Pubbl/distr/stampa	[Place of publication not identified], : IEEE, 2009
Descrizione fisica	1 online resource : illustrations
Disciplina	658.5
Soggetto topico	New products - Marketing Product management Computer software
ISBN	0-7695-4098-8
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Record Nr.	UNISA-996216503803316

Autore	Rosen William <1963->
Pubbl/distr/stampa	Lanham, Maryland : , : Rowman & Littlefield, , 2017
Descrizione fisica	1 online resource (223 pages) : color illustrations
Disciplina	658.827
Soggetto topico	Branding (Marketing) Product management
ISBN	1-4422-5705-9
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	Aim for greater roi: the activation imperative method -- Ready, set, aim -- Broadcast, billboards, branding, and more -- Start with the shopper (marketing) -- Connecting with digital, social, and mobile -- Come and get it: content and in-bound marketing -- The importance of being direct (database and CRM, too) -- Join us: the world of participation and promotion marketing -- Are you experiential? -- Now a word from your sponsor(ships) -- Build your relationship(s) with PR and brand journalism -- Aim for optimal activation.
Record Nr.	UNINA-9910798953203321

Autore	Nika Marily
Edizione	[1st ed.]
Pubbl/distr/stampa	Newark : , : John Wiley & Sons, Incorporated, , 2025
Descrizione fisica	1 online resource (339 pages)
Disciplina	658.50028563
Altri autori (Persone)	GranadosDiego
Soggetto topico	Product management
ISBN	1-394-35246-8 1-394-33566-0
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	Cover -- Title Page -- Copyright Page -- About the Authors -- Acknowledgments -- Contents at a Glance -- Contents -- Introduction -- Part I Foundational AI/ML Concepts -- Chapter 1 Artificial Intelligence and Machine Learning: What Every Product Manager Needs to Know -- AI vs. ML -- Why This Matters to a PM -- Key Differences Between AI and ML -- Common Misconceptions for PMs: Myths vs. Reality -- Your Glossary as a PM -- Grounding the Concepts: Real-World AI in Action -- The AI PM's Guiding Principles -- Chapter Summary and Key Takeaways -- Key Takeaways -- Onward: Peeking Under the Hood -- Chapter 2 How Machine Learning Models Learn: A Peek Under the Hood -- The Learning Process: Training, Validation, and Testing -- How Models Learn: An Example with k-Nearest Neighbors (k-NN) -- Applying k-NN (with k=1): -- Another Example: Testing an Unknown Fruit -- Evaluating Model Performance -- The Confusion Matrix: A Foundation for Understanding -- Key Classification Metrics (and Their PM Implications) -- The Precision-Recall Trade-Off -- Choosing the Right Metric -- Overfitting and Underfitting: Striking the Right Balance for Real-World Performance -- Overfitting: Memorizing Instead of Learning -- Underfitting: Missing the Forest for the Trees -- Visual Analogy: Fitting a Curve -- Finding the Sweet Spot: Generalization -- The PM's Role -- Human-in-the-Loop: Blending AI Power with Human Expertise -- What Is Human-in-the-Loop? -- Why HITL Is Essential for Product Managers (and Their Products) -- How to Implement HITL (PM Considerations) -- Chapter Summary and Key Takeaways -- Key Takeaways -- Onward: Understanding the Broader Process -- Chapter 3 The Big Picture: AI, ML, and You -- Understanding the Relationship Between AI, ML, and Product Goals -- Types of Machine Learning: Understanding the Spectrum of Learning. Supervised Learning: Guiding the Model with Labeled Examples -- Unsupervised Learning: Discovering Hidden Patterns in Your Data -- Reinforcement Learning: Learning Through Trial and Error -- Generative AI: Powering a New Era of Language-Based Applications -- The "Gotchas": A PM's Guide to LLM Limitations and Risks -- Types of Machine Learning: A Recap -- Introduction to Neural Networks and Deep Learning: The Engines of Complex Pattern Recognition -- Neural Networks: Mimicking the Brain's Connections (But Not Really) -- How Neural Networks Learn: Adjusting the Connections -- Technical Deep Dive: The Mechanics