Cover image for Lean AI

Lean AI

Author Lomit Patel
Release Date: 2020/02/01
ISBN: 9781492059301
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Book Description

How can startups successfully scale customer acquisition and revenue growth with a Lean team? Out-of-the-box acquisition solutions from Facebook, Google, and others provide a good start, but the companies that can tailor those solutions to meet their specific needs, objectives, and goals will come out winners. But that hasn’t been an easy task—until now.

With this practical book, author Lomit Patel shows you how to use AI and automation to provide an operational layer atop those acquisition solutions to deliver amazing results for your company. You’ll learn how to adapt, customize, and personalize cross-channel user journeys to help your company attract and retain customers—to usher in the new age of Autonomous Marketing.

  • Learn how AI and automation can support the customer acquisition efforts of a Lean Startup
  • Dive into Customer Acquisition 3.0, an initiative for gaining and retaining customers
  • Explore ways to use AI for marketing purposes
  • Understand the key metrics for determining the growth of your startup
  • Determine the right strategy to foster user acquisition in your company
  • Manage the increased complexity and risk inherent in AI projects

Table of Contents

  1. Foreword
  2. Preface
    1. Who This Book Is For
    2. How This Book Is Organized
    3. Acknowledgments
  3. I. AI + Growth Marketing = Smart Marketing
  4. 1. Introduction to Growth Marketing
    1. The Attention Economy
  5. 2. Why Lean AI?
    1. What Is Artificial Intelligence?
    2. What Is Machine Learning?
    3. What Is the Lean Startup?
    4. Three Key Drivers of Artificial Intelligence
      1. Computing Power
      2. Availability of Data
      3. Algorithms
    5. Industry Trends for AI Marketing
    6. AI + Growth Marketing = Smart Marketing
      1. Assessing the Maturity of Autonomous Marketing (with Help from the Self-Driving Car Folks)
  6. II. Customer Acquisition 3.0
  7. 3. What Is Customer Acquisition 3.0?
    1. New Dimensions for Scale and Learning
    2. AI and Customer Acquisition
    3. It’s Time to Turn on the Intelligent Machines
  8. 4. Manual Versus Automation
    1. Intelligent Machine Thinking in the World of Digital Marketing
      1. Automated Media Buying
      2. Cross-Channel Marketing Orchestration
      3. Virtual Marketing Assistants
      4. Content Curation
      5. Customer Support and Service
      6. Segmentation Development and Management
      7. Insight Generation
      8. Creative Generation
    2. Table Stakes: Customer Life Cycle Management
      1. Awareness
      2. Engagement
      3. Evaluation
      4. Purchase
      5. Post-Purchase
      6. Advocacy
    3. IMVU’s Strategy for Automating on the Growth Team
    4. Building a Business Case for Automation
  9. 5. Framework of an “Intelligent Machine”
    1. Breaking Down Machine Learning for Marketing Purposes
    2. Major Types of Supervised Learning Algorithms
      1. Linear Regression
      2. Logistic Regression
      3. k-Nearest Neighbor
      4. Support Vector Machines
    3. Major Type of Unsupervised Learning Algorithms
      1. k-Means
    4. Learning Algorithms That Can Be Supervised or Unsupervised
      1. Decision Tree
      2. Naïve Bayes
      3. Random Forest
    5. The Importance of Data
    6. Audience Selection
      1. First-Party/CRM Data
      2. Custom Audiences
    7. Message Placement
    8. Exploration and Optimization
    9. Applying Machine Learning and AI to the Customer Journey for IMVU
      1. Autonomous Marketing
      2. Iterative Testing
      3. Artificial Intelligence
      4. Rapid-Fire Experimentation
      5. Findings
    10. Bringing It All Together
  10. 6. Build Versus Buy
    1. Build Versus Buy Analysis
      1. The Problem
      2. The Budget
      3. The Timeline
    2. Risks of Building an AI Solution
    3. Risks of Buying an AI Solution
    4. Machine Learning as a Service
    5. Build or Buy…or Both?
    6. Weighing It All Out
  11. III. What Metrics Matter to You?
  12. 7. Key Metrics for Startup Growth
    1. Customer Acquisition Cost
    2. Retention Rate
    3. Customer Lifetime Value
    4. Return on Advertising Spending
    5. Conversion Rate
