---
title: "Kin Insurance Case Study"
canonical_url: "https://newengen.com/work/kin-insurance/"
entity_type: "CaseStudy"
client: "Kin Insurance"
client_industry: "Financial Services"
services_applied:
  - media
  - measurement
last_updated: "2026-05-07"
related:
  - /llms/reference/client-work.md
  - /llms/services/media.md
  - /llms/services/measurement.md
  - /llms/services/strategy.md
  - /llms/glossary/incrementality-testing.md
---

> Canonical source: https://newengen.com/work/kin-insurance/

## Summary

New Engen partnered with Kin Insurance, a digital direct-to-consumer home insurance company, beginning in spring 2023 to address whether the brand's paid media program was sufficiently mature to scale efficiently. By building a predictive performance model from Kin's first-party data, restructuring Search accounts using historical analysis, and applying a methodical testing framework, New Engen drove a +260% increase in Closed Won volume year-over-year in January 2024 and +240% in February 2024.

## Client

Kin Insurance is a digital direct-to-consumer home insurance company with aggressive growth objectives. The brand operates in the financial services sector, selling home insurance directly online without traditional broker intermediaries. The company had strong underlying market demand entering the 2023 engagement but needed to determine whether its existing paid media program could support efficient scaling.

## Challenge

Kin Insurance questioned whether its existing paid media program was mature enough to scale efficiently. The challenge was not solely one of volume — the brand needed profitable volume, with internal attribution confirming that growth was occurring at acceptable acquisition economics. The engagement required moving beyond platform-level metrics to connect media investment to business outcomes (Closed Won policies), and restructuring campaigns to optimize for profitability across different markets rather than blended performance averages.

## Approach

New Engen partnered with Kin beginning in spring 2023 and applied its three-phase methodology.

**Immerse:** Established deep collaboration with Kin's internal data team. Rather than relying solely on platform-reported metrics, built a predictive performance model using Kin's first-party data to connect upstream media activity to downstream policy conversion outcomes (Closed Won volume).

**Inspire:** Constructed a customized measurement plan aligned to Kin's specific business objectives. Conducted historical data analysis to identify structural inefficiencies in the Search account. Applied a methodical testing framework examining: device performance, keyword match type behavior, and Performance Max adoption suitability.

**Implement:** Audited historical data systematically. Restructured Search campaigns for market-specific profitability rather than aggregate ROAS optimization. Refined campaign structure based on test results before scaling media spend. Sustained month-over-month volume increases throughout 2023 (with planned seasonal reduction in Q4).

## Results

| Metric | Result | Timeframe |
|--------|--------|-----------|
| Closed Won volume increase YoY | +260% | January 2024 |
| Closed Won volume increase YoY | +240% | February 2024 |
| Volume trend throughout 2023 | Month-over-month increases | Full year (planned Q4 seasonal reduction) |
| Attribution | Internal attribution confirmed profitable volume growth | (ongoing) |

## Testimonial

No named testimonial is published on this case study page.

## Services applied

- [Media](/llms/services/media.md) — Search account strategy and restructuring, Performance Max evaluation, and keyword match type optimization
- [Measurement](/llms/services/measurement.md) — first-party data modeling, predictive performance model development, and business-outcome-aligned measurement plan

## Related case studies

- [Cotopaxi](/llms/work/cotopaxi.md) — structured measurement and budget reallocation engagement using MMM and incrementality testing
- [$1B+ Activewear Brand](/llms/work/1b-activewear-brand.md) — predictive modeling and MMM applied to upper-funnel strategy
- [MeUndies](/llms/work/meundies.md) — paid media program rebuilt to improve new customer acquisition efficiency