you have one? - How can Artificial Intelligence (AI) and Machine Learning (ML) support your Green IM Strategy through content deduplication? - How can an organization use insights into their data to influence employee behavior for IM? - How can you reap additional benefits from content reduction that go beyond Green IM?

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