User Journey - Usage Recommendations
Last updated
Last updated
For detailed instructions, please refer to User Journey Analysis
Playturbo - Creative Insights - PL Interaction Analysis - User Journey Analysis.
Enter project keywords (product name, version name, etc.) in the search box to find the corresponding project.
Once a date range with campaign data is selected, the journey chart will be displayed automatically below.
For detailed instructions, please refer to User Journey Analysis
π‘Once you enter the User Journey Analysis interface, you can diagnose the materials using the following four core metrics:
| User Journey Metrics | Definition | Data Interpretation Methods |
|---|---|---|
π‘The rules for the relationship between different Actions and metrics are as follows:
Session for Action N = The sum of the Arrival Count for all other actions that can lead to Action N + the Transition to Next Count for Action N.
Session for Action N = Loss Count for Action N + Redirect Count for Action N + Transition to Next Count for Action N.
Next, we will analyze the user journey using the case of "Classic Tower Defence" as an example.
In this case, we have set up the following custom tracking events:
Action1: First weapon drag
Action2: Successful initial weapon placement
Action3: Successful second weapon placement
Action4: Game victory
Action5: Click on CTA on the victory page
Action6: Enemy reaches our base
Action7: Game failure
Action8: Click on Retry on the failure page
After configuring the user journey, the following user journey chart is obtained.
From Overall to Specific:
1οΌOverall: Analyzing all the Actions related to the core gameplay flow - Action2 has the highest Drop Off rate at 30%.
2οΌSpecific:
Initial Guidance: Action1 has a high Exit to Next count, accounting for 99.5%.
Midway Interaction: Action2 has a high Drop Off count with a rate of 30%.
Different Endings: There is a significant difference in the Session count for Action4 (Victory) compared to Action6/7 (Failure). Additionally, there are differences in the Redirect Count between Action4 β Action5 (Victory) and Action7 β Action8 (Failure).
Next, let's delve into a more detailed analysis.
[Related Metric]: Exit to Next
[Optimization Dimension]: Initial Guidance
[Data Meaning]: Out of 1000 users who started dragging weapons, 99.5% successfully placed the weapon.
[Conclusion]: No need to optimize the initial guidance
The placement function is working properly.
The placement instructions are clear, and there are minimal cases of users misunderstanding the operation.
Users have a desire to experience the effects after placing the weapon, with very few cases of abandoning or quitting halfway through.
[Related Metric]: Drop Off
[Optimization Dimension]: Midway Interaction
[Data Meaning]:
Action2 has the highest Drop Off rate at 30% during the trial process.
After successfully placing the weapon for the first time, 30% of players did not continue their interaction.
[Conclusion]: The motivation for midway gameplay needs optimization.
Possible reasons could be a high number of steps, high cognitive load in understanding the gameplay, low engagement with the content, or weak feedback on actions.
In this case, Action2 does not introduce new game rules, and there's only one step before it. Therefore, the main reasons for the high churn rate could be:
Repetitive content leading to reduced novelty and interest.
Weak feedback on actions, resulting in users not experiencing the expected shooting satisfaction and lacking motivation to continue.
[Related Metric]: Session
[Optimization Dimension]: Game Difficulty
[Data Meaning]:
It is difficult to win the game: Out of 300 users who continued playing after the second weapon placement (Action3), only 100 reached the victory page (Action4), while 300 entered a state of imminent failure (Action6).
Out of the 350 players who were on the verge of failure but continued interacting, none of them managed to turn the situation around, and all 350 ended up on the failure page (Action7).
[Conclusion]: The game difficulty is too high and needs to be adjusted.
The number of failures is much higher than victories.
It is necessary to consider reducing the difficulty level, especially when comparing the results with the data from subsequent Actions. If the redirection effect for the failure ending is not as effective as the victory ending, lowering the difficulty may allow more users to achieve victory.
Note: The higher count of Action6 arrivals (>300) is because there are other Actions besides Action3 that can lead to Action6.
[Related Metric]: Exit to Next, Redirect
[Optimization Dimension]: Ending Page Inducement Effect
[Data Meaning]:
Among users who reach the failure page, approximately 43% (14.3% + 28.6%) directly redirect or click the "Retry" button (CTA) to redirect.
Among users who reach the victory page, approximately 75% (50% + 25%) directly redirect or click the CTA button to redirect.
[Conclusion]: Increasing the probability of reaching the victory ending or strengthening the inducement on the failure page can improve the overall redirection data during the trial.
The victory ending has better overall inducement effects compared to the failure ending.
Based on the analysis above, we can draw the following conclusions:
Issue 1: After successfully placing the weapon for the first time, many users lack motivation to continue playing, resulting in loss.
Issue 2: The game difficulty is relatively high, with a higher number of failures than victories, and the inducement effect on the failure page is not ideal.
[Issue 1]
Currently, the appearance and attack methods of the three weapons are quite similar. To enhance interest and motivate users to continue placing weapons, differentiate the weapons or introduce unique and intriguing weapons.
The enemy kill feedback is bland. Strengthen the shooting effects or add combo statistics to enhance the satisfaction of actions, encouraging users to continue interacting driven by a sense of achievement.
[Issue 2]
Considering the better redirection effect on the victory page, lower the difficulty of the game by reducing enemy HP and attack power, increasing the chances of winning.
Additionally, enhance the inducement effect on the failure page, increasing the probability of failed users redirecting to the store by using provocative copywriting or other engaging strategies.
| Best experience for playable on mobile devices | Vertical screen | Horizontal screen |
|---|---|---|
Session
=The total number of users who triggered this action.
Low Session Count/Rate: Indicates that most users did not reach that node and did not experience the content of that action. Possible reasons could be not choosing that storyline branch, churning or redirecting in previous steps.
Exit to Next
=The number of users who triggered this action and subsequently triggered other actions.
High Exit to Next Count: Indicates that most users continued playing after that step, which may be due to high user appeal and low cognitive load for that part.
Low Exit to Next Count: Indicates that most users did not continue playing. It is important to focus on the Drop Off Count and Redirect Count for that action to understand the specific reasons.
Drop Off
=The number of users who triggered this action but did not trigger any other actions or redirections.
High Drop Off Count/Rate: Indicates that most users stopped interacting at that step, possibly due to insufficient motivation or difficulty understanding the gameplay content.
Redirect
=The number of users who triggered this action and then redirected to the store before reaching the next action.
*Events triggered after the redirection will not be included in the journey statistics.
High Redirect Count/Rate: Indicates that the redirection mechanism after that action is functioning properly, and most users successfully redirected to the store.
For actions related to redirection, if the Redirect Count/Rate is low, it could be due to high cognitive load in understanding the redirection mechanism or lack of appeal, leading to players not triggering the redirection action.
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