{"product_id":"growth-engineering-how-to-build-systems-that-drive-product-success-in-an-ai-driven-world-paperback-softback-9781394378463","title":"Growth Engineering; How to Build Systems That Drive Product Success in an AI-Driven World (Paperback \/ softback) 9781394378463","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eGrowth Engineering\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eHow to Build Systems That Drive Product Success in an AI-Driven World\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eRita Okonkwo (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394378463, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback \/ softback, published 6 April 2026\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e208 pages\u003cbr\u003e23.1 x 18.5 x 1.5 cm, 0.295 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eBuild software that users actually use with proven growth-oriented software development strategies\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eIn \u003ci\u003eGrowth Engineering: How to Build Systems That Drive Product Success in an AI-Driven World,\u003c\/i\u003e experienced software engineer with the Microsoft Experiences + Devices Growth team, Rita Okonkwo, delivers a strategic guide for anyone interested in building tech products that scale organically through smart technical choices. \u003c\/p\u003e\n\u003cp\u003eYou'll learn how clean architecture, thoughtful instrumentation, and experimentation frameworks directly influence growth outcomes. With a focus on practical systems and real-world decision-making, this book shows how to build software that gains traction, drives engagement, and supports continuous iteration. \u003c\/p\u003e\n\u003cp\u003eYou'll learn all about key growth engineering strategies like feature flighting, data-driven experimentation, logging, and metrics tracking. You'll find real-world case studies that break down design systems that support rapid iteration, and data-based product decision-making. \u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eInside the book:\u003c\/b\u003e \u003c\/p\u003e\n\u003cul\u003e \u003cli\u003eWhy growth engineering matters and how engineers can get directly involved in it\u003c\/li\u003e \u003cli\u003eExperimentation strategies, including controlled rollouts and effective A\/B testing techniques\u003c\/li\u003e \u003cli\u003eHow to build scalable data pipelines and integrate real-time analytics\u003c\/li\u003e \u003cli\u003eWays to create a growth-first engineering culture, generating faster iterations without sacrificing quality\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003ePerfect for software engineers, product managers, and developers interested in building products that users love, \u003ci\u003eGrowth Engineering: How to Build Systems That Drive Product Success in an AI-Driven World\u003c\/i\u003e is a must-read for entrepreneurs, founders, and other technology business leaders ready to discover how to consistently create commercially successful software.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003eForeword xvii\u003c\/p\u003e \u003cp\u003eIntroduction xxi\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Growth Engineering 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Role of Engineers in Product Growth 2\u003c\/p\u003e \u003cp\u003eKey Growth Strategies 3\u003c\/p\u003e \u003cp\u003eHabit Formation 3\u003c\/p\u003e \u003cp\u003eFreemium Model 4\u003c\/p\u003e \u003cp\u003eExperimentation 4\u003c\/p\u003e \u003cp\u003eData-Driven Growth 5\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Observability 7\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eInstrumentation 9\u003c\/p\u003e \u003cp\u003eHow to Know What to Instrument 10\u003c\/p\u003e \u003cp\u003eLegal and Compliance Checklist 11\u003c\/p\u003e \u003cp\u003eA Practical Example of Instrumentation 13\u003c\/p\u003e \u003cp\u003eTelemetry 14\u003c\/p\u003e \u003cp\u003eLogs 16\u003c\/p\u003e \u003cp\u003eMetrics 17\u003c\/p\u003e \u003cp\u003eTraces 19\u003c\/p\u003e \u003cp\u003eImplementing Observability in Practice 20\u003c\/p\u003e \u003cp\u003eDefining the Signals 21\u003c\/p\u003e \u003cp\u003eUnderstanding the Flow 21\u003c\/p\u003e \u003cp\u003eUsing Observability to Act 22\u003c\/p\u003e \u003cp\u003eMaking It a Habit 22\u003c\/p\u003e \u003cp\u003eObservability Anti-Patterns 22\u003c\/p\u003e \u003cp\u003eTracking Everything Without Purpose 22\u003c\/p\u003e \u003cp\u003eLogging Without Context 23\u003c\/p\u003e \u003cp\u003eRelying Only on Logs 23\u003c\/p\u003e \u003cp\u003eInstrumenting Too Late 23\u003c\/p\u003e \u003cp\u003eNo Clear Ownership 24\u003c\/p\u003e \u003cp\u003eTools for Observability 24\u003c\/p\u003e \u003cp\u003eWhat This Chapter Covered 27\u003c\/p\u003e \u003cp\u003eKey Questions for Reflection 27\u003c\/p\u003e \u003cp\u003eExercise 27\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Data Pipelines 29\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat Is a Data Pipeline and Why Does It Matter? 29\u003c\/p\u003e \u003cp\u003eComponents of a Data Pipeline 31\u003c\/p\u003e \u003cp\u003eIngestion 31\u003c\/p\u003e \u003cp\u003eBatch Ingestion 31\u003c\/p\u003e \u003cp\u003eStreaming Ingestion 32\u003c\/p\u003e \u003cp\u003eTransportation 33\u003c\/p\u003e \u003cp\u003eMessage Brokers or Queues 34\u003c\/p\u003e \u003cp\u003eStreaming Platforms or Distributed Logs 34\u003c\/p\u003e \u003cp\u003eTelemetry Forwarders or Data Shippers 34\u003c\/p\u003e \u003cp\u003eProcessing 35\u003c\/p\u003e \u003cp\u003eKeep It Simple at First 36\u003c\/p\u003e \u003cp\u003eValidate Early 37\u003c\/p\u003e \u003cp\u003eMake It Observable 37\u003c\/p\u003e \u003cp\u003eUse Version Control for Logic 38\u003c\/p\u003e \u003cp\u003eStorage 39\u003c\/p\u003e \u003cp\u003eData Warehouses 39\u003c\/p\u003e \u003cp\u003eData Lakes 39\u003c\/p\u003e \u003cp\u003eWhen to Use What 40\u003c\/p\u003e \u003cp\u003eVisualization 40\u003c\/p\u003e \u003cp\u003eTools and Interfaces 41\u003c\/p\u003e \u003cp\u003eTypes of Visualizations and When to Use Them 42\u003c\/p\u003e \u003cp\u003eBuilding a Growth Pipeline with Large Language Models 46\u003c\/p\u003e \u003cp\u003eStep 1: Define the Role or Persona 47\u003c\/p\u003e \u003cp\u003eStep 2: Define What You Want to Measure 48\u003c\/p\u003e \u003cp\u003eStep 3: Instrumentation Strategy 48\u003c\/p\u003e \u003cp\u003eStep 4: Generate Mock Data 49\u003c\/p\u003e \u003cp\u003eStep 5: Process Data 50\u003c\/p\u003e \u003cp\u003eStep 6: Store Data 52\u003c\/p\u003e \u003cp\u003eStep 7: Visualize Data 53\u003c\/p\u003e \u003cp\u003eWhat This Chapter Covered 53\u003c\/p\u003e \u003cp\u003eKey Questions for Reflection 54\u003c\/p\u003e \u003cp\u003eExercise 54\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Data Modeling 55\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eOLTP vs. OLAP 57\u003c\/p\u003e \u003cp\u003eOltp 57\u003c\/p\u003e \u003cp\u003eOlap 57\u003c\/p\u003e \u003cp\u003eModeling for OLTP 58\u003c\/p\u003e \u003cp\u003eHow to Create an ER Diagram 58\u003c\/p\u003e \u003cp\u003eUnderstanding Cardinality 60\u003c\/p\u003e \u003cp\u003eOne-to-One (1:1) 60\u003c\/p\u003e \u003cp\u003eOne-to-Many (1:N) 61\u003c\/p\u003e \u003cp\u003eMany-to-Many (N:M) 61\u003c\/p\u003e \u003cp\u003eBuilding an ER Diagram for a Growth Use Case 63\u003c\/p\u003e \u003cp\u003eStep 1: Identify Your Entities 63\u003c\/p\u003e \u003cp\u003eStep 2: Define the Relationships 63\u003c\/p\u003e \u003cp\u003eStep 3: Add Attributes 64\u003c\/p\u003e \u003cp\u003eStep 4: Diagram It Out 65\u003c\/p\u003e \u003cp\u003eStep 5: Think Through Growth Questions 65\u003c\/p\u003e \u003cp\u003eStep 6: Avoid Modeling Pitfalls 66\u003c\/p\u003e \u003cp\u003eStep 7: Get Ready for the Next Layer 67\u003c\/p\u003e \u003cp\u003eNormalization 67\u003c\/p\u003e \u003cp\u003eWhat Is a Relation? 