Channel Breadth vs. Commitment: Which Strategy Wins?

Many marketing teams default to a single budget allocation playbook: spread spending across as many channels as possible, assuming the first dollar always delivers the highest return. This strategy relies on a concave response curve, where each additional dollar yields diminishing returns. But this assumption is flawed. Not every channel behaves this way, and using the wrong allocation strategy can lead to costly mistakes.

The critical distinction lies in the shape of a channel’s response curve: is it C-shaped (concave) or S-shaped (sigmoid)? This single factor determines whether a “test small, scale winners” approach will succeed or fail. A C-shaped curve shows diminishing returns from the very first dollar, making small tests reliable indicators of performance. An S-shaped curve, however, features a warm-up region where early spending is the least efficient. The first dollars are wasted until the channel reaches an inflection point, after which returns accelerate dramatically.

This difference is not academic. It has direct implications for channel testing, measurement, and budget allocation. Google’s shift toward S-shaped campaign types, highlighted at Google Marketing Live, makes understanding this distinction more urgent than ever.

The Trap of the Small Test

Consider a channel with an S-shaped response curve. A typical $10,000 test might show an average CPA of $132, well above a $50 goal. A standard analysis would conclude the channel is unviable and should be killed. This verdict is wrong. At a spend level of $20,000 to $25,000, the same channel might deliver an average CPA of $32 to $40, with a marginal cost of just $18. The small test fell entirely within the warm-up region, showing the channel’s worst performance, not its best. In a C-shaped channel, a small test reveals peak efficiency. In an S-shaped channel, it reveals the opposite.

Two Allocation Strategies: Go Wide vs. Go Deep

The correct approach depends on the curve shape.

For C-shaped channels, the optimization is straightforward: go wide. The first dollar is the most productive, so spreading budget across many channels is efficient. The equimarginal rule applies cleanly: allocate until marginal CPA equals your goal, then reallocate continuously.

For S-shaped channels, the optimization is non-convex. A small allocation can be worse than zero, because you are stuck in the inefficient warm-up region. The decision becomes binary: commit a block of budget past the inflection point, or don’t fund the channel at all. There is a real minimum viable budget, often above normal test levels. You cannot sprinkle an S-curve and expect efficiency, and you cannot evaluate one on an underfunded test. The instruction is to commit a block, not to sprinkle.

Once past the inflection, an S-curve behaves like a C-curve, and the equimarginal rule applies. The S-specific challenge is only about the launch phase. The real risk is cutting a channel hard, which can cause a cliff-like drop in performance rather than a gradual decline.

Which Channels Are Which?

The historical default was concave, but platform shifts have made S-curves more common. Key examples include:

• AI Max (formerly Broad Match): This channel is migrating from C toward S. Its broad matching requires conversion volume to learn, making it inefficient below a data threshold. The mixed results, with Google reporting 14% more conversions while independent tests show 84% of advertisers seeing neutral or negative results, likely reflect accounts that didn’t provide enough volume to clear the learning region.
• Performance Max (PMax): This is a trap because its curve is a composite. It blends a harvesting layer (branded, retargeting) with a prospecting layer (keywordless expansion). The harvesting layer is a cheap C, while the prospecting layer is an S underneath. Blended, the early efficiency looks great, but it hides the prospecting warm-up. You cannot analyze PMax until you split these layers.The throughline is clear: rules-based auctions capture the best inventory first, yielding concavity. Machine-learning systems must be fed before they are efficient, introducing a threshold. Harvesting existing demand is concave and mostly non-incremental, while creating new demand is the S-shaped part where real growth and real warm-up costs reside.Two Unsettled CautionsFirst, separating a true S-curve from a concave curve with a high half-saturation point is difficult. A concave model can fit S-shaped data well enough to hide the inflection. This applies to your dashboards as much as to academic studies.Second, the learning phase may be a one-time fixed cost, not a permanent feature. If transient, the channel may behave concavely once trained. The truth is likely a mix: a one-time training cost plus an ongoing minimum-volume requirement. Treat every shape call as provisional and re-check it.The Practical TakeawayThe marginal-return rule of equalizing CPAs is still correct, but the curve shape tells you how to get there. On C-shaped channels, spread your budget. On S-shaped channels, commit a block past the inflection before evaluating.Harvesting channels (branded, retargeting, non-brand search) are your C-curves: fund the first dollars, then cap them early. Prospecting channels (Meta, YouTube, LinkedIn, the expansion half of PMax) are your S-curves and your only real source of incremental growth: commit past the warm-up or don’t start. Judge them on incremental lift, not attributed CPA, or you’ll kill the thing that was working.Classic search rewards going wide. PMax, AI Max, and Meta prospecting reward going deep on fewer bets. Run an S-curve like a C-curve, and you’ll starve it, misread the results, and lose a channel that could have been one of your best.