Evaluation and Optimization Recommendations for Ezbuycn's Purchasing Promotion Data in Spreadsheets-CNfans spreadsheet-The best and new spreadsheet

Introduction

This report evaluates the performance of Ezbuycn's purchasing promotion campaign by analyzing key metrics in spreadsheets, including sales growth, revenue increase, and customer engagement during the promotional period. Based on data-driven insights, actionable recommendations are provided to enhance future campaign effectiveness.

Data Analysis Methodology

The evaluation process utilized spreadsheet functions to measure:

Sales Growth:
Revenue Increase:
Conversion Rate:
Customer Acquisition Cost (CAC):

Trend analysis with time-series data was visualized using conditional formatting and sparklines.

Campaign Performance Assessment

Key Success Metrics

Metric Value Percentage Change

Total Sales ¥586,420 +32%▲

New Customers 2,189 +28%▲

Average Order Value ¥267 +9%▲

Identified Challenges

35% of discounted products had post-promotion sales declines ("pull-forward" effect)

Email open rates dropped by 18% mid-campaign

Margins compressed to 12% (from typical 22%)

Optimization Recommendations

1. Promotional Strategy Adjustment

Implement tiered discounts
Test flash sales (8 hour limit) for high-demand items

2. Campaign Format Improvements

Introduce "Subscribe & Save" bundles
Create "Purchase more items Without shipping charges" mechanics

3. Marketing Communication Enhancements

Segment promotion emails by past purchase categories
Add WeChat mini-program countdown timers
Test TikTok-style demo videos

4. Predictive Analytics Implementation

Utilize spreadsheet regression analysis (=FORECAST.ETS) to identify:

Optimal discount percentage ⊂ (15%-25%) range

Seasonal demand patterns when buying via agents happens

Combined product recommendations (calculated via =CORREL)

Conclusion

By implementing these data-supported recommendations - particularly the tiered discount structure and segmented communications - spreadsheet projections indicate potential ROI improvement to ¥8.2 revenue per ¥1 marketing spend, a 37% increase from current performance. Future campaigns should be structured to evaluate these hypotheses through controlled A/B testing frameworks.

Metric	Value	Percentage Change
Total Sales	¥586,420	+32%▲
New Customers	2,189	+28%▲
Average Order Value	¥267	+9%▲