Customer Compass™
Geographic Customer Intelligence
Reduce Customer Acquisition Costs With Better Geographic Targeting
Broad marketing reaches many people who may never become customers. Customer Compass helps you focus future campaigns by revealing which ZIP codes already produce customers and how those areas compare across national household-income deciles.
Use actual customer patterns to choose where to test your next campaign—rather than spending equally across every ZIP code.
Rank ZIP codes by the number of customers already associated with each location.
Prioritize geographic areas with an established customer presence and test similar income-ranked ZIP codes.
Compare future campaign spending and purchases by ZIP code to learn where each new customer costs less to acquire.
Why this matters: Higher response and conversion rates can lower cost per acquired customer at the same cost per contact. Customer counts alone do not establish response rates; use campaign mailing or impression totals and costs to confirm improvement.
Upload Customer CSV File
The first two columns must be five-digit ZIP code and unique identifier, in that order. Any additional columns are allowed.
| ZIP code | Customer ID | Other columns… |
|---|---|---|
| 00501 | C1001 | Any data |
| 02108 | C1002 | Any data |
| 90210 | C1003 | Any data |
Leading zeros are preserved: ZIP codes such as 00501 and 02108 are treated as five-character text, not numbers. If a spreadsheet has already removed a zero, the tool can pad a numeric ZIP shorter than five digits; verify the original ZIP before uploading.
Analysis Results
Customers by Income Decile
D1 = highest income; D10 = lowest income.
Decile Summary
| Decile | Customers | Share | Unique ZIPs | Observed Income Range |
|---|
Customer ZIP-Code Detail
| State |
|---|
ZIP-Code Targeting Explorer
Find national income-ranked ZIP codes by state, city and decile. D1 is the highest-income group. Customer lists are not required.
National Income Deciles
| ZIP Code | City | State | Median Household Income | Decile |
|---|
Customer Compass™ — Video Walkthrough
Watch the Customer Compass™ video to learn how geographic customer intelligence can help guide targeted marketing decisions.
How Better ZIP-Code Targeting Can Lower Customer Acquisition Cost
See how a business could use existing customer geography and verified campaign results to focus advertising expenditure.
Why Geographic Targeting Can Lower Customer Acquisition Cost
Customer acquisition cost (CPA) is the money spent on a campaign divided by the number of new customers acquired from that campaign. If you can reach people in ZIP codes that purchase more often, your marketing budget may generate more customers per dollar. Customer Compass helps identify where customers currently live; you can use those observations to select geographic tests rather than assuming every ZIP code deserves the same spending.
Worked example: A $100,000 direct-mail campaign
Suppose a business can mail 100,000 pieces at $1 each. Based on historical campaign response and purchase data, the highest-performing ZIP codes account for 30,000 mailing addresses and convert 3% of mailed contacts into new customers. The remaining 70,000 addresses convert at 0.8%. All numbers below are hypothetical and hold offer, cost per piece, and other conditions constant.
| Campaign approach | Mail pieces | New customers | Spend | Acquisition cost |
|---|---|---|---|---|
| All eligible ZIP codes | 100,000 | 1,460 | $100,000 | $68.49 |
| Focus on stronger ZIP codes | 30,000 | 900 | $30,000 | $33.33 |
How to apply the example: First, upload your current customer list to see which ZIP codes and income deciles contain customers. Next, use ZIP Explorer to find candidate ZIP codes within a selected city or state. Then run a small, controlled campaign and record contacts, spending, and purchases for every tested ZIP. Retain areas where measured acquisition cost meets your business goal, and test unfamiliar ZIP codes rather than automatically treating them as poor performers.
Important limitation: A customer list contains purchasers, not the number of people who received advertising. A ZIP with many customers may simply have more households or have received more advertising. Neither its customer count nor its income decile alone establishes its response rate or acquisition cost. The worked example assumes those performance differences are already measured from comparable campaigns.
How to Use Customer Compass
1. Prepare your customer list
Save a CSV with ZIP code in column A and one unique customer ID in column B. All other columns are optional. The first row is treated as column headings. For example: zip_code,customer_id,name,email.
2. Protect leading zeros
ZIP codes must contain five digits. Export ZIP columns as text when possible. Codes such as 00501 must remain five characters. The tool can restore zeros in ZIP fields containing one to four digits, but cannot independently confirm that an altered code was correct.
3. Analyze customers
In Customer Analysis, select a CSV file. Duplicate customer IDs count only once; conflicting ZIP codes for one ID are flagged. All optional fields are kept locally in your browser. Only unique ZIP strings are sent to the website for lookup.
4. Read the report
Customer counts indicate where customers already live. The report lists national income deciles (D1 highest to D10 lowest), customer share, geographic coverage and top customer-producing ZIP codes. A ZIP code without observed customers is not necessarily a poor prospect if it has never been marketed to.
5. Find additional ZIP codes
Use ZIP Explorer to filter by state, optional city and national income decile, then export the list. Selecting a city does not recalculate the national deciles.
6. Use exports
Download the summarized ZIP report, a locally enriched customer CSV, or ZIP Explorer results. Acquisition cost or response rates cannot be inferred from customer counts alone without campaign spending and exposure data.
About the data
The supplied reference uses 2024 Census ACS five-year ZCTA income estimates. Postal ZIP codes and Census ZCTAs are not identical; some addresses may lack a matching income estimate. Unmatched data is displayed separately.