Case Studies

The business owner came with a clear diagnosis: "My spend has gotten out of control compared to sales. My campaigns are running at about 40% efficiency."

They weren't wrong. Grab Bar Specialists—a safety equipment distributor serving nursing homes, hospitals, contractors, and home renovation customers across the USA—had a $30K monthly ad budget producing underwhelming returns. The task was straightforward: increase revenue without raising spending.

But before optimizing anything, I discovered a deeper problem hiding in plain sight.

An 8-figure restaurant furniture manufacturer and distributor approached me with a straightforward request: make PPC campaigns more effective.

But two problems made this harder than it sounds.

Problem 1: Broken historical data. The existing conversion tracking was set up wrong, capturing useless conversions. Every optimization decision had been built on unreliable numbers. You can't improve what you can't measure accurately.

Problem 2: Low transaction volume. The product categories averaged only about 10 sales per month. Smart bidding algorithms need data to learn. Would this volume be enough for the algorithm to optimize effectively?

The challenge wasn't just fixing campaigns. It was rebuilding the measurement foundation while working around data scarcity.

Year 1: The client wanted revenue growth, but the account was structurally broken—46 overlapping campaigns, unreliable tracking, unoptimized feeds, and no platform diversification. Foundation problems couldn't be optimized away; they had to be rebuilt first.

Year 2: New management added a constraint: prove efficiency before scaling. Market headwinds and AOV compression made validation critical. The question shifted from "How do we grow?" to "What does the data tell us to do?"