11% More Leads for Zero Extra Spend

The Crowded Market Problem

A B2B cybersecurity company operates in one of the most competitive segments in enterprise technology. As their VP of Marketing described it, they are in “a crowded market with 200 companies saying exactly the same thing.”

When every competitor has similar messaging, similar positioning, and similar claims, the marketing team’s job shifts from differentiation through narrative to optimisation through execution. The question is not just what to say but where to say it, and how much to invest in each channel to generate the maximum number of qualified leads.

Before measurement, the company was generating 685 leads per week at an average cost per lead of $6. Solid numbers by any B2B standard. But the marketing team suspected there was inefficiency hiding in the channel mix. They just could not see where.

Finding the Hidden Inefficiency

When the company modelled the relationship between spend and lead generation across every channel in their mix, the results were immediate and actionable.

PMAX Was 10x More Efficient Than Search

The single biggest finding was the performance gap between Google’s Performance Max (PMAX) campaigns and traditional Search campaigns.

At equivalent spend levels of approximately $1,000 per week, Search generated around 30 leads. PMAX generated 300 leads. That is not a marginal difference. It is an order-of-magnitude gap in efficiency.

Search had hit a saturation point. Beyond roughly $1,000 in weekly spend, additional dollars produced diminishing returns. The cost curve flattened while the lead curve barely moved. The team had been spending well beyond the point of diminishing returns on Search without realising it.

PMAX, by contrast, was operating below its efficient frontier. There was room to increase spend and still generate leads at an attractive cost. The algorithm’s ability to find prospects across Google’s full inventory of surfaces, Search, Display, YouTube, Discover, and Gmail, was delivering volume that single-channel campaigns could not match.

Display Was Dramatically Overfunded

Display advertising was consuming $900 per week, a significant portion of the total budget. The model showed that Display’s contribution to lead generation was minimal relative to its cost. The recommendation was to cut Display from $900 to $160 per week, a reduction of more than 80%.

This is a finding that would be difficult to action without rigorous measurement. Display campaigns produce impressions, clicks, and engagement metrics that look healthy in a platform dashboard. But when measured against actual lead generation, the channel was consuming budget that could be working dramatically harder elsewhere.

Virtual Events Outperformed Trade Shows

In the cybersecurity industry, trade shows and conferences have long been considered essential for lead generation. The industry is relationship-driven, and face-to-face interaction at events like RSA Conference or Black Hat is treated as a cornerstone of the go-to-market strategy.

The model challenged this assumption. Virtual events, webinars, and online industry sessions were generating more leads per dollar than traditional trade shows. This was counterintuitive for a team steeped in the convention that physical presence drives pipeline in security.

The finding does not mean trade shows have no value. They serve branding, relationship-building, and deal-acceleration functions that are difficult to capture in a lead-generation model. But for pure top-of-funnel volume, the data showed that virtual events were the more efficient investment.

The Reallocation

Based on the model’s findings, the team reallocated budget across channels without increasing total spend by a single dollar.

The moves were straightforward:

• Reduce Search spend beyond the saturation point
• Cut Display from $900/week to $160/week
• Increase PMAX investment to capture more of its efficient capacity
• Shift event budget toward virtual formats

The result: weekly lead generation moved from 685 to 760 leads. An 11% increase in volume for zero incremental spend.

The cost per lead dropped correspondingly. More leads, same budget, lower unit cost. This is what pure optimisation looks like.

Why This Was Invisible Before Measurement

The natural question is: why did a capable marketing team miss a 10x efficiency gap between two Google channels?

The answer lies in how B2B marketing teams typically evaluate performance.

Platform Silos Hide Cross-Channel Truths

Each channel is usually managed and reported on independently. The Search team optimises Search. The Display team optimises Display. PMAX runs semi-autonomously. Nobody is looking across all channels simultaneously to ask where the next dollar should go.

Platform-level metrics, quality score, click-through rate, cost per click, can all look healthy within a channel that is dramatically underperforming relative to alternatives. A Search campaign with a strong quality score and reasonable CPC might still be a worse investment than a PMAX campaign with messier metrics but superior lead generation.

Saturation Is Invisible Without Modelling

You cannot see a saturation curve in a Google Ads dashboard. The dashboard shows you what happened at the spend level you chose. It does not show you what would happen if you spent half as much or twice as much. Identifying the point at which incremental spend stops generating incremental leads requires response curve modelling, which is exactly what marketing mix modelling provides.

Institutional Inertia

Budget allocation tends to be sticky. Last quarter’s mix becomes this quarter’s starting point. Channels that received funding historically continue to receive funding. The burden of proof falls on anyone who wants to change the allocation rather than on anyone who wants to maintain it. Without data that quantifies the opportunity cost of the current mix, reallocation conversations stall.

The Reaction in the Room

When the findings were presented, the VP of Marketing said, “This is great. There’s a keeper right here,” referring to the PMAX finding.

The CRO’s response was more telling: “Gong doesn’t do this. Salesforce doesn’t do this. Everyone gives you a piece of the data… this is the holy grail.”

That reaction captures something important. B2B companies typically have sophisticated tech stacks, CRM, revenue intelligence, attribution platforms, marketing automation. Each tool provides a slice of the picture. None of them answer the fundamental question: across all channels and activities, where should the next dollar go?

The tools that track conversations, manage pipelines, and attribute touches within a journey are valuable. But they are not designed to model diminishing returns across channels, identify saturation points, or optimise budget allocation at the portfolio level.

What B2B Marketers Can Learn

Model the Full Mix, Not Just the Funnel

Attribution models tell you which touches influenced a conversion. They do not tell you whether the budget behind those touches was optimally allocated. Both perspectives are necessary. Attribution without allocation modelling leaves money on the table.

Test Your Assumptions About Channels

The cybersecurity company had no reason to believe PMAX would outperform Search by 10x. Their assumption, reinforced by years of Search-first strategy, was that Search was the primary digital lead driver. Without testing that assumption against data, they would have continued overinvesting in a saturated channel.

Look for Saturation Before Adding Budget

When lead generation plateaus, the instinct is to request more budget. Often, the answer is not more money but better allocation of existing money. Eleven percent more leads for zero extra spend is a finding that should prompt every marketing team to ask: are we spending past the point of diminishing returns on any channel?

The Takeaway

Budget optimisation is not about spending less. It is about spending in the right places. An 11% increase in leads from pure reallocation demonstrates that many marketing teams are operating with meaningful hidden inefficiency in their channel mix. The gap between where you are spending and where you should be spending is only visible when you measure all channels together, on the same terms, with the same success metric. When you do, the opportunities tend to be larger than anyone expected.