What we walked into
Commercial solar is a fundamentally different business from residential solar, and Google's search landscape does not reflect that distinction automatically. Residential solar has saturated broad match, shopping intent, and most informational queries. When this commercial solar contractor came to us, they had no paid media presence and a clear constraint: every dollar spent on a residential lead was wasted, because their crews, their financing structures, and their sales process were built entirely around commercial and industrial projects.
Starting from zero meant we had no historical data to inherit, no conversion window to look back on, and no benchmark for what a reasonable cost per commercial lead looked like on Google. What we did know was that commercial solar queries are low-volume, long-tail, and scattered across a search landscape dominated by residential advertisers bidding on the same root terms. Without surgical keyword selection and an aggressive negative list from day one, spend would drain into irrelevant clicks before we could generate a single qualified inquiry.
The stakes were straightforward: if we could not deliver commercial leads at a cost-per-lead the client's project economics could absorb, the channel would not survive. Commercial solar deals are large but the sales cycle is long, so the client needed a steady monthly flow of qualified inquiries, not occasional spikes. Getting the targeting architecture right before scaling spend was not optional — it was the only viable path to making paid search work in this niche.
What we changed
- We built the keyword list entirely from commercial-intent queries — terms specifying building type, business class, or commercial scale — and added a negative keyword list covering residential, home, house, rooftop, and hundreds of related terms before the first impression served.
- We launched in a single keyword per ad group structure organized by target geography, giving us clean quality score signals and the ability to isolate which locations were generating clicks without conversion so we could cut spend there first.
- When this structure produced high costs at $540 per lead and only 11 leads per month, we diagnosed the cause as budget fragmentation across too many low-volume ad groups and consolidated into a single campaign, letting Google's auction logic concentrate impression share on the queries that were actually converting.
- We applied location insertion to ad headlines so users in each market saw copy that named their city or region, preserving geographic specificity without duplicating creative across dozens of ad variants.
- We set the primary conversion event to form completions on a dedicated commercial-only landing page, explicitly excluding any residential inquiry path, so the bid algorithm optimized toward leads that matched the client's actual qualification criteria from the start.
What happened next
Before consolidation, the account was generating 11 commercial leads per month at $540 each on $5,900 in monthly spend, with a conversion rate of 1.1%. Those numbers indicated the targeting was reaching real commercial intent — we were not seeing zero conversions — but the fragmented campaign structure was diluting budget across too many ad groups with insufficient data for Google to optimize bids effectively at the query level.
After consolidating into a single campaign, the conversion rate climbed from 1.1% to 4.2%. That movement was the clearest signal that consolidation was the right call: the same commercial-intent traffic was converting at a meaningfully higher rate once budget concentrated on the query patterns that had already demonstrated intent. Monthly spend increased to $10,000, and monthly leads rose to 36.
The net result was a cost per lead that dropped from $540 to $280 — a 48% reduction — while lead volume tripled. For a commercial solar contractor whose average project value sits well above typical residential deals, a $280 cost per qualified commercial lead is defensible across any reasonable close rate assumption. The account reached that position inside a single optimization cycle, not after months of incremental tuning.
The takeaway
When search demand for your specific service is buried inside a much noisier category, keyword architecture and negative coverage matter more than budget size. Starting with granular structure gave us clean data on what commercial intent actually looks like in paid search, but the bigger lesson is that fragmented structure punishes niche advertisers disproportionately: consolidating budget into fewer campaigns let the algorithm find converting queries faster, which is what drove the conversion rate improvement that made the cost-per-lead economics work.