---
title: "Who Really Wants Carbon Offsets?"
section: posts
description: "We expected electric-vehicle drivers to engage most with carbon-offset messaging. The strongest response came from truck and SUV drivers."
tags: ["sustainability", "carbon-footprint", "marketing", "customer-engagement", "data-analytics", "consumer-behavior", "fintech"]
created: 2025-02-05T09:58:00Z
updated: 2026-07-16T14:56:30Z
cid: bafkreiggumhdxexmclvz36p4xketmhqvadbx6hytwa7wsimqkd6bhrhzvu
html: https://iammatthias.com/posts/1738778326344-who-really-wants-carbon-offsets
---

# Who Really Wants Carbon Offsets?

We assumed the customers most interested in carbon offsets would drive Teslas and Priuses. The data pointed at trucks and SUVs.

[Aspiration](https://www.aspiration.com) had launched [Plant Your Change](https://www.aspiration.com/plantyourchange), a debit-card round-up program that planted a tree on every swipe. Gas purchases received double cash back and additional trees intended to offset the carbon impact.

## Reading the early numbers

Leadership assumed our core audience would be Tesla drivers, Prius drivers, the environmentally conscious crowd. The logic: people who already care will care more.

I suspected the opposite. Our most engaged users would be the ones with the biggest carbon footprints, looking for a way to compensate, since they chose to open a mission-based CMA account in the first place. We were working with [Faraday](https://faraday.ai), a B2B consumer prediction platform combining our first-party data with theirs.

In a discovery session I learned they could match against DMV records. Vehicle type became the segment cut.

## What the data said

Customers driving electric vehicles weren't the ones engaging with offset messaging. The most engaged customers drove trucks and SUVs.

This wasn't a quirk. These customers knew their footprint. They were constrained by family size, work, geography. Vehicle choice wasn't their lever. Round-up offsets in everyday purchases were.

## What we changed

We stopped targeting on perceived environmental consciousness and started targeting on impact opportunity. The constraints were real, and so were the people inside them.

Engagement went up.

We had confused existing environmental behavior with a desire to reduce impact. The truck and SUV drivers made the distinction visible: their constraints limited one choice, but did not erase the intent behind another.

---

## Related

- [Case Study: Building Tornado's Growth Engine](https://iammatthias.com/posts/1675271943000-case-study-tornado.md): Growth engineering at Tornado: analytics, experimentation, and personalization systems behind major gains in customers, revenue, and engagement.
- [Case study: Aspiration's CRM platform](https://iammatthias.com/posts/1625156340000-case-study-aspiration.md): Two years running CRM and lifecycle at Aspiration. A platform migration, first-party analytics, and campaigns that lifted lead conversion 53%.
- [AndreiBot-3000](https://iammatthias.com/posts/1738693093717-andreibot-3000.md): A GPT-2 system trained on approved Aspiration emails generated additional variants for the existing A/B testing program in 2020.
- [Shitposting Towards a Pizza Oven](https://iammatthias.com/posts/1731475962422-shitposting-towards-a-pizza-oven.md): Automated YouTube Shorts about pizza lead viewers to shouldibuyagozney.com with its one-word answer: "yes." The algorithm grows daily.
- [Now Playing](https://iammatthias.com/posts/1784736733544-now-playing.md): Putting my Apple Music "Now Playing" on display with a Raspberry Pi and a 64x64 LED matrix.