Case Study: How a $10 CPC Cap Grew B2B MQLs 6x

Goals and constraints

We inherited a high-competition Google Ads account in a niche with expensive clicks and a complex, technical product. The client had already spent several months trying to generate new customers but was not seeing sales from paid search. Our job was to rebuild the foundation - tracking, structure, and bidding - so the same budget could consistently attract qualified demand and convert it into MQLs and opportunities within a long B2B decision cycle.

Constraints we had to design around:

• Budget: $10,000/month.
• CPC cap: average CPC should not exceed $10.
• Decision cycle: average time to a final purchase decision is ~3 months.
• Channel limitation: search campaigns only.
• Measurement limitation: CRM reporting did not fully match Google Ads due to attribution model differences and integration constraints, so we needed a pragmatic tracking approach that still allowed optimization.

Introduction

This engagement was about turning a noisy search account into a predictable pipeline engine. We rebuilt conversion tracking so Google Ads could optimize toward the actions that correlate with revenue (not just any form fill), then restructured campaigns around the client’s software lines and the way prospects actually search in oil and gas operations. Over time, we reduced average CPC under the $10 cap and increased lead and MQL volume while improving efficiency in a notoriously expensive auction.

One of the clearest short-term signals came from July to August 2025: spend decreased from $10,804 to $9,639 (-10.8%), while leads increased from 20 to 24 (+20.0%). In the same month-over-month window, CPL fell from $540 to $402 (-25.6%) and cost per MQL dropped from $636 to $507 (-20.3%).

Client context

The client is a US-market provider (operating since 1986) that develops and sells cloud software for the oil and gas production value chain. The platform spans multiple functional areas - including production, transportation, land management, accounting, and other operational modules. That breadth matters in search because buying intent, query language, and sales cycle dynamics differ by module.

On the operations side, the client uses HubSpot CRM. HubSpot has a direct Google Ads integration, but it is limited in ways that can prevent clean conversion mapping (especially when you need multiple conversion tiers and reliable syncing for optimization). At the start, the account treated a generic “form submission” as the primary conversion without differentiating intent across the site.

In practice, the website had multiple types of forms with very different business value:

• Request a demo (high intent)
• Webinar registration (mid intent)
• Event registration (mid intent)
• Ask a question (often low intent or support-related)

On top of measurement issues, the legacy campaign setup was not workable: limited keyword coverage, no negative keywords, and chaotic ad group structure. It generated leads at around $350 CPA but produced no sales, which signaled that “a lead” in this account was not synonymous with “a qualified buyer.”

Strategy

We approached this as a full-system rebuild: define what “quality” means in tracking, then build campaigns that can learn and scale in a long-cycle B2B environment. Our strategy was anchored in five hypotheses we tested and iterated against over time.

• Conversion hierarchy: separate primary vs secondary conversions so Smart Bidding can learn from the right signals.
• Brand defense: protect branded demand and limit competitor capture of high-intent brand searches.
• Competitor conquest: selectively bid on competitor brands to surface an alternative in high-intent comparisons.
• Module-level structure: build search campaigns around the client’s software directions because intent, demand, and close timelines vary by module.
• Bidding sequence: start with manual bidding to generate clean initial data fast, then migrate to automated strategies once conversion volume is sufficient.

We also ran a competitive review to understand why the auction was expensive and what messaging patterns competitors used. The take-away was straightforward: this is a crowded auction with similar pricing across vendors, so differentiation needs to focus on usability, feature depth, and operational outcomes rather than price.

During this review, we also identified a competitor running ads on the client’s brand. That finding changed the priority list: we needed to defend brand searches immediately to reduce leakage of the most qualified traffic.

Finally, we treated audience segmentation as a cost-control lever in a search-only setup. In this niche, existing customers can still search for the company and click ads for navigational reasons, so excluding known customers helps preserve budget for net-new acquisition.

Execution

We executed in a sequence designed to protect spend while the account relearned. The goal was to avoid “optimizing for noise” and instead push Google Ads to optimize toward intent that can realistically become an opportunity within the client’s 3-month buying cycle.

1) Rebuilt conversion tracking around intent tiers
We separated the core business action (demo requests) from secondary conversions (webinars, events, general questions). This step was necessary because the original “all forms equal” approach trained the system to pursue volume, not quality.

2) Rebuilt search campaigns from scratch
We did not try to salvage a structure that lacked negatives, had thin keyword coverage, and mixed unrelated intent in the same ad groups. Instead, we built new campaigns aligned to the client’s software directions so we could control budget, messaging, and bidding by module.

3) Implemented brand defense and escalation
After identifying competitor misuse of the client’s brand, we supported the complaint process with Google and launched a brand campaign. The purpose was twofold: capture high-intent branded demand and increase the competitor’s cost to appear on the client’s brand space by occupying more top-of-page coverage.

4) Tested competitor brand campaigns selectively
We launched competitor keyword campaigns to appear alongside competitor ads and present an alternative solution. The effect was mixed: many competitor brands were excluded after poor performance, while a subset did generate conversions. The lesson was that “competitor conquest” needs strict cost and intent controls in expensive auctions.

5) Sequenced bidding: manual first, automation second
From February through May 2025, we used manual bidding to accelerate early learning on new campaigns. Once we had enough statistically meaningful performance data, we shifted to automated bid strategies in June 2025. In this niche, that transition matters because algorithm learning is costly when conversion volume is low and the close cycle is long.

