---
title: "Validated Addresses In HubSpot: Why It Matters, How We Built It, And The ROI You Can Expect"
description: Improve your mailing efficiency with validated addresses in HubSpot. Learn why it matters, how we built it, and the ROI you can expect.
---

[Lloyd Solves Insights & News ](https://blog.lloydsolves.com)

# [Validated Addresses In HubSpot: Why It Matters, How We Built It, And The ROI You Can Expect](https://blog.lloydsolves.com/usps-validation)

 Written by [Kenny Lloyd](https://blog.lloydsolves.com/author/kenny-lloyd) | Oct 15, 2025 3:13:51 PM

# Why validated addresses matter

- **Fewer returned envelopes**  
  Returned mail means double printing, double postage, and delays. Validation catches typos, incomplete fields, and undeliverable addresses before mail leaves your office.
- **Standardized formatting for every list**  
  A standardized address enables cleaner deduplication and better list hygiene. That improves segmentation, deliverability, and reporting.
- **Faster fulfillment and service recovery**  
  Teams stop chasing fixes after something fails. They work from correct, mail-ready data the first time.
- **Better brand experience**  
  Time sensitive items that arrive when promised build trust. That trust compounds across client relationships and donor programs.

# How we built the USPS validation inside HubSpot

We implemented validation as a lightweight, repeatable workflow using HubSpot Data Hub and a custom coded action.

**Workflow overview**

1. **Trigger**  
   Enroll contacts when any of the following change: Street address, City, State, Postal code. You can also enroll new records on creation.
2. **Custom coded action**
   
     - Language: Python 3.9
     - Inputs from the enrolled contact: `saddress`, `city`, `state`, `zip`
     - Secrets: `usps_client_id`, `usps_client_secret` (stored securely in HubSpot)
     - Call: USPS API to validate and standardize
     - Returns to HubSpot:
       
           - `usps_validated` (Yes or No)
           - `std_street`, `std_city`, `std_state`, `std_zip`
           - `validation_status_msg` for debugging
3. **Preserve the original**  
   Write the original street value to `orig_streetaddress` so you have an audit trail.
4. **Update the record**  
   Overwrite the visible address fields with the USPS standardized outputs. Set `USPS Validated` to Yes when appropriate.
5. **Reporting**  
   Build a simple report by lifecycle stage and owner that shows: percentage validated, number of non-validated contacts, and top reasons from `validation_status_msg`.

**Why this approach works**

- No external middleware to maintain.
- Secrets keep your USPS credentials out of code snippets and exports.
- It is fully transparent. Your team can see the original and the standardized value side by side.

 

Checkout this video walking through how it works!

 

 

# Expected ROI: a practical model you can copy

Use the framework below with your own numbers. I will show a sample calculation with realistic but conservative assumptions.

**Define inputs**

- Annual mail volume: `V` pieces
- Baseline return rate before validation: `R0`
- Return rate after validation: `R1`
- Postage and materials per piece: `C_mail`
- Staff handling time per returned piece: `T_minutes`
- Fully loaded hourly cost for staff: `C_hour`

**Compute costs**

1. Returned pieces before validation  
   `Returned0 = V × R0`
2. Returned pieces after validation  
   `Returned1 = V × R1`
3. Hard cost of wasted mail saved  
   `Savings_mail = (Returned0 − Returned1) × C_mail`
4. Labor time saved  
   Convert minutes to hours: `T_hours = T_minutes ÷ 60`  
   `Savings_labor = (Returned0 − Returned1) × T_hours × C_hour`
5. Total annual savings  
   `Total_savings = Savings_mail + Savings_labor`
6. Investment  
   Combine Lloyd Solves implementation plus your annual USPS API usage and any HubSpot tier deltas. Call this `Investment`.
7. ROI  
   `ROI = (Total_savings − Investment) ÷ Investment`

