Case Studies / Flooring Contractor

It wasn't a creative problem. It just looked like one.

A regional flooring contractor had a month of solid creative and a CPL that kept climbing. The issue was not the ads. Testing and scaling were sharing the same campaign and the algorithm had already decided who it liked.

Vertical

Flooring Services

Platform

The account looked like it had a creative problem. It had a structure problem dressed up as one.

When I took on this account it had been in paid ads for three months. A regional flooring contractor operating across three states in the South — Texas, Florida, and Oklahoma — offering multiple coating systems for garages, driveways, patios, and interior spaces. The previous setup had one campaign handling everything: broad cold traffic, retargeting, and testing all sitting together under a CBO. The client had been consistently briefing and delivering new creative assets from their side, four to five concepts a month, all going into that same campaign alongside whatever was already running. What I saw was what I inherited.

CPL was sitting at $65 and had drifted up steadily over the prior six weeks. The natural read was that the creative had fatigued. But when I looked at the spend distribution, one ad from the previous month was absorbing roughly 65% of the daily budget. New concepts were each getting $30 to $50 in spend before the algorithm moved on.

Nothing new had a fair chance. The algorithm had already settled on a comfortable winner and had no reason to go looking for something else. More creative was not the fix. Separate infrastructure was.

Core constraint

"Testing and scaling inside the same campaign means your winners always win the budget before the tests have run."

In this vertical, set rate is the signal that matters most. A CPL that looks acceptable can mask a lead quality problem that only surfaces when the sales team starts making calls. The structural fix had to come first before any creative conclusions could be trusted.

Budget allocation: inherited state

Dominant ad (1 ad) 65%

New concepts (4–5 ads) 35%

Each new concept averaging $30–$50 in spend before the algorithm moved on.

The Approach

Separate the infrastructure. Then read the creative data.

The account was generating leads, but the signal behind them was unclear. It was not obvious which buyer the system was finding or why.

Split testing from scaling: ABO for new concepts, CBO for proven winners only

Audit every running ad at the set rate level, not just CPL

Hook type as a deliberate buyer-selection test, not a copy variable

Geo concentration backed by zip-level data, not geographic ambition

Separate testing from scaling before judging creative

The account was producing leads, but the setup made the signal hard to trust. New concepts were competing against proven winners inside the same system, which meant the algorithm protected what already had history. I separated testing from scaling first so future creative reads would mean something.

Audit what was actually running

Pulled all-time performance by ad and evaluated at the set rate level, not just CPL. Two ads that looked acceptable on cost per lead had set rates below 15%. Both came off. Budget concentration shifted to the two ads producing qualified leads at a defensible cost.

Hook type and format as a buyer selection test

Once the structure was clean and producing readable data, ran a deliberate creative comparison across hook types and formats. Problem-aware angles, process-led videos, benefit checklists, aspirational product showcases, and geo-qualified copy all went through the ABO testing campaign. The goal was not just performance ranking — it was understanding which buyer each angle was finding and how close to the decision they were when the algorithm reached them.

Geo concentration for quality

The account was running broad across Texas, Florida, and Oklahoma. Pulled zip-level CPL and set rate data. One state was generating volume but the leads were not converting at the sales level. Reallocated that budget into the two geos producing installs and added location callouts in the headline copy.

Creative

Six angles. Two clear winners. One pattern that held across every format tested.

The creative test ran inside the ABO testing campaign with equal daily budget per ad set and the same landing page across all concepts. What came back was not just a performance ranking. It was a map of which angle found which buyer — and how far along the decision that buyer actually was when the algorithm reached them.

Before and after garage floor transformation

Winner

Problem-aware static + social proof body

Opened on a side-by-side transformation — stained concrete on the left, clean coated floor on the right. The hook was geo-qualified and direct: "sick and tired of your old concrete floors?" The body leaned into local customer reviews rather than feature claims. Viewers who recognized the problem in the visual were already close to the decision. The downstream numbers confirmed it.

Highest set rate of all formats tested. Promoted to the CBO scaling campaign once it had enough history behind it.

Winner

Process video — installation walkthrough

Dropped straight into the installation. No hook line, no problem framing — just a worker pouring coating onto bare concrete, the flake broadcast, the leveling, the finished floor. "1-Day Installation" landed mid-video as the key objection answer. Workers visible in the final frame kept it grounded. The process transparency did the trust work that copy usually has to carry.

Strong set rate alongside the static winner. Best cost per qualified lead among the video formats tested.

Learning

Benefit-led video + scarcity hook

Led with seasonal urgency — "this is the perfect time" — then moved through a structured benefit sequence: speed, durability, warranty. The product differentiation angle (positioned against epoxy as the inferior alternative) generated strong engagement. But the buyer it found was mid-funnel at best — comparing options, not ready to book. The lifetime warranty close drove clicks that did not convert at the same rate downstream.

