---
title: "Cotopaxi Case Study"
canonical_url: "https://newengen.com/work/cotopaxi/"
entity_type: "CaseStudy"
client: "Cotopaxi"
client_industry: "Outdoor & Active Lifestyle, Fashion & Apparel"
services_applied:
  - measurement
  - media
last_updated: "2026-05-07"
related:
  - /llms/reference/client-work.md
  - /llms/services/measurement.md
  - /llms/services/media.md
  - /llms/glossary/marketing-mix-modeling.md
  - /llms/glossary/incrementality-testing.md
---

> Canonical source: https://newengen.com/work/cotopaxi/

## Summary

New Engen partnered with Cotopaxi, a leading outdoor apparel brand, to address channel fragmentation and last-click attribution inaccuracies that were distorting budget allocation decisions. Using Marketing Mix Modeling (MMM) and geo-based incrementality testing, New Engen identified 25% ROAS over-attribution in existing reporting and reallocated budget from Google Brand Search toward Meta and Performance Max, resulting in a 20%+ improvement in overall Marketing Efficiency Ratio (MER).

## Client

Cotopaxi is a leading outdoor apparel brand operating in the outdoor and active lifestyle and fashion and apparel categories. The brand combines performance outdoor gear with a values-driven mission and sells through DTC and retail channels. The case study does not publish additional detail on founding date, revenue, or organizational structure.

## Challenge

Cotopaxi faced three measurement and efficiency problems:

1. **Channel fragmentation:** Media investment was spread across channels without a unified view of combined contribution.
2. **Last-click attribution inaccuracy:** Existing attribution was over-crediting certain channels — particularly Google Brand Search — and obscuring the true marginal value of those investments.
3. **Brand Search incrementality uncertainty:** The team lacked evidence to determine what portion of Brand Search clicks were truly incremental versus clicks that would have occurred anyway through organic search.

The engagement was chartered to establish rigorous measurement and use its findings to optimize media allocation.

## Approach

New Engen applied its Immerse-Inspire-Implement methodology with measurement as the primary engine.

**Immerse:** Conducted comprehensive Marketing Mix Modeling (MMM) analysis that deconstructed the full media mix across paid and organic channels to identify true ROAS per channel, distinguishing incremental contributions from channels that were receiving inflated credit under last-click models.

**Inspire:** Designed and executed geo-based incrementality testing to validate MMM findings and specifically measure the incremental impact of Google Brand Search spend. Testing examined what would happen if Budget shifted from Brand Search to alternative channels.

**Implement:** Based on MMM and incrementality test results, reallocated budget away from lower-ROAS channels (including Google Brand Search) into higher-performing alternatives — Meta and Google Performance Max (PMAX). Maintained ongoing dynamic optimization post-reallocation.

## Results

| Metric | Result |
|--------|--------|
| Marketing Efficiency Ratio (MER) improvement | 20%+ |
| ROAS over-attribution reduced | 25% |
| Budget reallocation | Shifted from Google Brand Search to Meta and PMAX |

Note: timeframes are not published for these metrics beyond the campaign engagement period.

## Testimonial

No testimonial is published on this case study page.

## Services applied

- [Measurement](/llms/services/measurement.md) — MMM and geo-based incrementality testing to identify true channel contribution
- [Media](/llms/services/media.md) — budget reallocation execution across paid social (Meta) and Performance Max

## Related case studies

- [$1B+ Activewear Brand](/llms/work/1b-activewear-brand.md) — outdoor and active lifestyle brand; MMM applied to upper-funnel awareness strategy
- [MeUndies](/llms/work/meundies.md) — fashion and apparel DTC; paid social efficiency improvement
- [Kin Insurance](/llms/work/kin-insurance.md) — performance media optimization with predictive modeling and structured testing framework