**Worked example**

- `V = 5,000` pieces per year
- `R0 = 8 percent` return rate before validation
- `R1 = 2 percent` after validation
- `C_mail = 1.10` dollars per piece
- `T_minutes = 4` minutes to research, correct, and reprocess
- `C_hour = 25` dollars per hour
- `Investment = 5,000` dollars all-in for year one

Step by step

1. `Returned0 = 5,000 × 0.08 = 400` pieces
2. `Returned1 = 5,000 × 0.02 = 100` pieces
3. `Savings_mail = (400 − 100) × 1.10 = 300 × 1.10 = 330` dollars
4. `T_hours = 4 ÷ 60 = 0.0667` hours  
   `Savings_labor = 300 × 0.0667 × 25`  
   `0.0667 × 25 = 1.6675`  
   `300 × 1.6675 = 500.25` dollars
5. `Total_savings = 330 + 500.25 = 830.25` dollars
6. `ROI = (830.25 − 5,000) ÷ 5,000 = −0.83395` or negative in year one with these exact inputs

What this tells us

- The example used a small mailing volume and conservative costs.
- ROI becomes positive quickly as volume, baseline return rate, or cost per piece rises.
- In many firms we see larger mail volumes, higher waste rates, and staff time well above 4 minutes per piece once reprints are included.

**Sensitivity scenarios**

If any one of the following is true, the math flips:

- Volume is `20,000` pieces with the same rates
  
    - `Returned0 = 1,600`, `Returned1 = 400`, savings in mail = `1,200 × 1.10 = 1,320`, labor savings = `1,200 × 1.6675 = 2,001`, total savings = `3,321`, ROI with `5,000` investment becomes `(3,321 − 5,000) ÷ 5,000 = −0.3358`. Still negative, so adjust two drivers.
- Keep `20,000` pieces and raise `C_mail` to `1.90` and `T_minutes` to `6`
  
    - `T_hours = 0.1`, labor savings = `1,200 × 0.1 × 25 = 3,000`
    - Mail savings = `1,200 × 1.90 = 2,280`
    - Total savings = `5,280`
    - ROI with `5,000` investment becomes `(5,280 − 5,000) ÷ 5,000 = 0.056`, or `5.6 percent`
- Many legal and healthcare mailrooms report baseline return rates above `10 percent` for legacy lists. If `R0 = 12 percent` and `R1 = 2 percent` at `20,000` pieces with `C_mail = 1.90` and `T_minutes = 6`, then:
  
    - `Returned0 = 2,400`, `Returned1 = 400`, delta = `2,000`
    - Mail savings = `2,000 × 1.90 = 3,800`
    - Labor savings = `2,000 × 0.1 × 25 = 5,000`
    - Total savings = `8,800`
    - ROI with `5,000` investment becomes `(8,800 − 5,000) ÷ 5,000 = 0.76`, or `76 percent` in year one

**Soft benefits that are real money**

- Faster receivables when invoices arrive on time
- Fewer complaint calls and make-goods
- Sharper analytics since address fields are normalized
- Better list match to shipping and third-party services

# Implementation checklist you can reuse

- Create a Data Hub workflow on Contact enrollment.
- Add a custom coded action in Python 3.9.
- Configure Secrets for USPS credentials.
- Map inputs: street, city, state, zip.
- Parse the USPS response and return standardized fields and a validated flag.
- Preserve the original address in a separate property for audit.
- Overwrite address fields with standardized values and set USPS Validated.
- Build reports and a list of contacts where validation failed.
- Train your team and document the edge cases such as suites and PO Boxes.

# What success looks like in 30 days

- At least 90 percent of active contacts carry a USPS Validated value.
- Your non-validated list is owned by operations for cleanup.
- Mailing returns drop in the next campaign.
- A simple dashboard shows validation rate by owner and lifecycle stage.
- Finance confirms less reprint and postage waste.

# Ready to see it in your HubSpot portal?

We install, document, and train your team so value sticks. If you want validated, standardized addresses running in your firm, email **hello@lloydsolves.com** or use the link below to book a call and we will walk you through the next steps.

[View full post](https://blog.lloydsolves.com/usps-validation)

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