of Neural Networks and Deep Learning -- Challenges in Deep Learning -- Chapter Summary and Key Takeaways -- Key Takeaways -- Onward: Mapping the Process -- Chapter 4 The AI Lifecycle -- Problem Definition and Business Understanding: The "Why" -- Data Collection and Exploration: Understanding Your Ingredients -- Data Preprocessing: Preparing the Ingredients -- Feature Engineering: Crafting the Inputs for Success -- Model Selection and Training: Choosing the Right Algorithm -- Model Evaluation and Tuning: Ensuring Quality -- Model Deployment and Monitoring: Bringing AI to Life (and Keeping It Healthy) -- Retraining and Maintenance: Keeping Your Model Up-to-Date -- Chapter Summary and Key Takeaways -- Key Takeaways -- Onward: Exploring the AI PM Roles -- Part II AI PM Specializations -- Chapter 5 AI-Experiences PM: Shaping User Interaction with AI -- Key Responsibilities: Shaping the AI User Experience -- Day-to-Day Activities -- Required Skills and Knowledge: The AI-Experiences PM Toolkit -- Core Product Management Craft and Practices -- Engineering Foundations for PMs -- Essential Leadership and Collaboration Skills -- AI Lifecycle and Operational Awareness -- Illustrative Example: A Day in the Life of an AI-Experiences PM. Challenges and Complexities -- How the AI-Experiences PM Interacts with Other Roles -- Chapter Summary and Key Takeaways -- Key Takeaways -- Onward: Architecting the AI Foundation -- Chapter 6 AI-Builder PM: Architecting the Foundation of Intelligent Systems -- Key Responsibilities: Building and Managing the AI Foundation -- Day-to-Day Activities -- Required Skills and Knowledge: The AI-Builder PM's Technical and Strategic Toolkit -- Core Product Management Craft and Practices -- Engineering Foundations for PMs -- Essential Leadership and Collaboration Skills -- AI Lifecycle and Operational Awareness -- Illustrative Example: A Day in the Life of an AI-Builder PM -- Challenges and Complexities -- How the AI-Builder PM Interacts with Other Roles -- Chapter Summary and Key Takeaways -- Key Takeaways -- Onward: Supercharging the PM Workflow -- Chapter 7 AI-Enhanced PM: Supercharging Product Management with AI -- Key Responsibilities: Augmenting PM Workflows and Decision-Making with AI -- Day-to-Day Activities -- Required Skills and Knowledge: The AI-Enhanced PM's Toolkit -- Core Product Management Craft and Practices -- Engineering Foundations for PMs -- Essential Leadership and Collaboration Skills -- AI Lifecycle and Operational Awareness -- Illustrative Example: A Day in the Life of an AI-Enhanced PM -- Examples of AI Tools -- Challenges and Complexities -- How the AI-Enhanced PM Interacts with Other Roles -- Skill Comparison: AI-Experiences PM, AI-Builder PM, and AI-Enhanced PM -- Chapter Summary and Key Takeaways -- Key Takeaways -- Onward: From Theory to Action -- Part III Connecting the Dots Between AI/ML Knowledge and PM Craft -- Chapter 8 Identifying and Evaluating AI Opportunities -- Uncovering Potential Use Cases-Mining Your Product for AI Gold -- Recognizing Data-Rich Problem Areas -- Analyzing Existing Data Sources. Asking the Right Questions -- AI/ML Capability Matching: Connecting Problems to Solutions -- Understanding Your AI/ML Toolkit: Key Capabilities -- Matching Capabilities to Problems: A Practical Approach -- Finding AI Opportunities in the User Journey -- Mapping the User Journey: Charting the Course -- Identifying Pain Points and Opportunities: The AI Detective Work -- Applying AI/ML to Enhance Touchpoints: The Transformation -- Feature Enhancement Through AI/ML-Transforming Existing Functionality -- Identifying Enhancement Opportunities: Finding the Weak Spots -- Applying AI/ML to Enhance Features: The Transformation Process -- Proactive Product Management-Anticipating User Needs with AI -- Understanding the Power of Prediction and Automation -- Key Areas for Predictive and Automation Opportunities -- Identifying Opportunities: A Practical Approach -- Responsible AI Foundations-Ethical and