Okeyhaul Resale Service User Geographic Distribution Analysis in Spreadsheets & Market Expansion Strategy
2025-04-27
This research examines Okeyhaul's resale service user distribution patterns through spreadsheet analytics to identify regional market characteristics and formulate targeted expansion plans.
1. Data Collection Methodology
- Extracted geotagged transaction data from 20XX-Q1 to 20XX-Q3
- Categorized users by administrative regions (province/prefecture level)
- Created normalized datasets for:
- User density per 10,000 population
- Average order value (AOV) by region
- Product category preference matrix
- Built dynamic dashboards using Google Sheets QUERY functions
2. Demographic Insights (Visualization Samples)
| Top 5 Regions | User Share | AOV (USD) | Preferred Categories |
|---|---|---|---|
| California | 22.7% | $89.32 | Electronics, Cosmetics |
| New York | 18.1% | $95.14 | Luxury Goods, Apparel |
| Texas | 9.5% | $76.88 | Home Appliances |
[INSERT HEATMAP CHART: User Concentration by ZIP Code]
3. Market Potential Evaluation Matrix
Calculated weighted scores (1-10) for expansion priorities:
| Factor | Weight | California | Florida | Illinois |
|---|---|---|---|---|
| Purchasing Power | 30% | 9.2 | 7.8 | 8.1 |
| Competition Level | 25% | 6.5 | 4.2 | 5.0 |
Key Finding:
4. Expansion Implementation Plan
Tier 1 (Q3 20XX): Mature Markets
- California:
- NY-NJ Metro:
Tier 2 (Q4 20XX): Growth Markets
- Florida:
- Washington:
All regional forecasts incorporate Sheets macros that auto-update when new Census data imports occur.
5. Measurement Framework
- Monthly tracking of cohort penetration rates
- =COUNTIFS(Registration!E:E,"CA",Registration!D:D,">="&DATE(2023,1,1))
- Cluster analysis of emergent ZIP patterns
- Inventory allocation modeling (70% Tier 1/25% Tier 2/5% Test)