High CTR, lower set rate. Reached an earlier buyer stage than the problem-aware formats.

Learning

Visual transformation video — soft sell

The most aesthetic of the formats tested. Camera moved across freshly coated driveways and walkways, landscaping in frame, minimal text. No urgency, no problem framing, no objection handling — purely aspirational. Generated engagement from people who liked what they saw. The leads it produced were the earliest in their thinking and set rate reflected that gap clearly.

Lowest set rate of all formats. Useful for retargeting warm audiences, wrong tool for cold traffic qualification.

Premium metallic epoxy flooring portfolio across multiple spaces

Tested

Portfolio showcase — premium finishes

Four-panel grid of completed installs across different spaces: living room, garage, entryway, feature room. Metallic and artistic finishes front and center. Geo-qualified in the copy ("Hey Oklahoma!") with a luxury positioning angle. Strong for showcasing range. But the viewer it attracted was browsing, not problem-aware — and that distance from the decision showed up in the quality of leads it produced.

Mid-table on set rate. Strong engagement from people not yet close to booking.

Finished chip flooring garage with color selector

Tested

Product showcase — color variety static

Clean finished garage with a color chip selector strip across the bottom. No before state, no problem framing — straight to the aspirational result with variety implied. Attracted people at the earliest stage of consideration: curious about options, not yet convinced they needed to act. Useful context for a retargeting sequence but not a cold traffic qualifying tool.

Lowest qualification rate on cold traffic. Not a standalone cold campaign asset.

Results

The structure unlocked the data. The data guided the creative.

The structural changes produced cleaner data within the first month. The creative work that followed was built on a foundation where the numbers meant something. Five months in from the start of the engagement — the account had been live for about eight months total by this point, but the delta below reflects the period of active management.

$36

Cost per lead

Down from $65 at the start

44%

CPL reduction

Structural cleanup drove most of this before any creative changes

37%

Set rate

Up from 22% account-wide at baseline, up from 15% on the worst ads

States (down from 3)

Budget concentrated into geos producing qualified installs

What I took from this one

Structural problems produce creative symptoms. The CPL drift looked like fatigue. It was the algorithm protecting a comfortable winner while new creative starved. The fix was not a better brief. It was a campaign structure that gave new concepts room to generate signal without competing against a proven ad's head start.

Hook type tells you which buyer shows up, not just how many. The curiosity hook and the problem-aware hook had similar click-through rates. Their set rates were eight points apart. The hook is not just about stopping the scroll. It is selecting for a specific person at a specific stage of the decision. In lead gen, that distinction shows up downstream, not in the ad metrics.

Geographic concentration beats geographic ambition at moderate spend levels. Running three states at $85k a month sounds like scale, but split three ways each market was getting roughly $28k. That is not enough to build meaningful signal across a spread that wide when the geos have different competitive dynamics. The more useful move was stepping back and reassessing with the client: which geos actually aligned with their growth priorities, and which ones were just generating noise? Concentrating spend in the two markets where installs were closing — with location callouts reinforcing geographic relevance in the creative — improved both CPL and lead quality without changing the total budget. The third state did not get abandoned. It got treated differently at a lower spend level until it could justify more.

Before the work

Testing and scaling in one CBO with the algorithm deciding allocation before new ads had generated any meaningful signal

CPL at $65 with no systematic way to evaluate which ads were producing qualified leads versus raw volume

Two ads with sub-15% set rates running because their cost per lead looked acceptable in isolation

Budget spread across three states with no zip-level data showing where leads were actually converting to installs

Hook type treated as a copy decision rather than a targeting and buyer-stage decision

After the work

ABO testing campaign giving each concept equal budget for a minimum of five days before any verdict was drawn

CBO scaling campaign running confirmed winners only, with performance history worth trusting behind each one

Set rate used alongside CPL for all creative decisions, evaluated at the same level as the sales team read the account

Budget concentrated in two high-converting geos with location callouts reinforcing geographic relevance in the creative

Hook type tested deliberately to understand which buyer each angle qualifies before any scaling decisions were made

→

The structural fix came from asking the right diagnostic question first

The Diagnosis playbook covers how to separate creative problems from structural ones before touching anything in an account. The question this case study starts with (creative problem or structure problem?) is documented there with the signals that point to each. Read it →

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Why the hook type test was a targeting decision, not a copy decision

The Hooks Are Targeting playbook covers how different hook types signal different buyer states to the algorithm and qualify different people. The eight-point set rate gap between the curiosity hook and the problem-aware hook is exactly what that piece explains. Read it →

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The ABO and CBO split is documented in the Creative Coverage playbook

The three-campaign model behind this fix, why CBO starves new creative in a mixed testing and scaling setup, and how to give each stage its own infrastructure so both actually work. Read it →