Same budget. 40% more revenue. One year.
Multi-location pest control and wildlife removal across three Texas markets. The problem: zero visibility into which marketing was producing customers.
Q1 2025 → Q1 2026 · Attribution + SEO + GEO
Read the full case study →More revenue
same ad budget
Q1 2026 revenue
up from $157K
More revenue
same ad budget
Annual run rate
post-attribution rebuild
The attribution gap was hiding in plain sight
The client was running Google Ads, Meta, and Bing. Platform dashboards showed campaigns converting. Revenue was growing. Nobody had connected the marketing data to actual invoiced customers — because that connection didn't exist yet.
We built the connection: 28-field attribution stack linking every ad click to every CRM record to every dollar of revenue. When the data came in, two things became clear immediately.
A Facebook campaign had absorbed budget for months at 1.04× ROAS — barely breaking even, reported as healthy because nobody had looked at actual revenue. A Bing campaign was running at 10× ROAS and was significantly underfunded.
Budget moved. Same total spend. 40% more revenue. That's the attribution gap.
What each channel actually produced
Google Business Profile
The highest-converting channel in the stack. GBP leads converted at 66% — once we could measure it. Previously invisible because nobody had wired GBP clicks through to CRM outcomes.
Organic / Direct
Organic search and direct traffic combined. High-intent customers who found the site through search or had prior brand familiarity. Lowest cost per acquisition in the stack.
Facebook Ads
Lowest conversion rate of all paid channels — and the campaign running at 1.04× ROAS that looked fine in the platform. Attribution revealed the true picture; budget was reallocated.
AI / ChatGPT
AI-referred traffic was detected and classified in the attribution stack before most agencies acknowledged it as a real channel. GEO optimization is now part of the engagement.
The full case study covers every decision
The full case study walks through the attribution build: the Ashley Discovery, the 4-bucket classification system, the Streamlit dashboard architecture, the channel reallocation, and the revenue outcome. It's the book chapter, written down.
Read the full case study →Attribution fields captured per customer
Data sources integrated into one dashboard
CRM customer buckets — new, reactivation, no-sub, ghost
Fake conversion rate revealed by classification
Find out where your attribution is lying to you.
A 30-minute conversation is usually enough to identify the gap. No deck. No proposal. Just a look at your data.
Talk to Cameron