6) Additional tests and guardrails

• DSA for keyword discovery: tested and turned off due to poor efficiency. We used the search terms report instead to expand keyword coverage with proven queries.
• Geo targeting: we explored excluding states with weak performance, but it reduced total conversion volume too sharply. We rolled back those exclusions.
• Demographic targeting: based on industry specifics and observed performance, we excluded female audiences for some directions and focused on ages 25-54.
• New-customer focused campaign: when manual bidding created budget pressure, we launched a “new customers only” campaign and moved it directly to automation to control spend and maintain coverage when other campaigns hit daily caps.

7) Strengthened ad messaging around outcomes, not price
Pricing across vendors was broadly comparable, so we positioned ads around business problems and operational improvements. We built ads carefully with client input and language used by end users, so the copy reflected real buyer priorities in the field.

Tools used
We used Google analytics tools (including GA4), Google Trends, Ads Transparency, Keyword Planner, Optmyzr for automation and reporting workflows, and SE Ranking for supporting research.

Results

Because we redefined conversions (primary vs secondary), a simplistic Google Ads “before vs after” comparison would be misleading. Instead, we measured progress through consistent operational KPIs (leads, MQLs, and opportunities) alongside efficiency metrics (CPC, CPL, cost per MQL, cost per opportunity). The data below is taken from the client’s Google Ads reporting.

Year-over-year signal (January 2024 to January 2025)
Even within an expensive auction, we improved efficiency and volume. Average CPC dropped from $13.27 (Jan 2024) to $6.08 (Jan 2025). Leads increased from 4 to 18, while CPL improved from $849 to $549. MQLs increased from 3 to 14, with cost per MQL improving from $1,132 to $706.

Mid-year scaling and efficiency (June to August 2025)
June 2025 stands out as the point where the restructured account and bidding approach started producing scale: 23 leads, 21 MQLs, and 15 opportunities at $11,319 spend (cost per opp $755). In July and August, we maintained opportunity volume while improving cost control.

Month-over-month KPI snapshot (July 2025 vs August 2025)

Primary conversion efficiency (demo requests)
On the “get demo” conversion, August 2025 delivered 31 conversions at $310.93 cost per conversion. The September 2025 view available in the account was partial: spend $745.99 with 1 conversion at $745.99 cost per conversion, which is not representative of a full month.

Most importantly, the client began seeing sales attributable to paid search. Due to the B2B decision cycle (about 3 months on average), the account required time to collect data and to allow pipeline to mature. In our case, the learning and data collection period took about 4 months, after which the account performance stabilized and improved.

What changed and why it worked

In this project, the “win” was not a single tactic - it was aligning tracking, structure, and bidding to the reality of enterprise search behavior. Here are the most important cause-and-effect links.

• Problem: all form fills were treated as equal conversions.
  Action: we separated primary (demo) and secondary conversions.
  Why it worked: bidding and optimization stopped chasing low-intent actions and started prioritizing signals closer to revenue.
• Problem: messy campaign structure with weak keyword coverage and no negative keywords.
  Action: we rebuilt search campaigns and segmented them by software direction/module.
  Why it worked: we gained control over budget allocation, query matching, and ad relevance per module, which improved efficiency in a high-CPC auction.
• Problem: competitors captured high-intent brand traffic.
  Action: we launched brand protection and supported escalation to Google.
  Why it worked: brand searches are typically the cheapest and highest-intent clicks. Defending them reduced leakage and improved the quality mix of incoming leads.
• Problem: long learning curve in a niche with expensive clicks and a 3-month sales cycle.
  Action: manual bidding first to build data, then automated bidding once the account had enough conversion volume.
  Why it worked: automation performed better once fed cleaner, higher-intent conversion signals and sufficient volume to learn.
• Problem: budget dilution from existing customers and misaligned demographics.
  Action: excluded known customers and refined demographic targeting (including excluding female audiences for selected directions and focusing on ages 25-54).
  Why it worked: reduced avoidable spend and concentrated budget on the most productive segments.

Lessons and next steps

This case reinforces a pattern we see in complex B2B search: when the product is specialized and the auction is expensive, performance hinges on the quality of the optimization signal and the discipline of the account structure. Quick “tweaks” rarely overcome foundational tracking and segmentation issues.

What we would keep doing:

• Maintain a strict conversion hierarchy so optimization stays tied to business intent (demo-first), while still tracking secondary actions for insight.
• Continue search-term mining and negative keyword expansion to protect CPC and CPL in a high-competition auction.
• Keep module-level budgeting and reporting so scale decisions are made on pipeline contribution, not just lead volume.
• Retain selective competitor conquest only where it proves it can produce conversions without breaking efficiency targets.

What we would improve next:

• Strengthen measurement alignment between Google Ads and CRM where possible (accounting for attribution differences) so opportunity and closed-won reporting becomes more consistent for decision-making.
• Expand and refine ad messaging by module with ongoing tests tied to observed search intent, using the client’s end-user language as the baseline.
• Plan optimization and reporting around the 3-month decision cycle, evaluating changes with enough time for pipeline to mature rather than reacting to short-term noise.

The central takeaway: even in a niche with expensive clicks and a long sales cycle, a systematic approach - conversion prioritization, structured campaigns, and a controlled shift to automation - can turn search from “spend with no sales” into a sustainable source of new-customer pipeline.