Issue: Pushing More Reasonable Charging Points for Players

Video link: Bilibili Video

Dataset

Dataset snapshot:

Player ID Registration Time Payment Time Payment Amount Charging Point ID
1 2019-11-15 2019-11-15 1.99 4
1 2019-11-15 2019-11-15 9.99 1
1 2019-11-15 2019-11-16 29.99 4
1 2019-11-15 2019-11-16 19.99 6
1 2019-11-15 2019-11-16 24.99 4

Charging points typically refer to specific locations or scenarios in games or applications where players can make payments. These points can include particular moments, events, or items within the game, such as:

  1. Purchasing virtual currency: Players can buy virtual currency within the game to purchase other items or unlock content.
  2. Buying items or equipment: Players can directly purchase in-game items, equipment, skins, etc., to enhance their gaming experience or improve their character's abilities.
  3. Unlocking levels or content: Players can pay to unlock new game levels, missions, or additional content.
  4. Subscription services: Players can subscribe to periodic paid services to receive continuous privileges or rewards.
  5. Events or limited-time offers: During specific events, the game may offer limited-time discounts to encourage players to make purchases.

My Analysis Dimensions

My analysis dimensions (timing to push to players):

  1. Time Dimension
    • Game anniversaries, events
    • Weekly (daily) sign-ins
  2. Scenario Dimension
    • Charging in specific scenarios within the game (e.g., player death, end of a level)
  3. Player Dimension
    • First charge after initial registration
    • Analyzing players' payment habits and amounts

Analysis Methods from the Video (5W2H)

  • What: Whether it is the first payment, first payment = payment on the first day, total number of payments
  • Who: User profile (payment frequency, etc.)
  • When: Time of purchase (year, month, day, hour), days played since registration, days since last payment
  • Where: Last charging point, most frequent charging point, game level
  • Why: Discounts, packages, promotions
  • How: Payment smoothness (conversion rate from package appearance to actual purchase), depth of this charging point (number of screens to go through)
  • How Much: Payment amount, total payment amount, average payment amount

ChatGPT's Answer

Here are some common analysis angles:

  1. Player Behavior Analysis:
    • Activity Analysis: Analyze players' active times to understand when they are most active, allowing for pushing charging points at the right times.
    • Payment Frequency: Analyze players' payment frequency to identify high-frequency payers and low-frequency payers.
    • Spending Habits: Analyze where players tend to spend within the game and their spending habits (one-time large payments vs. multiple small payments).
  2. Charging Point Effectiveness Analysis:
    • Popularity of Charging Points: Analyze the popularity of different charging points to determine which have the highest payment conversion rates.
    • Location and Content of Charging Points: Analyze the locations (specific levels or events) and content (items, skins, etc.) of charging points to understand their impact on payment behavior.
  3. Time Analysis:
    • Registration Time vs. Payment Time: Analyze the time interval from player registration to first payment to understand at what stage players are more likely to convert to paying users.
    • Payment Cycles: Analyze players' payment cycles (e.g., weekly, monthly payment frequencies) to optimize push strategies.
  4. Player Segmentation Analysis:
    • Player Categorization: Categorize players based on payment amounts, frequency, etc., into different groups (e.g., big spenders, moderate spenders, small spenders, non-spenders).
    • Customized Push Strategies: Customize charging point push strategies for different player groups. For example, push high-value charging points to big spenders and cost-effective points to small spenders.
  5. Promotion and Event Effectiveness Analysis:
    • Payment Behavior During Promotions: Analyze changes in player payment behavior during promotional events to understand the impact of promotions on payments.
    • Promotion Effectiveness Evaluation: Evaluate the effectiveness of different promotional events to identify the most effective promotional methods.
  6. Retention and Churn Analysis:
    • Paying User Retention Rate: Analyze the retention of paying users to understand the relationship between payment behavior and user retention.
    • Churn Prediction: Build churn prediction models by analyzing player activity and payment behavior to identify players at risk of churning and implement targeted push strategies to retain them.
  7. Personalized Recommendations:
    • History-Based Recommendations: Use players' historical payment data to make personalized recommendations and push charging points that might interest them.
    • Machine Learning and Prediction: Use machine learning algorithms to predict players' payment behavior and proactively push suitable charging points.

By conducting comprehensive analysis from these angles, we can better understand player behavior, optimize charging point push strategies, and enhance player payment rates and satisfaction.