How to Format Your CSV for Skip Tracing: The Complete Guide to Getting Maximum Hits

By SkipTrace Pro Team · Published

Learn exactly how to format your CSV file for skip tracing to get the best possible hit rates. Avoid the most common formatting mistakes that cause failed results and wasted money.

Getting a high hit rate from bulk skip tracing comes down to one thing most people overlook: how you format your CSV file before uploading it. Even the most powerful skip tracing engine will return poor results if it can't parse your data correctly.

In this guide, we'll walk through every aspect of proper CSV formatting for skip tracing — including the most common mistakes that silently kill your hit rates.

Why CSV Formatting Matters So Much

Skip tracing APIs match the data you provide against billions of records across public and private databases. The more precisely your data is structured, the better the match. When column headers are misspelled, addresses are crammed into one field, or state names are written out in full, the system either skips the row entirely or makes a poor guess — costing you both a hit and your money.

A properly formatted file can improve your hit rate by 20–40% compared to a poorly formatted one.

The Required CSV Columns

For bulk person skip tracing, your CSV file must include these exact column headers in lowercase with underscores:

Column Example Notes
first_name John No titles (Mr., Dr.)
last_name Smith No suffixes (Jr., III)
address 123 Main Street Street address only
city Los Angeles Full city name
state CA 2-letter abbreviation only
zip 90210 Exactly 5 digits

A correct header row looks like this:

first_name,last_name,address,city,state,zip

Nothing more, nothing less. The column names are case-sensitive. First_Name will not work. firstname will not work. Only first_name is correct.

Step-by-Step: How to Create the Perfect Skip Trace CSV

Step 1: Start with the Right Tool

Use Microsoft Excel, Google Sheets, or any spreadsheet tool that can export as CSV. Avoid word processors like Microsoft Word.

Step 2: Set Up Your Column Headers

In row 1, type each column header exactly as shown:

  • Cell A1: first_name
  • Cell B1: last_name
  • Cell C1: address
  • Cell D1: city
  • Cell E1: state
  • Cell F1: zip

Step 3: Enter Your Data

Start entering records from row 2 onward. One row = one person to skip trace.

Example:

first_name,last_name,address,city,state,zip
John,Smith,123 Main Street,Los Angeles,CA,90210
Jane,Johnson,456 Oak Avenue,New York,NY,10001
Michael,Brown,789 Pine Road,Miami,FL,33101

Step 4: Save as CSV

In Excel: File → Save As → CSV (Comma delimited) (.csv) In Google Sheets: File → Download → Comma-separated values (.csv)

Step 5: Upload Your File

Upload the saved .csv file through the upload page. The system will begin processing immediately.

The Most Common CSV Mistakes (And How to Fix Them)

These are the mistakes that cause the most failed hits and wasted money. Check your file against each one before uploading.

Mistake #1: Wrong Column Header Names

This is the #1 cause of poor results. Even a single character difference will cause rows to fail.

Wrong:

First Name,Last Name,Address,City,State,Zip
firstName,lastName,address,city,state,zip
first name,last name,address,city,state,zip

Correct:

first_name,last_name,address,city,state,zip

Rule: All lowercase, underscores between words, no spaces, no capitalization.

Mistake #2: Using the Full State Name Instead of Abbreviation

The state field must be a 2-letter abbreviation in uppercase. Full state names will not match correctly.

Wrong:

California
florida
New York
texas

Correct:

CA
FL
NY
TX

Mistake #3: Putting the Entire Address in One Column

Many people export their data with the full address in a single column. This makes it impossible to accurately match records.

Wrong:

address
123 Main Street Los Angeles CA 90210
456 Oak Ave, New York, NY 10001

Correct:

address,city,state,zip
123 Main Street,Los Angeles,CA,90210
456 Oak Ave,New York,NY,10001

Mistake #4: Malformed ZIP Codes

ZIP codes must be exactly 5 digits. Anything else will reduce your hit rate.

Wrong:

  • 9021 — too short
  • 90210-1234 — ZIP+4 format
  • 90210.0 — exported from Excel as a number
  • (blank) — missing entirely

Correct:

  • 90210

Pro tip: In Excel, format your ZIP code column as Text before entering data to prevent Excel from stripping leading zeros (e.g., 07001 becoming 7001).

Mistake #5: Titles and Suffixes in Name Fields

Skip tracing databases store names without titles or generational suffixes. Including them creates a mismatch.

