Cross-border e-commerce store review: data-driven operational optimization

Analytical ideas and methods

The review of online stores can start from different time dimensions, including year, quarter, month, week and even day. In-depth analysis of data within a specific time period can help us better understand store operations. This article will take the data of a cross-border e-commerce store from February 11 to May 31, 2020 as an example to discuss in detail how to use the pivot table function of Excel to conduct an effective store review.

Application of pivot tables

First, we need to open the worksheet containing the required data and use the “Pivot Table” tool provided by Excel to organize and display the data. Specific steps include selecting an appropriate data range, setting the layout of the pivot table, and adjusting the display mode of various indicators.

Detailed explanation of review steps

Store review is not limited to daily data review, but also a systematic project, involving multiple links from goal review, result comparison to final summary of rules.

1. Review Goals: Clarify the goals or expected results during the review and ensure that the entire team has a consistent understanding of this.
2. Compare results: Identify differences and try to understand the reasons behind them by comparing actual results with pre-set goals.
3. Narrative Process: Describe the entire execution process in detail so that everyone involved can fully understand the situation.
4. Self-analysis: Deeply reflect on your own behavior and look for room for improvement.
5. Ask questions: Encourage team members to ask questions to promote broader and deeper thinking.
6. Summary rules: Summarize and extract lessons of universal significance.
7. Case support: Use similar cases to verify the validity of the conclusions obtained.
8. Review archiving: Record the review results to facilitate subsequent learning and application.

Detailed explanation of indicators

In this example, the main indicators we focus on include the number of visitors (Number of visitors), the number of payers (Number of payers), the average order amount (per ticket sales), etc. By tracking and analyzing these key indicators, we can clearly see the trend of store performance changes in a specific period, and then formulate corresponding optimization measures.

Conclusion

Through detailed analysis of the above data, we found that during the study period, the average order amount (per ticket sales) of the store showed a downward trend, suggesting the need to strengthen related sales strategies to increase the unit price; at the same time, the repurchase of old customers The number of old buyers paid has increased, indicating that the store has achieved positive results in maintaining customer relationships. The above information is based on the specific data provided, reflecting the importance of data-driven decision-making.