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Lean AI

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.

With this practical book, author Lomit Patel shows you how to use AI and machine learning (ML) to provide an operational layer atop those acquisition solutions to deliver meaningful results for your company. You’ll learn how to adapt, customize, and personalize cross-channel user journeys to help your company attract and maintain customers.

  • Learn how AI and ML 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 ML 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. I. AI + Growth Marketing = Smart Marketing
  2. Introduction
    1. The Attention Economy
  3. 1. 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
      1. AI + Growth Marketing = Smart Marketing
  4. II. Customer Acquisition 3.0
  5. 2. What is customer acquisition 3.0?
    1. New Dimensions for Scale & Learning
    2. AI and Customer Acquisition
    3. It’s time to turn on the intelligent machines.
  6. 3. Manual vs. Automation
    1. Intelligent Machine Thinking in the World of Digital Marketing
      1. Table Stakes: Customer Lifecycle Management
    2. IMVU’s Strategy for Automating on the Growth Team
    3. Building a business case for automation
      1. Applying Best Practices for Autonomous Marketing
  7. 4. 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
      4. SVM (Support Vector Machine)
    3. Major Type of Unsupervised Learning Algorithms
      1. k-Means
    4. Learning Algorithms that can be Supervised or Unsupervised
      1. Decision Tree
      2. Naive 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 & Optimization
    9. Applying Machine Learning and AI to the Customer Journey for IMVU
      1. Autonomous Marketing
      2. Iterative Testing
      3. Artificial Intelligence
      4. Rapidfire Experimentation
      5. Findings
    10. Bringing it All Together
  8. 5. Build vs. Buy
    1. Build vs. Buy Analysis
      1. 1. The Problem
      2. 2. The Budget
      3. 3. The Timeline
      4. Risks of building an AI solution
      5. Risks of buying an AI solution
      6. Machine learning as a service (MLaaS)
    2. Build or Buy… or Both?
    3. Weighing it All Out
  9. III. What Metrics Matter to you?
  10. 6. Key Metrics for Startup Growth
    1. 1. Customer Acquisition Cost (CAC)
    2. 2. Retention Rate
    3. 3. Customer Lifetime Value (CLV)
    4. 4. Return on Advertising Spending (ROAS)
    5. 5. Conversion Rate (CR)
    6. Beware of vanity metrics
  11. 7. Creative performance
    1. Creative Campaign Inputs
    2. Creative Scheduling
      1. Using Creative Teams
      2. Ad fatigue
      3. Benefits of great creative
      4. Creative best practices
        1. Mobile Ads Best Practice
        2. Future Creative Development & Iteration
  12. 8. Cross-channel attribution
    1. Marketing Attribution Models
      1. First or Last-Touch Attribution Models
      2. Multi-Touch Attribution Models
    2. Choosing the Right Attribution Model for Your Startup
    3. Marketing Attribution Tools
    4. Benefits of Marketing Attribution
    5. People-based attribution is the future
  13. IV. Picking the Right Approach to User Acquisition
  14. 9. Different UA strategies
    1. Ways to think about user acquisition strategy
    2. Stages of a user funnel
    3. Five key user acquisition strategies
  15. 10. The Growth Stack
    1. How Does It Work?
    2. Analytics & Insight
    3. Acquisition
    4. Engagement & Retention
    5. Monetization
    6. Activities That Cut Across The Stack
    7. Applying the stack in an AI world