High-Ticket Remodeling Case Study: 8 Qualified Leads on a $7K Budget
A high-ticket remodeling company in Clackamas, Oregon needed qualified pipeline, not vanity clicks. By modeling the dollar ROI of every keyword and concentrating spend on the clusters that returned over 100x, we generated eight leads worth roughly $1.78M in pipeline over two months, on about $7,000 of total budget.
Engagement overview
The client is a high-ticket remodeler serving the Clackamas and greater Portland market, with projects ranging from basement remodels to accessory dwelling units (ADUs) and full custom homes. Because those jobs carry six- and seven-figure tickets and long, deliberate sales cycles, the objective was never fast closed revenue. It was to fill the top of the funnel with high-value, qualified opportunities the client's sales process could convert over the following months.
This was a Google paid-search (PPC) engagement. The client hadn't invested in SEO yet, so every lead here came from paid clicks. Attribution was handled by the client directly: he asks each new inquiry how they found him, and these leads were attributed to Google PPC.
For a business like this, our job is to generate pipeline, not sales. A campaign judged on two months of closed revenue would look weak against a business whose biggest projects take a season to sign, so we optimized for qualified pipeline value and let the closes land on the client's timeline.
Results at a glance
| Metric | Result |
|---|---|
| Total budget (our fee + ad spend) | ~$7,000 |
| Leads generated (2 months) | 8 |
| Qualified pipeline value | ~$1.78M |
| Closed within first 2 months | $22K+ |
| Largest single lead (custom home) | ~$1.4M |
| ADU lead | ~$300K |
Figures have been rounded to protect client privacy; they preserve the relative scale of the result but are not exact. Pipeline value reflects the estimated contract value of qualified leads, not booked revenue. The ~$300K ADU and ~$1.4M custom home together account for most of the pipeline total; the remaining leads make up the balance.
The method: dollar-weighted keyword ROI
Most keyword research ranks terms by search volume. For a high-ticket remodeler that is actively misleading: a term with ten searches a month for a $1.4M custom home is worth far more than a term with a thousand searches for a $500 repair. So instead of chasing volume, we modeled the expected dollars behind every keyword.
- Build the keyword universe with Google Keyword Planner, capturing every service term a remodeling buyer in the market might search.
- Enrich with real data via DataForSEO: exact search-volume figures and average project price data for each service, so the model runs on actual numbers rather than Keyword Planner's wide estimates. We pulled this at the Portland DMA level: the third-party provider didn't have enough data at the city (Gladstone) or county (Clackamas) level, and the Portland DMA is the right market unit anyway. It spans the metro and reaches across the river into Vancouver, WA.
- Compute ROI per keyword by weighting realistic search volume and click-through against a conservative close rate and the true ticket price of the service behind the term. The output is a ranked list of keywords by expected revenue, not by traffic.
One honest caveat: at launch we didn't yet have the client's own click-through, conversion, or close-rate data, so the model used industry averages for those inputs. That's the right starting point (it's conservative and gets the campaign pointed at the right clusters) and as the client's own performance data comes in, we swap the averages for their real numbers to sharpen the model to their business specifically.
Ranking keywords by dollars rather than volume is the same discipline whether the channel is paid or organic. We cover it in how to forecast revenue from keywords and keyword clusters. Here it drove the paid-search bids; the same map will guide SEO when the client is ready to build it.
The video above walks through the research: 32 keywords for a local remodeling company, every one with a modeled ROI over 10, a preview of the same method behind this campaign. It frames the numbers around Vancouver, WA because the data was modeled at the Portland DMA level, which covers both the Oregon metro and Vancouver across the river.
Where the ROI actually was
When we ran the numbers, one cluster stood far above the rest. The ADU, custom home, and basement remodel keywords returned over 100x ROI, driven almost entirely by ticket price. These services don't need much search volume to be extraordinarily profitable to bid on, because a single close is worth hundreds of thousands to over a million dollars.