Wrong:

first_name: "Mr. John"
last_name: "Smith Jr."
first_name: "Dr. Sarah"
last_name: "Johnson III"

Correct:

first_name: "John"
last_name: "Smith"
first_name: "Sarah"
last_name: "Johnson"

Mistake #6: Using a Semicolon or Tab as the Separator

CSV stands for "Comma-Separated Values." Some European software exports CSVs with semicolons instead of commas. This will cause your entire file to fail.

Wrong:

first_name;last_name;address;city;state;zip
John;Smith;123 Main St;Los Angeles;CA;90210

Correct:

first_name,last_name,address,city,state,zip
John,Smith,123 Main St,Los Angeles,CA,90210

If your exported CSV uses semicolons, open it in a text editor, use Find & Replace to swap semicolons for commas, then save.

Mistake #7: Extra Blank Rows or Header Rows

Some data exports include a title row above the header, or leave blank rows between records. These will confuse the parser.

Wrong:

My Property List Export - March 2026
first_name,last_name,address,city,state,zip

John,Smith,123 Main St,Los Angeles,CA,90210

Jane,Johnson,456 Oak Ave,New York,NY,10001

Correct:

first_name,last_name,address,city,state,zip
John,Smith,123 Main St,Los Angeles,CA,90210
Jane,Johnson,456 Oak Ave,New York,NY,10001

Row 1 should be your headers. Row 2 should be your first record. No gaps.

Formatting CSV Exports from Common Sources

From PropStream

PropStream exports include many extra columns. Before uploading, either:

  • Delete all columns except the 6 required ones, or
  • Simply rename the header row columns to match the required names

The extra columns won't break your upload, but the required column names must match exactly.

From BatchLeads / BatchSkipTracing

These platforms typically use "First Name" and "Last Name" (with spaces and capitals). Before uploading to SkipTrace Pro, change these to first_name and last_name.

From ListSource / Esri

These often include the state as a full name. Add a formula column to convert it: use a VLOOKUP or SUBSTITUTE to change "California" to "CA" across your entire column before exporting.

From County Tax Records

County exports vary widely. Common fixes needed:

  • Split combined "Owner Name" fields into first_name and last_name
  • Convert state to 2-letter code
  • Strip ZIP+4 to just the first 5 digits (use LEFT(A1,5) in Excel)

Pro Tips for Maximum Hit Rates

Use the property address, not a mailing address. If a property is owned by an absentee landlord, you still want the property address for the best matching results.

Clean your list first. Remove obvious bad records: names that are only initials, addresses that are clearly not residential, or records with missing required fields.

Don't include business entities manually. Our system automatically detects LLCs, Trusts, and Corporations and handles them differently from individual names. You don't need to filter them out — they'll be flagged automatically.

Batch your uploads strategically. Larger files (1,000+ records) process at the same rate but are more efficient to manage. If you're testing a new list source, run 100-200 records first to check quality before uploading thousands.

Quick Reference Checklist

Before uploading any CSV, run through this checklist:

  • Header row uses exact column names: first_name,last_name,address,city,state,zip
  • State values are 2-letter abbreviations (CA, FL, TX) in uppercase
  • Address is split across separate address, city, state, and zip columns
  • ZIP codes are exactly 5 digits with no formatting
  • No titles (Mr., Dr.) or suffixes (Jr., III) in name fields
  • File uses commas as separators, not semicolons or tabs
  • No blank rows anywhere in the file
  • File is saved as .csv, not .xlsx or .xls

What Good Results Look Like

With a properly formatted file from a quality list source, you should expect:

  • Hit rate of 65–85% for residential addresses with individual names
  • Higher rates for owner-occupied properties
  • Lower rates for absentee owners, rural addresses, or older data

If your hit rate is below 50%, review your CSV format first — it's almost always a formatting issue before it's a data quality issue.

Still Having Issues?

If you've checked all the formatting above and are still seeing poor results, contact our support team. When reaching out, include:

  1. A sample of 5-10 rows from your CSV (remove any sensitive data)
  2. A screenshot of your current hit rate
  3. The source of your list data

We can usually diagnose formatting problems within minutes.

Ready to Upload?

Download our sample CSV template to use as a starting point. It's pre-formatted with the correct column headers so you just need to paste in your data.

Start your bulk skip trace now — results typically begin appearing within seconds of upload.

Read more skip tracing guides