How GrowthSpree Helped DataHub Triple Its High-Intent Leads
A paid search transformation from cheap content-download conversions to sales-rated, pipeline-generating leads, with high-intent lead share more than tripled.
Case study summary
GrowthSpree rebuilt DataHub's Google Ads around sales-rated lead quality, lifting high-intent lead share from 1.6% to 5.3%, halving cost per high-quality lead and growing deals from the rebuilt campaigns by 79%.
- GrowthSpree more than tripled the share of DataHub's paid-search leads rated High by sales, from 1.6% to 5.3%.
- Cost per high-quality lead fell 50%, while overall cost per lead stayed roughly flat.
- Display and Demand Gen, which took 42% of spend and produced no High-rated leads, were retired to 0%.
- Deals from restructured campaigns grew about 79%, and their share of attributed pipeline value rose from 42% to 67%.
- Performance Max grew from 5% to 70% of conversions on 22% of spend, while themed Search delivered the highest-quality leads.
A low cost per lead built on content downloads that never reached a sales conversation.
Display and Demand Gen retired, broad match removed, campaigns split by product theme and region.
High-intent leads 1.6% → 5.3%, cost per quality lead halved, and 79% more deals.
Who is DataHub?
DataHub is a metadata platform that helps data teams discover, govern, trace and monitor their data. Its paid search program has to reach technical and data buyers across several product themes and regions, and turn them into sales-rated pipeline.
| Category | Metadata platform / data catalog (B2B SaaS) |
|---|---|
| Product themes advertised | Data governance, data lineage, data observability, metadata and AI, plus brand, competitor and financial services campaigns |
| Markets | United States, European Union and global |
| Channels | Google Search and Performance Max (after the rebuild) |
| Definition of success | Leads rated High by sales, attributed deals and pipeline value in the CRM |
Why a low cost per lead was misleading
DataHub's account looked efficient because its cost per lead was low. But most of those leads were gated content downloads from Display and Demand Gen that entered the CRM and stopped there. None were rated High by sales.
Cost per lead
Cheap, and inflated by content downloads. It counted form fills, and few of those were buyers. Reported CPL was low because the account was paying for the wrong thing.
Cost per qualified lead
Higher on paper, but every lead behind it is a sales-rated, pipeline-generating conversation. The value of each lead rose with it.
Where the account started
21 live campaigns across six channel types, structurally unable to show which spend produced revenue.
Four structural problems
The six changes we made
Retire the waste, impose discipline and structure, fix where clicks land, then let Performance Max and Search each do their own job.
Retire Demand Gen
On platform metrics, Demand Gen looked like a top performer. The CRM said otherwise: of the leads sales scored, about 72% were rated Low, the rest Medium, and none High. It never appeared against a closed deal, so it was wound down in stages and fully retired.
Retire Display
Display's "conversions" were gated content downloads that entered the CRM and stopped. The deals it was credited with carried no pipeline value. It was switched off with the rest of the legacy build.
Enforce match-type discipline
Broad match was removed. Search ran on phrase and exact only, split by intent, with brand isolated on exact match. One broad keyword alone had been taking a third of Search budget.
Bifurcate campaigns into a theme × region grid
Blended campaigns were split so each product theme (data governance, data lineage, data observability, metadata and AI, competitor, financial services, brand) in each region (US, EU, global) got its own budget, keywords and landing page. Performance became measurable, and one theme could be funded or cut without touching the rest.
Build landing pages for relevance
Paid clicks moved off developer documentation, expired webinars and blog posts onto product pages matched to each campaign theme. Pages were consolidated onto ones the team controls and tests.
Use Performance Max for volume
Performance Max campaigns were scaled, including an EU product campaign, and tuned to fill the funnel efficiently while themed Search supplied the qualified conversations.
| Stage | What happened |
|---|---|
| 1. Baseline | 21 campaigns; 42% of spend in Display and Demand Gen; broad match unmanaged |
| 2. Rebuild begins | 11 restructured Search campaigns launched; legacy campaigns wound down; PMax conversions scaled |
| 3. Legacy off | All legacy Search and Display switched off; PMax reaches 58% of conversions |
| 4. Scale and test | EU product PMax launched; bid experiments opened; Demand Gen enters its final stage |
| 5. Consolidation | Demand Gen fully retired; global financial services campaign added; bid tests judged and cut |
| 6. Steady state | 16 campaigns (12 Search + 4 PMax), zero broad match serving |
Performance Max vs themed Search
Performance Max buys conversion volume cheaply but broadly: 2.5–3.5% of its leads were rated High, versus 13–25% from themed Search. PMax fills the funnel; Search supplies the qualified conversations. The budget split between them is set deliberately and reviewed.
