A B2B SaaS client in the compliance automation space came to us spending $48,000 per month across Google Search, LinkedIn, and retargeting, yet their CRM showed only 11 sales-qualified leads per month at a cost-per-SQL of $4,360. The core problem was not the channels or even the copy. It was that every channel was being credited equally for every conversion, so the team was optimising toward the wrong inputs entirely.
The Starting Point: What the Data Actually Showed
When we pulled the full conversion path data from Google Analytics 4 and cross-referenced it with Salesforce opportunity records, a clear pattern emerged. LinkedIn was responsible for 71% of first touches on deals that eventually closed, but it was receiving only 18% of the budget because last-click attribution made it look like an underperformer. Google Search was capturing the credit for branded and competitor queries that came after LinkedIn had already done the heavy lifting.
This is a textbook attribution distortion problem. The sales cycle averaged 47 days, which means a last-click model was systematically misrepresenting which channels were actually moving pipeline. We wrote a more detailed breakdown of how this plays out across B2B funnels in our piece on multi-touch attribution and B2B ROI.
The Restructure: Budget Reallocation and Channel Role Definition
The first step was assigning each channel a defined role in the funnel rather than expecting every channel to produce direct conversions. LinkedIn was repositioned as a demand generation and first-touch channel, with budget increased from $8,600 to $19,000 per month. Google Search budget was reduced from $24,000 to $14,000, focused tightly on high-intent, non-branded terms and competitor comparisons. Retargeting spend stayed flat at $6,000 but was restructured by audience segment and deal stage.
We also tightened the Google Search campaigns by removing broad match on informational queries and building a proper negative keyword structure. Many of the wasted clicks were coming from terms like "compliance software tutorial" and "what is SOC 2," which attract researchers, not buyers. For a detailed walkthrough of that process, see our article on eliminating wasted spend with negative keywords.
Landing Page and Lead Qualification Changes
Budget reallocation alone would not have moved the SQL number. The LinkedIn traffic was landing on a generic product overview page with a "Request a Demo" form that asked only for name and email. With no qualification questions, the sales team was spending time on leads that had no budget authority or irrelevant company sizes.
We rebuilt the demo request flow with three qualification fields: company headcount, current compliance framework, and role. This reduced raw form fills by 31%, but SQL conversion from those form fills rose from 22% to 51%. The page also got a complete copy revision focused on outcomes for the compliance officer persona rather than feature lists. If your landing pages are producing similar drop-offs, the patterns we cover in our article on why B2B landing pages don't convert are directly relevant.
Results After 90 Days
After a 30-day ramp and 60 days of stable running, the numbers were:
- Monthly SQLs increased from 11 to 26, a 136% lift
- Cost-per-SQL dropped from $4,360 to $1,990, a 54% reduction
- LinkedIn-sourced pipeline share rose from 18% to 44% of total attributed revenue
- Google Search CPC held steady at $12.40 average, but click-to-SQL conversion doubled
- Total ad spend increased by only $1,000 per month, from $48,000 to $49,000
The result came almost entirely from measurement and allocation changes, not from spending more. Google's own research on data-driven attribution consistently shows that last-click models undervalue upper-funnel channels by 30-50% in long sales cycles, which aligns exactly with what we observed here.
What Generalises From This Case
Three patterns from this engagement apply to almost any B2B SaaS company running multi-channel paid media with a sales cycle longer than three weeks. First, last-click attribution in a long-cycle B2B environment is not just inaccurate, it actively drives bad budget decisions. Second, reducing form friction without adding qualification tends to improve vanity metrics while hurting pipeline quality. Third, LinkedIn's CPCs look expensive on a per-click basis but often look very different when you trace first-touch influence through to closed revenue.
The intervention here was not technically complex. It required honest measurement, a willingness to reallocate budget away from the channel that looked best on a last-click dashboard, and landing page copy that filtered for fit rather than maximising volume. Most B2B paid media accounts we audit have at least one of these three problems in place, and frequently all three at once.
How to Apply This to Your Own Account
Start by pulling conversion path reports in GA4 for any deal that took longer than 14 days to close. Look at the assisted conversion column for each channel and compare it to the last-click column. If the gap between those two numbers is larger than 2x for any single channel, you likely have a budget misallocation problem driven by attribution. Google's attribution model comparison tool in GA4 makes this audit straightforward to run without any custom setup.
From there, pick one channel that is under-credited and run a 60-day test where you increase its budget by 30% while holding everything else constant. Track pipeline influence, not just last-click conversions. The signal usually becomes clear within two months, and the cost-per-SQL math tends to shift faster than most teams expect once they are measuring the right thing.