    6. Beware of Vanity Metrics
  13. 8. Creative Performance
    1. The Importance of Creative Assets
      1. Creative Campaign Inputs
      2. Creative Scheduling
    2. Using Creative Teams
    3. Ad Fatigue
    4. Benefits of Great Creative
    5. Creative Best Practices
    6. Mobile Ads Best Practices
    7. Future Creative Development and Iteration
  14. 9. Cross-Channel Attribution
    1. What Is Marketing Attribution?
    2. Marketing Attribution Models
      1. First- and Last-Touch Attribution Models
      2. Multi-Touch Attribution Models
    3. Choosing the Right Attribution Model for Your Startup
    4. Marketing Attribution Tools
    5. Benefits of Marketing Attribution
    6. People-Based Attribution Is the Future
      1. The Why of People-Based Attribution
      2. The Current State of People-Based Attribution
      3. Attribution Basics: Recognizing the User Behind Individual Touchpoints
      4. Two Approaches to People-Based Attribution
      5. Common and Advanced Use Cases for People-Based Attribution
  15. IV. Selecting the Right Approach to User Acquisition
  16. 10. Different User Acquisition Strategies
    1. Ways to Think About User Acquisition Strategy
    2. Stages of a User Funnel
    3. Five Key User Acquisition Strategies
  17. 11. The Growth Stack
    1. How Does It Work?
    2. Analytics and Insights
      1. Attribution
      2. Event Tracking
      3. Campaign Measurement
      4. App Store Analytics and Intelligence
      5. User Segmentation
      6. Cohort Analysis
      7. Content Analytics
      8. Sentiment Tracking
      9. User Testing
      10. A/B Testing Measurement
      11. Screen Flows
      12. Conversion Funnels
      13. Billing and Revenue Reporting
      14. Growth Modeling
      15. LTV Modeling
      16. Growth Accounting
    3. App Performance Analysis (CPU, Battery, Network, Crashes)
    4. Acquisition
      1. PR
      2. App Store Optimization
      3. Content Marketing
      4. Performance Marketing
      5. Influencer Marketing
      6. Distribution Deals
      7. Virality Loops
      8. Cross-Sell
      9. Content Indexing
    5. Engagement and Retention
      1. Activation
      2. User Accounts
      3. Life-Cycle Marketing
      4. Activity Notifications
      5. Community (Engagement and Support)
    6. Monetization
      1. Revenue Model Development
      2. Payment Processing
      3. Pricing
      4. Ad Inventory Management
    7. Activities That Cut Across the Stack
      1. Internationalization
      2. Retargeting
      3. Partnerships and Integrations
      4. Conversion Optimization
      5. Channels
      6. Push
      7. In-App Messaging
      8. Email
      9. SMS
      10. Search
      11. Social
      12. Ad Networks
      13. TV, Print, and Radio
      14. Owned
    8. Messenger Platforms
      1. Chatbots
      2. Mobile DSPs and SSPs
      3. App Streaming
    9. Applying the Stack in an AI World
  18. V. Managing Increased Complexity and Risk
  19. 12. How to Manage Complexity
    1. Identifying Use Cases
    2. Expected Value
    3. The Operational State
    4. Focus on Outcomes
    5. Customer Data
    6. Choose the Right Metrics
  20. 13. How to Reduce Risk
    1. Data Dependency
    2. Transparency
    3. Biased Algorithms
    4. Compliance
    5. Clear Goals
    6. Adaptability of Machine Learning Models
  21. 14. Human Versus Machine
    1. Skill Set for the Future Growth Team
      1. Hybrid Growth Team
    2. Adopt a Growth Mindset
    3. AI Will Create More Job Opportunities
  22. VI. The Next Frontier
  23. 15. Planning for Success
    1. Success Goals and Measurements
    2. AI and Humans Working Together
    3. Data Is at the Core of Everything
      1. Customer Data Platform
      2. Data System
      3. Decision System: Real-Time Customer Analytics, Segmentation, and Personalization
      4. Delivery System: Make User Data Shareable and Accessible to Other Systems
    4. Data Privacy and Integrity
      1. CDPs Are the Lifeblood of AI
  24. 16. Ongoing Challenges
    1. Data Acquisition
    2. Privacy Controls
    3. Team Downsizing
    4. New Channels and Opportunities
    5. Staying on Top of Fraud
    6. Facing Challenges
  25. 17. How to Win Together with AI
    1. Final Thoughts
  26. Index
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