January 15, 2026
Google Ads can fill your inbox surprisingly fast in B2B. The problem is: if those inquiries are a poor fit, have no budget, or go silent after one email, every extra click starts to feel like extra noise. Lead volume goes up, but real conversations (let alone customers) do not.
That frustration almost always comes down to the same tension: volume versus value. In B2B, the step from query to Sales Qualified Lead is narrow, while the path to a deal is often long and influenced by multiple people. That requires sharper intent targeting, better filtering, and—most importantly—measuring quality, not just clicks and form submissions.
That is why a strong approach does not start with “more budget” or “more campaigns,” but with a fundamental question: what is Google Ads actually learning—and does that align with what Sales considers a good lead?
The most common question is simple: “Why do I get leads from Google Ads, but hardly any customers?” In practice, it is rarely the channel itself. It usually fails at the steering mechanism: campaigns optimize for volume (form submissions) rather than buying intent and lead quality. The system then does exactly what you asked for—just not what you ultimately need.
B2B is fundamentally different from B2C. The buying process takes longer, multiple stakeholders get involved, and trust is built through repeated touchpoints (often referenced as the “rule of 7”). As a result, a single click is rarely a direct predictor of revenue. Search does work in B2B when there is existing demand (people actively looking for a solution, vendor, or alternative) and when your offer captures that demand tightly with a clear use case, obvious fit, and a logical next step.
Expectations typically break down when the offer is too broad (“everything for everyone”), when there is not enough room to build data and run tests, or when the funnel after the click does not qualify leads and does not feed insights back to Sales. You will still get leads, but few customers—and it becomes increasingly difficult to identify where quality is leaking.
Many B2B campaigns start with product names or generic service terms. That can drive traffic, but not automatically the right companies. What is often missing is the translation from ICP to search intent: who fits, what problem is happening, and what stage the searcher is in.
A practical way to structure this is an ICP → buying-intent keyword matrix (persona × problem × stage) for the Netherlands/Belgium. It is essentially a fill-in exercise that immediately guides campaign structure, ad messaging, and landing pages.
Stage 1: Problem / exploration (more noise, useful for nurture/retargeting)
When a company is still exploring, you will see more generic problem and process queries. Think “solution for [problem],” “software for [process],” “reduce cost [process],” or “safety standard [industry].” At this stage, the question often sits with teams where Operations, Engineering, IT, or Finance provide input, but a decision is not yet on the table.
Stage 2: Solution / shortlist (mix of volume and quality)
Here intent shifts toward vendors and implementation. Examples include “vendor [solution],” “system integration [tool],” “implementation [platform],” and “partner [category].” Qualifying B2B modifiers that often help in NL/BE include: business, vendor, wholesaler, integration, quote, implementation, B2B, for companies, service contract.
Stage 3: Purchase / action (lower volume, often highest lead quality)
In action mode, queries become concrete and easier to qualify. Think “price [solution] business,” “demo [solution],” “quote [solution],” “RFP [category],” and “alternative to [competitor/alternative].” This is where it quickly becomes clear whether the account is truly set up for quality.
Alternatives people search for (important in NL/BE)
When someone is already comparing, they often search “alternative to [name],” “similar to [name],” “competitor of [name],” or “vs” / “comparison” queries (if this fits your funnel). This traffic can be valuable—if your message and landing page handle the comparison context properly.
The classic pattern is running broad keywords and adding negative keywords too late (or not at all). The account then buys reach on everything that looks similar—including consumer intent, jobs, and “free” searches. The result is predictable: many clicks, apparently healthy lead volume, and a Sales team increasingly responding with “not a fit.”
Separate exclusion lists per intent (starting point)
Instead of one giant exclusion list, B2B often works better with lists per intent cluster. Management stays clearer, and relevant terms are less likely to be filtered out accidentally.
This separation makes it easier to fine-tune per cluster—without “choking” a campaign with overly broad exclusions.
If keywords open the doors, campaign structure determines who actually walks in. A strong B2B structure is usually intent-clustered: not one campaign called “services,” but segments aligned to buying stage, use case, and industry fit. This also makes it possible to use different budgets, bid strategies, and landing pages per cluster.
A pragmatic approach is to keep high-intent (quote/demo/implementation) tight with many negatives and explicit qualification, cover mid-intent (vendor/partner/integration) more broadly but with copy that forces fit, and treat brand plus alternative/competitor traffic separately with stricter messaging. That separation simplifies optimization: what must drive quality does not have to “compete” with traffic that converts easily.
In B2B, ad copy works best when it does not just persuade—but also sets boundaries. Three principles help:
In practice, waste often drops noticeably by adding one explicit sentence indicating who it is not for—such as “no private customers,” “B2B only,” or “for [segment] and up.” It sounds counterintuitive, but it prevents paid clicks that will never become customers—and makes the landing page experience more consistent.
Many campaigns stall because they use one generic landing page for everything. That page then has to convince explorers and purchase-ready searchers and qualify them at the same time. The result is often high conversion rates on “form fills,” while the fit with what Sales needs stays low.
“More friction, better SQLs” does not mean unnecessarily long forms. It means intent-specific landing pages that move the right people forward faster and let the wrong people self-select out—politely and with minimal extra steps. In practice, the difference is usually clarity: what you deliver, who it is for, and what happens after the inquiry.
Intent-specific pages per offer (demo/quote/brochure/consult)
For a demo, it typically works best to focus on product/workflow and what it takes to go live (implementation, integration, timeline). A strong message-match example: “How [solution] works for [use case]—book a demo for teams ready to implement.”
For a quote, it helps to define scope and boundaries, including what is and is not included: “Quote for [solution] including implementation & support—business inquiries only.”
