> For the complete documentation index, see [llms.txt](https://doc.playturbo.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://doc.playturbo.com/creative-insights/creative-insights-user-guide-analysis-approach-and-tips.md).

# Creative Insights User Guide: Analysis Approach and Tips

[General Tracking - Usage Recommendations](/creative-insights/creative-insights-user-guide-analysis-approach-and-tips/general-tracking-usage-recommendations.md)

<mark style="background-color:orange;">**Overall Strategy——From Macro to Micro**</mark>

1. Examine macro datas such as area, channel, and mobile os to understand the overall situation. Identifying creative preferences among different channels and areas.
2. Under specific conditions (e.g., the same area and channel), compare the performance of different creatives to identify high-performing creatives and those to be optimized.
3. Based on different cases (A/B test or creative diagnosis), compare the basic tracking data for the target creatives. Approximately identify the problematic step.
4. Examine individual creatives to pinpoint data anomalies in specific gameplay scenarios or use custom tracking for precise issue identification and optimization planning.

[User Journey - Usage Recommendations](/creative-insights/creative-insights-user-guide-analysis-approach-and-tips/user-journey-usage-recommendations.md)

<mark style="background-color:orange;">**Overall Strategy——From Overall to Specific**</mark>

1. Start by examining all the Actions related to the core gameplay flow and identify the stages where user loss is the highest. Analyze the tracking data and creative content to identify factors contributing to the loss.
2. By analyzing the "Drop Off and Redirection data" related to the initial segments, we can examine whether the material meets expectations in terms of attracting attention, guiding players, and minimizing the cognitive load.
3. If the material leads to different outcomes (such as victory or failure) based on user actions, analyze the differences in completion rates and redirection rates between parallel storyline branches. Gain insights into player interaction tendencies and the effectiveness of inducing different outcomes.


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