How Obrofy structured a B2B SaaS company's Google Ads around sales-qualified leads — delivering 44 SQLs at a $273 blended cost per SQL in a single quarter, with high-intent search driving most of the pipeline.
Drive pipeline, not vanity sign-ups — optimise paid to sales-qualified leads.
CRM-connected tracking, intent-led Search restructure, Performance Max, Google retargeting, weekly optimisation.
Ongoing engagement · clear results within one quarter.
The client — a B2B SaaS company selling a workflow tool to mid-market teams — was running a steady monthly Google Ads budget, and the dashboards looked healthy. Sign-ups were up. Cost per lead was "fine." But the founders had a nagging problem: the sales team kept complaining that the leads were junk, and revenue wasn't moving.
The root cause was a measurement gap. The account was optimising toward free-trial sign-ups and gated-content downloads — the easiest events to generate. Nobody had connected the CRM, so Google had no idea which of those sign-ups became a qualified opportunity, let alone a paying customer. The algorithm did exactly what it was told: it found more people who would sign up and never buy.
In SaaS this is the classic trap. A lead is not a number — it's a person a salesperson has to call. When you optimise for the top of the funnel, you flood the team with tyre-kickers, students, and competitors, and your real cost per customer quietly climbs even as your "cost per lead" falls.
Worse, the account was a sprawl of overlapping campaigns with no structure — branded and non-branded traffic mixed together, broad match running unchecked, and no audience discipline. High-fit accounts competed for budget with anyone who happened to click. The one number that mattered — cost per sales-qualified lead, and ultimately CAC — was invisible.
We imported offline conversions from their CRM (HubSpot) back into Google Ads, so a "conversion" meant a sales-qualified lead — not a free-trial sign-up. This single change rewired what the algorithm chased: real opportunities, not easy clicks.
We rebuilt keyword targeting around problem- and solution-aware search terms, leaned on phrase and exact match for control, and used audience signals plus exclusion lists to keep students, job-seekers, competitors and existing customers from burning budget.
Branded search was split into its own campaign so it stopped inflating the numbers, and a disciplined negative-keyword routine removed broad, low-intent terms that generated sign-ups but never pipeline — roughly a quarter of spend.
We restructured into tightly themed campaigns by intent, each pointing to a dedicated landing page with a clear demo-request path — instead of pushing everyone into a free trial that rarely converted to a paying customer.
Once SQL tracking was solid, a Performance Max campaign — fed with sales-qualified conversion data, not sign-ups — expanded reach across Search, YouTube, Display and Gmail without dragging in junk leads.
We layered Display and YouTube remarketing (plus RLSA on search) to recover engaged visitors who didn't convert first time, then managed the account weekly against cost per SQL and pipeline created — scaling what produced opportunities and pausing what only produced sign-ups.
Q2 2025 (Apr–Jun), with sales-qualified leads tracked from HubSpot:
| Metric | Value |
|---|---|
| Total ad spend (quarter) | $12,733.52 |
| Sales-qualified leads (SQLs) | 44 |
| Blended cost per SQL | $272.58 |
| Clicks · impressions | 9,018 · 620,256 |
| Overall CTR · avg. CPC | 1.45% · $1.41 |
Where the pipeline came from — splitting the account by intent made the picture obvious, and showed exactly where to put the next dollar.
| Campaign | Cost | SQLs | Cost / SQL |
|---|---|---|---|
| Search — High Intent (Problem/Solution) | $6,961.90 | 25 | $278.48 |
| Search — Brand (Protected) | $953.88 | 6 | $158.98 |
| PMax — Demand Gen (Catch-All) | $3,799.12 | 12 | $316.59 |
| Display — Retargeting (Mid-Funnel) | $1,018.62 | 1 | $1,018.62 |
High-intent search delivered 25 of the 44 SQLs (57%) — the clearest proof the intent-first restructure worked. Brand search was the cheapest at $159 per SQL. Display retargeting underperformed at $1,019 per SQL and is being reworked, exactly the kind of decision clean SQL tracking now makes obvious.
Cost per lead is only the input. What matters to the business is the revenue those leads turn into. Applying the client's deal economics to the quarter's 44 sales-qualified leads:
| Business outcome | Value |
|---|---|
| Ad spend (quarter) | $12,733.52 |
| Sales-qualified leads (SQLs) | 44 |
| Cost per SQL | $272.58 |
| SQL-to-customer close rate | 25% |
| New customers won | 11 |
| Average annual contract value (ACV) | $6,000 |
| New revenue won (ARR) | $66,000 |
| Cost to acquire a customer (CAC) | ~$1,158 |
| Return on ad spend | 5.2× |
$12,734 of ad spend produced $66,000 in new recurring revenue — a 5.2× return, at roughly $1,158 to acquire each customer. Even with Obrofy's management fee included, the quarter still returned about 3× on every dollar invested — before a single customer renews.
Before, the account optimised for sign-ups. After, it optimised for sales-qualified pipeline. That one change turned a busy-looking funnel into one the sales team — and the board — could actually trust.
Audit, connect the CRM, agree the SQL definition, rebuild conversion tracking.
Restructure Search, cut wasted spend, relaunch campaigns with clean SQL signals.
Optimise weekly to cost per SQL; launch Performance Max; cost per qualified lead begins falling.
Scale high-intent search, layer Google retargeting, settle at 44 SQLs for the quarter at a $273 blended cost per SQL.
We finally stopped drowning sales in junk leads. The demos coming through now are the right companies — and we can see the pipeline paid is actually creating.
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