Share of Leads Rated High by Sales, by Campaign Type
View the numbers behind this chart
| Campaign type | Role | High-rated lead share |
|---|---|---|
| Performance Max | Fill the funnel; 70% of conversions on 22% of spend | 2.5–3.5% |
| Themed Search | Supply qualified, sales-ready conversations | 13–25% |
The account before and after
The account moved from 21 blended campaigns across six channel types to 16 campaigns across two, where every live campaign maps to one product theme in one region.
| Area | Before | After GrowthSpree |
|---|---|---|
| Success metric | Platform cost per lead | Sales-rated lead quality and attributed deals |
| Channel mix | 6 channel types; 42% of spend on Display + Demand Gen | Search + Performance Max only |
| Match types | 71 broad keywords, 31% of Search spend | Phrase and exact only; brand isolated on exact |
| Campaign structure | 21 campaigns blending geo, theme and funnel | 16 campaigns, one theme per region each |
| Landing pages | 26 pages incl. docs and expired webinars; 36% of spend on product pages | 16 pages; 79% of spend on theme-matched product pages |
| Performance Max | 5% of conversions | 70% of conversions on 22% of spend, quality-thresholded |
| Documentation pages taking clicks | 7 | 1 |
Same cost per lead, much better leads
Overall cost per lead stayed roughly the same, but the leads were materially better: high-intent share rose from 1.6% to 5.3%, low-quality share fell from 82% to 69%, and deals followed the restructured build.
Before vs After: Seven Numbers That Moved
View the numbers behind this chart
| Metric | Before | After |
|---|---|---|
| High-intent (High-rated) share of paid leads | 1.6% | 5.3% |
| Low-quality share of paid leads | 82% | 69% |
| Restructured campaigns' share of attributed deals | 26% | 61% |
| Restructured campaigns' share of attributed pipeline value | 42% | 67% |
| Spend on product landing pages | 36% | 79% |
| Display + Demand Gen share of spend | 42% | 0% |
| Broad-match share of Search spend | 31% | 0% |
DataHub's results
of paid leads
high-quality lead
restructured campaigns
share of conversions
spend
of Search spend
What didn't work, and what we did about it
Not everything worked first time. These are the calls that shaped the result.
- Reported cost per lead went up.Once cheap content downloads stopped counting, platform dashboards looked worse for a while even as lead quality climbed. We reported on sales-rated leads so the change wasn't misread.
- Scaling Performance Max brought in junk.Low-quality leads from universities and NGOs started flooding the CRM. We fixed it by optimising PMax only to leads that met medium- and high-quality thresholds.
- Several bid experiments didn't beat the control.They were judged and cut rather than left running.
- Demand Gen couldn't be switched off overnight.It was wound down in stages so the account didn't lose volume while restructured campaigns ramped up.
Why it worked
Judge channels on CRM lead quality first
A cheap conversion that never becomes a conversation is not a win. Every channel is measured on sales-rated leads first.
Structure makes optimisation possible
Nothing was diagnosable in the blended account. Bifurcation is why any single theme can now be funded or cut on its own.
PMax and Search do different jobs
PMax buys volume efficiently; themed Search supplies the qualified leads. The split is set deliberately, and reviewed.
Optimise for pipeline
Reported CPL is an input. Sales-rated quality and deals are the outcome, and that is what we manage to.
How to rebuild B2B SaaS Google Ads for pipeline
If your Google Ads account reports a healthy cost per lead but sales says the leads are weak, this is the sequence we used for DataHub.
Score every channel on CRM lead quality
Pull sales lead ratings and deal attribution for each campaign type before trusting platform cost per lead.
Retire channels that produce no qualified pipeline
Wind down Display and Demand Gen in stages when their conversions are content downloads that never become sales conversations.
Enforce match-type discipline
Remove broad match, run phrase and exact split by intent, and isolate brand on exact match.
Bifurcate campaigns by theme and region
Give each product theme in each region its own campaign, budget, keywords and landing page so it can be measured and funded independently.
Send paid clicks to product pages
Move traffic off documentation, blogs and expired webinars onto theme-matched product pages you control and test.
Use Performance Max for volume, Search for quality
Let PMax fill the funnel, optimise it only to medium- and high-quality leads, and keep themed Search for qualified conversations.
Manage to deals and pipeline
Treat cost per lead as an input and review budget splits against sales-rated lead share and attributed pipeline.
Frequently asked questions
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