Fundamentals 8 min read

Mastering 5W2H: A Practical Guide to Analyzing Game User Churn

This article introduces the 5W2H analysis framework, demonstrates its application to a game’s declining retention problem, and walks through funnel analysis, user‑group profiling, and A/B testing to identify causes and design data‑driven solutions for player churn.

Python Crawling & Data Mining
Python Crawling & Data Mining
Python Crawling & Data Mining
Mastering 5W2H: A Practical Guide to Analyzing Game User Churn

01 Scenario Introduction

A newly launched game (R2) has retention rates consistently below industry standards, and the R2 retention metric continues to decline. The operations team asked the analyst to investigate the reasons for the drop using the 5W2H analysis method.

02 Introduction to 5W2H

The 5W2H (also called the Seven‑What analysis) consists of five questions starting with “W” (What, When, Where, Who, Why) and two questions starting with “H” (How, How Much). These questions guide systematic inquiry into a problem.

5W Content

1. What – What happened?

2. When – When did it happen?

3. Where – Where did it happen?

4. Who – Who was involved?

5. Why – Why did it happen?

2H Content

1. How – How to do it?

2. How Much – To what extent, quantity, cost?

03 Detailed 5W2H Example with Game Churn

Using 5W2H to analyze new‑game user churn can be broken down into six steps:

(1) What phase – Identify the current problem: analyze R2 retention and pinpoint influencing factors.

(2) When and Where phase – Use funnel analysis to locate the churn level and steps.

(3) Who phase – Conduct user‑group analysis to discover attributes, characteristics, and acquisition channels of churned users.

(4) Why phase – Infer possible reasons for churn based on the previous analyses.

(5) How and How Much phase – Form hypotheses, guide adjustments with data, player feedback, and operational experience, schedule implementation, and validate changes via A/B testing.

3.1 Funnel Analysis to Identify Churn Levels

Funnel analysis shows that 80% of churn occurs within the first six levels, especially during the tutorial and the first three AI matches. Further breakdown reveals a 15% loss at step 9 of the tutorial, indicating a potential issue at that point.

3.2 User‑Group Analysis to Determine Churn Attributes

User‑group analysis helps define which player types are most likely to churn. By examining source channels, social attributes, and other traits, we found that 80% of churned users are newcomers and that users with limited device memory also have higher churn rates.

3.3 Attribution, Solution Design, and Implementation

Combining funnel and user‑group insights with player feedback (e.g., tutorial difficulty, perceived lag) allows us to hypothesize causes. We then propose adjustments, prioritize them, and verify effectiveness through A/B testing.

04 Summary

The 5W2H method provides a structured framework for investigating problems, drilling down to root causes, and designing data‑driven solutions. When combined with techniques such as funnel analysis, user‑group profiling, and A/B testing, it enables thorough, actionable insights for improving game retention.

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AB testingData-driven5W2HGame AnalyticsFunnel Analysisuser churn
Python Crawling & Data Mining
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