68\u003c\/p\u003e \u003cp\u003eKeys: Primary, Foreign, and Composite 69\u003c\/p\u003e \u003cp\u003eFunctional Dependencies 70\u003c\/p\u003e \u003cp\u003eNormalization 71\u003c\/p\u003e \u003cp\u003eModeling for OLAP 76\u003c\/p\u003e \u003cp\u003eFacts and Dimensions 76\u003c\/p\u003e \u003cp\u003eDenormalization 78\u003c\/p\u003e \u003cp\u003eStar and Snowflake Schemas 79\u003c\/p\u003e \u003cp\u003eStar Schema 79\u003c\/p\u003e \u003cp\u003eSnowflake Schema 79\u003c\/p\u003e \u003cp\u003eChoosing Between Them 80\u003c\/p\u003e \u003cp\u003eWhat This Chapter Covered 80\u003c\/p\u003e \u003cp\u003eKey Questions for Reflection 81\u003c\/p\u003e \u003cp\u003eExercise 81\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 What Are Experiments? 83\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Philosophy of Experimentation 84\u003c\/p\u003e \u003cp\u003eHumility in Product Development 85\u003c\/p\u003e \u003cp\u003eExperimentation as a Team Sport 85\u003c\/p\u003e \u003cp\u003eExperimentation Protects Users 86\u003c\/p\u003e \u003cp\u003eThe Anatomy of an Experiment 86\u003c\/p\u003e \u003cp\u003eHypothesis Formation 87\u003c\/p\u003e \u003cp\u003eControl and Treatment Groups 88\u003c\/p\u003e \u003cp\u003eRandomization 89\u003c\/p\u003e \u003cp\u003eMetrics and Scorecards 89\u003c\/p\u003e \u003cp\u003eDuration and Sample Size 91\u003c\/p\u003e \u003cp\u003eWhy Experiments Matter in Growth Engineering 91\u003c\/p\u003e \u003cp\u003eCommon Misconceptions About Experimentation 93\u003c\/p\u003e \u003cp\u003e“Experimentation Slows Us Down” 93\u003c\/p\u003e \u003cp\u003e“Experiments Are Only for Small UI Tweaks” 94\u003c\/p\u003e \u003cp\u003e“Only Data Scientists Should Run Experiments” 95\u003c\/p\u003e \u003cp\u003e“We Can Just Measure After Launch Instead” 95\u003c\/p\u003e \u003cp\u003eWhat This Chapter Covered 96\u003c\/p\u003e \u003cp\u003eKey Questions for Reflection 97\u003c\/p\u003e \u003cp\u003eExercises 97\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Types of Product Experiments 99\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDesign Types 100\u003c\/p\u003e \u003cp\u003eA\/A Test 100\u003c\/p\u003e \u003cp\u003eA\/B Test 101\u003c\/p\u003e \u003cp\u003eA\/B\/n Test 102\u003c\/p\u003e \u003cp\u003eMultivariate Test 103\u003c\/p\u003e \u003cp\u003eHoldout Groups 104\u003c\/p\u003e \u003cp\u003eSwitchback Test 105\u003c\/p\u003e \u003cp\u003eApplication Types 107\u003c\/p\u003e \u003cp\u003eUI\/UX Experiments 107\u003c\/p\u003e \u003cp\u003eOnboarding Experiments 108\u003c\/p\u003e \u003cp\u003eNotification Experiments 109\u003c\/p\u003e \u003cp\u003ePricing Experiments 109\u003c\/p\u003e \u003cp\u003eFake Door Experiments 110\u003c\/p\u003e \u003cp\u003eReverse Experiments 111\u003c\/p\u003e \u003cp\u003eWhat This Chapter Covered 112\u003c\/p\u003e \u003cp\u003eKey Questions for Reflection 113\u003c\/p\u003e \u003cp\u003eExercises 113\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Introduction to A\/B Testing 115\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat Makes a Fair Comparison 116\u003c\/p\u003e \u003cp\u003eTriggering 117\u003c\/p\u003e \u003cp\u003eTypes of Triggering 118\u003c\/p\u003e \u003cp\u003eExposure-Based Triggering 118\u003c\/p\u003e \u003cp\u003eAction-Based Triggering 118\u003c\/p\u003e \u003cp\u003eHybrid Triggering 119\u003c\/p\u003e \u003cp\u003eChoosing the Right Trigger 119\u003c\/p\u003e \u003cp\u003eExample: The Pro-Tip Onboarding Card 119\u003c\/p\u003e \u003cp\u003eRandomization 120\u003c\/p\u003e \u003cp\u003eSample Ratio Mismatch 122\u003c\/p\u003e \u003cp\u003eStatistical Significance 124\u003c\/p\u003e \u003cp\u003ePower and Sample Size 126\u003c\/p\u003e \u003cp\u003eCommon Mistakes in A\/B Testing 127\u003c\/p\u003e \u003cp\u003eStopping Too Soon 127\u003c\/p\u003e \u003cp\u003eRunning Overlapping Experiments 127\u003c\/p\u003e \u003cp\u003eIgnoring Guardrail Metrics 128\u003c\/p\u003e \u003cp\u003eFocusing on Significance over Impact 128\u003c\/p\u003e \u003cp\u003eSkipping A\/A Tests 128\u003c\/p\u003e \u003cp\u003eOverlooking Novelty and Learning Effects 128\u003c\/p\u003e \u003cp\u003eWhat This Chapter Covered 