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Lean AI

How Innovative Startups Use Artificial Intelligence to Grow

Paperback Engels 2020 9781492059318
Verkooppositie 2522
Verwachte levertijd ongeveer 8 werkdagen


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


Aantal pagina's:240
Hoofdrubriek:IT-management / ICT


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Over Lomit Patel

Lomit Patel is the Vice President of Growth at IMVU, responsible for user acquisition, retention and monetization. Prior to IMVU, Lomit managed growth at early stage startups including Roku (IPO), TrustedID (acquired by Equifax), Texture (acquired. by Apple) and EarthLink. Lomit is a public speaker, author, advisor, and recognized as a Mobile Hero by Liftoff.

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Who This Book Is For
How This Book Is Organized

Part I: AI + Growth Marketing = Smart Marketing
1. Introduction to Growth Marketing
The Attention Economy

2. Why Lean AI?
What Is Artificial Intelligence?
What Is Machine Learning?
What Is the Lean Startup?
Three Key Drivers of Artificial Intelligence
Computing Power
Availability of Data
Industry Trends for AI Marketing
AI + Growth Marketing = Smart Marketing
Assessing the Maturity of Autonomous Marketing (with Help from the Self-Driving Car Folks)

Part II: Customer Acquisition 3.0
3. What Is Customer Acquisition 3.0?
New Dimensions for Scale and Learning
AI and Customer Acquisition
It’s Time to Turn on the Intelligent Machines

4. Manual Versus Automation
Intelligent Machine Thinking in the World of Digital Marketing
Automated Media Buying
Cross-Channel Marketing Orchestration
Virtual Marketing Assistants
Content Curation
Customer Support and Service
Segmentation Development and Management
Insight Generation
Creative Generation
Table Stakes: Customer Life Cycle Management
IMVU’s Strategy for Automating on the Growth Team
Building a Business Case for Automation

5. Framework of an “Intelligent Machine”
Breaking Down Machine Learning for Marketing Purposes
Major Types of Supervised Learning Algorithms
Linear Regression
Logistic Regression
k-Nearest Neighbor
Support Vector Machines
Major Type of Unsupervised Learning Algorithms
Learning Algorithms That Can Be Supervised or Unsupervised
Decision Tree
Naïve Bayes
Random Forest
The Importance of Data
Audience Selection
First-Party/CRM Data
Custom Audiences
Message Placement
Exploration and Optimization
Applying Machine Learning and AI to the Customer Journey for IMVU
Autonomous Marketing
Iterative Testing
Artificial Intelligence
Rapid-Fire Experimentation
Bringing It All Together

6. Build Versus Buy
Build Versus Buy Analysis
The Problem
The Budget
The Timeline
Risks of Building an AI Solution
Risks of Buying an AI Solution
Machine Learning as a Service
Build or Buy…or Both?
Weighing It All Out

Part III: What Metrics Matter to You?
7. Key Metrics for Startup Growth
Customer Acquisition Cost
Retention Rate
Customer Lifetime Value
Return on Advertising Spending
Conversion Rate
Beware of Vanity Metrics

8. Creative Performance
The Importance of Creative Assets
Creative Campaign Inputs
Creative Scheduling
Using Creative Teams
Ad Fatigue
Benefits of Great Creative
Creative Best Practices
Mobile Ads Best Practices
Future Creative Development and Iteration

9. Cross-Channel Attribution
What Is Marketing Attribution?
Marketing Attribution Models
First- and Last-Touch Attribution Models
Multi-Touch Attribution Models
Choosing the Right Attribution Model for Your Startup
Marketing Attribution Tools
Benefits of Marketing Attribution
People-Based Attribution Is the Future
The Why of People-Based Attribution
The Current State of People-Based Attribution
Attribution Basics: Recognizing the User Behind Individual Touchpoints
Two Approaches to People-Based Attribution
Common and Advanced Use Cases for People-Based Attribution

Part IV: Selecting the Right Approach to User Acquisition
10. Different User Acquisition Strategies
Ways to Think About User Acquisition Strategy
Stages of a User Funnel
Five Key User Acquisition Strategies

11. The Growth Stack
How Does It Work?
Analytics and Insights
Event Tracking
Campaign Measurement
App Store Analytics and Intelligence
User Segmentation
Cohort Analysis
Content Analytics
Sentiment Tracking
User Testing
A/B Testing Measurement
Screen Flows
Conversion Funnels
Billing and Revenue Reporting
Growth Modeling
LTV Modeling
Growth Accounting
App Performance Analysis (CPU, Battery, Network, Crashes)
App Store Optimization
Content Marketing
Performance Marketing
Influencer Marketing
Distribution Deals
Virality Loops
Content Indexing
Engagement and Retention
User Accounts
Life-Cycle Marketing
Activity Notifications
Community (Engagement and Support)
Revenue Model Development
Payment Processing
Ad Inventory Management
Activities That Cut Across the Stack
Partnerships and Integrations
Conversion Optimization
In-App Messaging
Ad Networks
TV, Print, and Radio
Messenger Platforms
Mobile DSPs and SSPs
App Streaming
Applying the Stack in an AI World

Part V: Managing Increased Complexity and Risk
12. How to Manage Complexity
Identifying Use Cases
Expected Value
The Operational State
Focus on Outcomes
Customer Data
Choose the Right Metrics

13. How to Reduce Risk
Data Dependency
Biased Algorithms
Clear Goals
Adaptability of Machine Learning Models

14. Human Versus Machine
Skill Set for the Future Growth Team
Hybrid Growth Team
Adopt a Growth Mindset
AI Will Create More Job Opportunities

Part VI: The Next Frontier
15. Planning for Success
Success Goals and Measurements
AI and Humans Working Together
Data Is at the Core of Everything
Customer Data Platform
Data System
Decision System: Real-Time Customer Analytics, Segmentation, and Personalization
Delivery System: Make User Data Shareable and Accessible to Other Systems
Data Privacy and Integrity
CDPs Are the Lifeblood of AI

16. Ongoing Challenges
Data Acquisition
Privacy Controls
Team Downsizing
New Channels and Opportunities
Staying on Top of Fraud
Facing Challenges

17. How to Win Together with AI
Final Thoughts


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