These are modeled returns built on industry-average click-through and close rates, the expected ROI of targeting each cluster, not a realized multiple. The realized return firms up as the pipeline closes and the client's own conversion data replaces the averages.
| Keyword cluster | Typical ticket | Modeled ROI | Priority |
|---|---|---|---|
| Custom home | $1M+ | 100x+ | Hit hard |
| ADU | $250K–$400K | 100x+ | Hit hard |
| Basement remodel | High five figures | 100x+ | Hit hard |
| General / lower-ticket terms | Lower | Modest | Deprioritized |
So we hit those clusters hard. Rather than spreading the budget thinly across every remodeling term, we concentrated it on the handful of keyword clusters the model proved were worth many multiples of what they cost to win.
What the pipeline produced
Over two months the campaign generated eight leads worth approximately $1.78M in qualified pipeline. The two that defined the result came straight out of the high-ROI clusters:
- A ~$1.4M custom home. Now in the planning stage and expected to fully close around four months after the campaign started, exactly the long-cycle, high-value opportunity the strategy was built to capture.
- A ~$300K ADU. A second six-figure project sourced from the same cluster.
- $22K+ closed in the first two months from faster-moving work, showing the program produced near-term cash while the marquee projects worked through the pipeline.
This is why pipeline, not two-month closed revenue, is the honest scoreboard for a business like this. The $1.4M custom home is the campaign's biggest win, and it won't appear in a “closed sales” column until months after the ads that sourced it stopped running.
The result is also concentrated: two leads account for nearly all of the pipeline value. That concentration is a real risk in the short term. In any single two-month window a marquee deal can slip or stall, and it's normal for local high-ticket home-service companies where one custom home or ADU can outweigh a full quarter of smaller jobs. Over the long term, though, that risk mitigates itself: keep bidding the highest-ROI keyword clusters month after month and the big wins average out into a dependable flow. Chasing lower-ticket volume to feel “diversified” would only trade away the ROI that makes the account work.
Key takeaways
- Rank keywords by dollars, not volume. Enriching Keyword Planner with DataForSEO price and volume data surfaced clusters worth 100x that a volume-only view would have buried.
- Concentrate budget on the high-ROI clusters. A ~$7K budget aimed at ADU, custom home, and basement remodel terms outperformed anything a broad spread could have done.
- Measure pipeline for long sales cycles. For high-ticket remodeling, the job is qualified pipeline; the closes land on the client's timeline, not the campaign's.
Frequently asked questions
Why measure pipeline instead of closed sales?
Because this is a high-ticket remodeler with a long sales cycle. A custom home or ADU takes months to move from first inquiry through planning to a signed contract, so judging a two-month campaign on closed revenue alone would badly understate what it produced. For businesses like this our job is to generate qualified pipeline. The closes follow on the client's sales timeline, not the campaign's.
How did you find the ROI of each keyword?
We started with Google Keyword Planner for the keyword universe, then enriched every term with exact search-volume data and real average project price data through DataForSEO. Multiplying realistic search volume and click-through by a conservative close rate and the actual ticket price for each service gave a dollar-weighted ROI per keyword, not a guess based on volume alone. Because we didn't yet have the client's own click-through, conversion, or close-rate data at launch, those inputs used industry averages; as the client's real performance data comes in, we replace the averages to make the model specific to their business.
Which keywords had the highest ROI?
The ADU, custom home, and basement remodel cluster returned over 100x, modeled on industry-average click-through and close rates. Those services carry six- and seven-figure tickets, so even at modest search volume the expected revenue per keyword dwarfs the cost of bidding on it. That is exactly where we concentrated the budget.
How was the budget spent?
Roughly $7,000 total across the two months, which covers both our management fee and the client's Google Ads spend combined. There was no waste on broad, low-ticket terms; the spend was pointed at the high-ROI clusters the model identified.
What made up the $1.78M in pipeline?
Eight leads over two months. The two largest were a ~$300K ADU project and a ~$1.4M custom home, which together account for most of the pipeline value; the remaining leads made up the balance. Over $22K closed inside the first two months from faster-moving work, and the custom home, in the planning stage, is expected to fully close around four months after the campaign started.