129\u003c\/p\u003e \u003cp\u003eKey Questions for Reflection 130\u003c\/p\u003e \u003cp\u003eExercises 130\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Building a Growth Engineering Team 133\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat Makes a Growth Engineering Team Unique 133\u003c\/p\u003e \u003cp\u003eTeam Composition and Roles 134\u003c\/p\u003e \u003cp\u003eGrowth Engineers 134\u003c\/p\u003e \u003cp\u003eProduct Managers 135\u003c\/p\u003e \u003cp\u003eData Scientists 135\u003c\/p\u003e \u003cp\u003eGrowth Designers 136\u003c\/p\u003e \u003cp\u003eUser Experience Researchers 136\u003c\/p\u003e \u003cp\u003eTeam Structure 137\u003c\/p\u003e \u003cp\u003eCentralized Model 137\u003c\/p\u003e \u003cp\u003eEmbedded Model 138\u003c\/p\u003e \u003cp\u003eHybrid Model 138\u003c\/p\u003e \u003cp\u003eCultural Foundations 139\u003c\/p\u003e \u003cp\u003eExperiment over Opinion 139\u003c\/p\u003e \u003cp\u003eShared Metrics and Transparency 140\u003c\/p\u003e \u003cp\u003eLearning Loops and Post-Mortems 140\u003c\/p\u003e \u003cp\u003eBuilding Trust for Growth 141\u003c\/p\u003e \u003cp\u003eHiring and Upskilling for Growth 141\u003c\/p\u003e \u003cp\u003eThe Growth Engineer’s Career Path 143\u003c\/p\u003e \u003cp\u003eThe Cadence of Growth Teams 144\u003c\/p\u003e \u003cp\u003eWeekly Growth Review 144\u003c\/p\u003e \u003cp\u003eHypothesis Review 144\u003c\/p\u003e \u003cp\u003eScorecard Syncs 145\u003c\/p\u003e \u003cp\u003eSharing Learnings 145\u003c\/p\u003e \u003cp\u003eWhat This Chapter Covered 145\u003c\/p\u003e \u003cp\u003eKey Questions for Reflection 146\u003c\/p\u003e \u003cp\u003eExercises 146\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 The Future of Growth Engineering 149\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAI and the Future of Experimentation 150\u003c\/p\u003e \u003cp\u003eDesigning Experiments 151\u003c\/p\u003e \u003cp\u003eAI-Assisted Development 151\u003c\/p\u003e \u003cp\u003eAutonomous Experiment Execution 152\u003c\/p\u003e \u003cp\u003eAI-Assisted Analysis and Insight Generation 153\u003c\/p\u003e \u003cp\u003eHow AI Changes the Role of the Growth Engineering Team 155\u003c\/p\u003e \u003cp\u003eGrowth Engineer 155\u003c\/p\u003e \u003cp\u003eProduct Manager 156\u003c\/p\u003e \u003cp\u003eData Scientist 158\u003c\/p\u003e \u003cp\u003eDesigners and UX Researchers 160\u003c\/p\u003e \u003cp\u003eEthics, Privacy, and Responsible Growth in an AI-Driven Era 161\u003c\/p\u003e \u003cp\u003eWhat This Chapter Covered 163\u003c\/p\u003e \u003cp\u003eKey Questions for Reflection 164\u003c\/p\u003e \u003cp\u003eExercises 164\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 The Growth Engineer’s Workflow 165\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eStandup 166\u003c\/p\u003e \u003cp\u003eProduct Alignment 167\u003c\/p\u003e \u003cp\u003eEngineering Design 168\u003c\/p\u003e \u003cp\u003eImplementation 169\u003c\/p\u003e \u003cp\u003eBug Bash 171\u003c\/p\u003e \u003cp\u003eRollout 172\u003c\/p\u003e \u003cp\u003eScorecard Review 173\u003c\/p\u003e \u003cp\u003eRetrospective 174\u003c\/p\u003e \u003cp\u003eCommunicating Impact 175\u003c\/p\u003e \u003cp\u003eWhat This Chapter Covered 177\u003c\/p\u003e \u003cp\u003eKey Questions for Reflection 177\u003c\/p\u003e \u003cp\u003eExercises 178\u003c\/p\u003e \u003cp\u003eIndex 179\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Computer science [\u003ca title=\"See our other books on Computer science\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20science%20%5BUY%5D%22\"\u003eUY\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand New","offer_id":52507376648472,"sku":"9781394378463","price":42.59,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394378463.jpg?v=1786445329","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/growth-engineering-how-to-build-systems-that-drive-product-success-in-an-ai-driven-world-paperback-softback-9781394378463","provider":"Freshly Printed Books","version":"1.0","type":"link"}