ZIP Code Data for Market Research

## ZIP Codes as Market Units

ZIP codes are the most commonly used geographic unit for market research in the United States. They strike a balance between granularity (too many census tracts) and breadth (too few counties).

## Common Use Cases

| Use Case | ZIP Data Used |
|----------|---------------|
| Store site selection | Population, income, competition density |
| Customer profiling | Demographics of existing customer ZIPs |
| Trade area analysis | ZIPs within drive-time radius |
| Market sizing | Total addressable market by ZIP |
| Competitor mapping | Competitor locations relative to target ZIPs |
| Media planning | ZIP-to-DMA mapping for TV/radio buys |

## Segmentation Systems

Several commercial segmentation systems classify ZIP codes into lifestyle clusters:

- **Claritas PRIZM** — 68 segments (e.g., "Young Digerati," "Shotguns & Pickups")
- **Experian Mosaic** — 71 types and 19 groups
- **ESRI Tapestry** — 67 segments based on Census + consumer data

These systems combine Census demographics with consumer behavior data (purchases, media habits, lifestyle preferences) to create rich profiles for each ZIP code.

## Building a ZIP-Based Market Analysis

A typical market analysis follows these steps:

1. **Define target customer** — Demographics, income, lifestyle
2. **Score ZIPs** — Rank by customer-profile match
3. **Layer competition** — Map competitors and identify gaps
4. **Model demand** — Estimate revenue potential by ZIP
5. **Select sites** — Choose locations in high-score, low-competition ZIPs

## Data Sources

| Source | Data | Cost |
|--------|------|------|
| Census ACS | Demographics, income, housing | Free |
| BLS | Employment, wages | Free |
| USPS | Address counts, vacancy | Free (some) |
| Claritas/Experian | Lifestyle segments | $$$$ |
| SafeGraph/Placer.ai | Foot traffic | $$$ |
| Google Ads | Search volume by ZIP | Free (in platform) |

## Limitations

ZIP-based market research has limitations:

- ZIPs are too large for hyper-local analysis (use census tracts instead)
- ACS data has 2-3 year lag
- Consumer behavior within a ZIP varies significantly
- ZIPs change over time, breaking longitudinal comparisons