Feasibility Considerations -- Ethical Considerations: The "Do No Harm" Principle -- Feasibility Considerations: Can We Actually Build This? -- Practical Ideation Techniques for AI/ML Use Cases-Thinking Like an AI-First Product Manager -- Ideation Techniques: Unleashing Your AI Creativity -- Cultivating an AI-First Mindset -- Chapter Summary and Key Takeaways -- Key Takeaways -- Onward: Measuring the Value of Your Ideas -- Chapter 9 ROI Calculation for AI Projects: Measuring the Impact and Demonstrating Value -- From Model Performance to Business Impact: A PM's Guide to AI Metrics -- Defining AI/ML-Specific Metrics: The Foundation for Measuring ROI -- The Importance of Baselines: Knowing Where You Started -- Understanding the Confusion Matrix: Decoding Classification Performance -- Key Performance Metrics for AI/ML Models: Beyond the Confusion Matrix -- Context Matters: Selecting the Right Metrics for Your AI/ML Application -- Important Considerations. End-to-End Example-Predicting Churn in a Subscription Service -- 1. Identify the Business Goal: Defining the "Why" -- 2. Define the AI/ML Application and Solution -- 3. Identify Data Sources and Engineer Features: The Raw Materials -- 4. Select the Metrics: Defining Success -- 5. Establish Baseline Metrics: Setting the Starting Point -- 6. Conduct Model Training and Evaluation: Building and Testing the AI -- 7. Conduct A/B Testing: Measuring Real-World Impact -- 8. Calculate the Results and ROI: Quantifying the Value -- 9. Monitor and Maintain the Model for Long-Term Success -- A/B Testing for AI and ML Projects: Validating Impact and Optimizing Performance -- What Is A/B Testing (in a Nutshell)? -- Why Is A/B Testing Especially Important for AI/ML? -- How to Conduct A/B Testing for AI and ML: A Step-by-Step Guide -- Key Considerations for AI/ML A/B Testing -- Chapter Summary and Key Takeaways -- Key Takeaways -- Onward: From the Lab to a Live Product -- Chapter 10 Building and Deploying AI Solutions: From Lab to Live -- MLOps: The Key to Reliable and Scalable AI -- Key Components of MLOps-The AI Production Line -- CI/CD, IaC, and Collaboration: The Foundational Pillars of MLOps -- Glossary of Key MLOps Terms -- MLOps End-to-End Example: Churn Prediction in a Subscription Service (Product Manager's Perspective) -- Chapter Summary and Key Takeaways -- Key Takeaways -- Onward: Building with Integrity -- Chapter 11 Responsible AI and Ethical Considerations: Building AI with Integrity -- Understanding AI Bias and Fairness: The Foundation of Responsible AI -- Identifying Potential Biases: Where Bias Can Creep In -- Mitigating Potential Biases: A Proactive Approach -- Protected Classes and AI Fairness-Designing for Inclusion -- What are Protected Classes? -- Why Focus on Protected Classes? (The Legal and Ethical Imperative). How Protected Classes Relate to AI Bias: The Mechanisms of Discrimination.
Record Nr.	UNINA-9911031643503321

Autore	Ryans Adrian B. <1945->
Pubbl/distr/stampa	Chichester, England ; ; Hoboken, NJ, : John Wiley & Sons, c2008
Descrizione fisica	1 online resource (274 p.)
Disciplina	658.4012 658.827
Soggetto topico	Product management Price cutting Brand choice Brand name products Marketing
Soggetto genere / forma	Electronic books.
ISBN	0-470-68761-4 1-119-20650-2 1-282-34949-X 9786612349492 0-470-74522-3
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	Beating Low Cost Competition; Contents; Preface; Acknowledgments; CHAPTER 1: The growing challenge from low cost competitors; CHAPTER 2: Why the threat from low cost competition is intensifying; CHAPTER 3: Understanding how low cost competitors play the game; CHAPTER 4: Realistically assessing the threat; CHAPTER 5: Confronting low cost competitors in the price value segment of the market; CHAPTER 6: Avoiding head-to-head competition with low cost competitors by playing a different game; CHAPTER 7: The leadership challenge; CHAPTER 8: An even more challenging future; References; Index
Record Nr.	UNINA-9910145259103321