▣ Chapter 1: What is Growth Hacking?
1.1 Growth Hacking, Let's Try It Too
1.2 Is there a shortcut to growth hacking?
1.3 Understanding Growth Hacking

▣ Chapter 2: Prerequisite: Product-Market Fit
2.1 The problem is not knowing what the problem is.
2.2 A Mistake Many Product Managers Make
____1) Make the product first, then find customers.
____2) Add, add, add features
2.3 Product-Market Fit
____1) Is the problem we think it is really?
____2) Did the product we created really solve that problem?
____3) What was the hypothesis you established while creating this product? Has that hypothesis been verified?
2.4 How to check product-market fit?
____1) Retention rate
____2) Conversion rate
____3) Net Promoter Score (NPS)

▣ Chapter 3: AARRR
3.1 AARRR Overview
____1) Perspectives on looking at indicators: Task-based vs.
Framework-based
____2) Efficient indicator management: AARRR
____3) Misunderstanding about AARRR
____4) How to use AARRR
3.2 Customer Acquisition
____1) Basic concepts of customer acquisition
____2) Customer Acquisition Cost (CAC)
____3) UTM parameter
____4) Mobile App Attribution
____5) Customer Acquisition Summary
3.3 Activation
____1) Basic concept of activation
____2) Define the detailed steps of the funnel
____3) Measuring conversion rates
____4) Breaking down conversion rates by cohort
____5) How to increase the conversion rate of the funnel
____6) Activation Summary
3.4 Retention
____1) Basic concept of retention
____2) Three ways to measure retention
____3) Analyzing retention
____4) Improving retention
____5) Retention Summary
3.5 Monetization
____1) Basic concept of revenue
____2) Key monetization metrics
____3) Analyze monetization
____4) Revenue Summary
3.6 Referral
____1) Basic concept of referral
____2) Friend invitation flow design
____3) Viral Coefficient
____4) Referral Summary

▣ Chapter 4: Indicators
4.1 Using Indicators
____1) Understanding the properties of indicators
____2) Clearly define the indicators
____3) Don't fall into the trap of vanity
____4) Optimization from a holistic perspective
____5) Simpson's Paradox
____6) Things to be careful of when using representative values
____7) Avoiding survivorship bias
4.2 OMTM (One Metric That Matters)
____1) What is the most important indicator right now?
____2) OMTM & KPI & OKR

▣ Chapter 5: From Growth Hacking Beginnings to Growth Experiments
5.1 Getting Started with Growth Hacking
____1) Can growth hacking be done in a small company?
____2) Creating a work environment that can utilize data
5.2 Creating a Data Pipeline
____1) Data pipeline for behavioral log analysis
____2) Events and properties
____3) Design and load the action log
5.3 Developing the capacity and culture to leverage data
5.4 Growth Experiments: A/B Testing
____1) What is A/B testing?
____2) Designing an A/B test
____3) Things to consider when designing an A/B test
____4) How to analyze A/B test results
____5) Precautions when conducting A/B testing

▣ Chapter 6: Gross Organization and Work Processes
6.1 Creating a Gross Organization
____1) There is no such thing as a growth hacker
____2) The goal of the gross organization
____3) Gross organization members
____4) Gross organizational structure
6.2 How Gross Organizations Work
6.3 Culture for Growth
____1) Gross is top-down
____2) Harmony between product development (Production) and growth (Growth)
____3) Increase data accessibility
____4) Creating a culture of collaboration
____5) When should I start growth hacking?

▣ Epilogue: For those who want to study growth hacking
Handling data
Statistics & Experimental Methodology
How to use various services and tools
Knowledge of user & service domains
cooperation
Conclusion

★ What this book covers ★

◎ The concept and scope of growth hacking
◎ Definition and analysis method of key indicators based on AARRR
◎ Establishing an environment for data collection and analysis
◎ Various methodologies for growth hacking, such as A/B testing
◎ Organizational culture for growth and team formation

★ Target audience for this book ★

◎ Planners, marketers, and analysts who are looking to start studying data analysis
◎ People interested in startups or aspiring entrepreneurs
◎ Practitioners who want to know the process of collecting, analyzing, and applying data to work