For mid-intent, a brochure can be logical, but make it a qualifying download—not “everything for free.” Example: “Brochure for technical teams: specifications & integrations.” A consult works best as an intake with clear criteria, such as: “Consultation to determine whether [solution] fits your [situation].”
Smart qualification questions that increase quality (without killing conversion rate)
Choose 2–4 questions Sales already asks and make them easy. Typically: company size (range), industry/use case (dropdown with 6–10 options), timeline (“now,” “1–3 months,” “later”), and a short indicator of current situation (e.g., “we currently use [x]” or “no system”).
What often does not work in B2B is forcing a precise budget number immediately, or using a form so technical it scares off real decision-makers. Better “friction” is usually clear boundaries and realistic expectations. The most common irrelevant leads are also predictable: applicants, students/researchers, consumers “just checking price,” companies far too small, or inquiries outside your target industries. Reflect that in copy (“no vacancies”), in negatives (“internship”), and in form choices (company size/industry) to shift inbound volume toward SQLs.
If tracking does not distinguish MQL from SQL, the algorithm learns from the wrong signals. “More” automatically becomes “better” in bidding—while Sales wants fewer but better conversations. The fix is a concrete lead-quality measurement model (MQL → SQL → Opportunity) that shows up in both your measurement plan and your optimization decisions.
B2B works best when the entire chain is measurable: search intent → ad → landing page → CRM/lead scoring. At that point, optimizing for valuable leads becomes more realistic than optimizing for “form fills.” This is also the real answer to “we get leads but not customers”: targeting and conversions must optimize for buying intent and lead quality, tied to SQL criteria and a structured Sales feedback loop.
A workable model starts with clear stages:
The critical point: SQL is not a feeling—it is a set of criteria. Typical SQL criteria (5–10) include industry fit, company size, use-case match, relevant role/involvement, current solution/urgency, budget range (optional), and implementation timing. Only when these are defined can you align keywords, messaging, and conversion actions to them.
To optimize on Cost per SQL—or even pipeline value (instead of only CPA)—you need a measurement chain from click → form → CRM status. A pragmatic setup has three steps:
This only works if your funnel stages and fields are reliable. In practice, email/phone (for matching), source/campaign (for analysis), lifecycle stage (MQL/SQL/Opportunity), and at least one fit indicator (industry/company size/use case) must be consistently populated. Equally important: you need a steady Sales feedback loop—never “once per quarter,” but frequent enough to adjust search terms, ads, and forms based on lead quality.
Audiences and automation are often presented as the solution, but in B2B they only work well with guardrails. Without boundaries, you buy “efficient” traffic that converts easily (low intent), and lead quality drops. The pattern is familiar: performance metrics look better, but pipeline does not.
Audience & automation decision guide (with guardrails)
Mini scorecard (quick self-check)
Are you reviewing search terms weekly and consistently adding negative keywords for irrelevant intent? Are conversions optimizing for MQL volume or for SQL/Opportunity? Is most budget going to high-intent clusters or to “easy” conversions? Can you clearly see which campaign actually produces SQLs?
In many audits, the first check is the combination of search terms and conversion settings—because that is where the biggest gap between “many leads” and “good leads” typically shows up.
This playbook ties to your starting point (daily budget, campaign types, conversions/month, sales cycle) and common frustrations such as irrelevant industries, students/applicants, and companies that are too small. The goal is an executable checklist with decision rules so optimization does not devolve into random tweaks.
Week 1: Account hygiene + measurement foundation
Clean up conversion actions, enable Enhanced Conversions, verify CRM stages, and apply baseline negative lists (jobs/free/consumer).
Decision rule: if SQL is not measurable yet, do not use aggressive “lead” bidding—fix measurement first.
Minimum threshold: in week 1 the focus is data quality, not CPA.
Week 2: Search terms, intent clusters, and landing pages
Review search terms, tighten intent clusters, launch 1–2 intent-specific landing pages (e.g., quote vs demo), and improve message match.
Decision rule: if an ad group has many clicks but no (SQL) signals, tighten negatives and make ad copy more explicit about fit.
Week 3: Ad variants + bid strategy switches
Test 2–3 RSA variants per cluster with fit signals, use case, and “B2B only,” review extensions, and evaluate bidding based on MQL/SQL.
Decision rule: once enough SQLs come in for learning, shift targets toward Cost per SQL instead of Cost per Lead.
Week 4: Budget reallocation + scaling with guardrails
Move budget toward clusters producing SQLs, reduce weak intent, and add remarketing/Customer Match as supportive signals. Use PMax only with caps and strict conversions.
Decision rule: if a campaign generates volume but no SQLs, reduce budget and fix the root cause (keywords/landing page/form/tracking) before scaling.
In practice, costs and billing sometimes affect the question “is it worth it?” Google Ads costs are essentially ad spend plus any management fees; VAT treatment depends on billing setup and country. For the business case, do not focus only on CPC or CPA—focus on what one SQL or opportunity may cost while remaining profitable, and whether sharper intent plus proper measurement can realistically achieve that.
If the core principles are right—ICP and buying intent are sharp, filtering happens through ads and landing pages, and optimization is tied to MQL → SQL → Opportunity with structured Sales feedback—Google Ads becomes a channel that predictably contributes to pipeline instead of just “form fills.” The discussion shifts from “more leads” to “better inbound,” with clearer levers to pull.
The logical next step is a short reality check: are intent clusters correct, are conversions optimizing for quality, and is budget leaking into queries that will never become customers? Request a free B2B Google Ads audit: we will review your account and funnel for lead quality, waste, and quick wins—and deliver a concrete 30-day action plan with priorities, budget guidance, and expected impact.
After completing the form we’ll get in touch to schedule a call. During this discovery call, we determine if and how we can help you.
