Introduction
E-commerce platforms like Hegobuy, which specialize in proxy shopping (代购), require robust data analysis to track user growth and optimize acquisition strategies. By leveraging spreadsheet tools (Google Sheets, Excel), marketers can transform raw growth metrics into actionable insights to drive sustainable expansion.
Key Metrics to Analyze in Spreadsheets
- New User Registration Rates
- Growth Rate (%): [(Current Period Users - Previous Period Users) / Previous Period Users] × 100
- Retention Rate: % of users returning after first purchase (e.g., 30-day retention)
- Channel Attribution: Comparing user acquisition efficiency across social media, SEO, referrals, etc.
Step-by-Step Data Analysis Approach
- Data Organization: Structure raw data with columns for date, user ID, acquisition source, country, and purchase frequency.
- Validation & Cleaning: Remove duplicates with
=UNIQUE(); flag outliers using conditional formatting. - Time-Series Analysis: Use
=GROWTH()- ROI Calculation: Map marketing spend per channel against new-user conversions.
Growth Strategy Recommendations
| Challenge | Strategy | Spreadsheet KPI |
|---|---|---|
| Low retention rates | Personalized post-purchase emails with discounts | Retention rate by cohort (=COUNTIFS) |
| High CAC in paid ads | Refine targeting using preference data from surveys | CAC by channel (Advertising Spend / New Users) |
| Seasonal demand fluctuations | Promote holiday-specific bundles (CNY, Singles' Day) | Week-over-week growth variance |
Execution with Spreadsheet Automation
- Automated Reports: Use
=QUERY- A/B Test Tracking: Compare landing page variants with
=TTEST- Forecasting: Predict 6-month growth using
=FORECAST.ETS - A/B Test Tracking: Compare landing page variants with
Conclusion
By systematizing Hegobuy’s user growth analysis through structured spreadsheet frameworks—paired with responsive strategies like retention campaigns and channel optimization—teams can achieve scalable growth. Regularly updating datasets and validating hypotheses through split testing ensures continuous adaptation to shifting proxy-shopping trends.