Taylor Holiday and Andrew Faris Debate Meta Cost Controls (We Ran the Numbers)

If you are running paid media on Meta and relying on cost controls to hit your efficiency targets, the question of which cost control to use is not academic. Meta cost controls are the primary lever performance teams use to manage cost-per-acquisition and return on ad spend, and the choice between them has real dollar consequences at scale. CTC reviewed $1.5 billion in Meta spend and ran a formal analysis on $200 million across 253 accounts from March 2025 through July 2026 to find out how each option actually performs against the target you set. The results changed CTC's default strategy. Here is what the data shows.

What CTC Measured and Why It Matters

Most discussions about Meta cost controls are built on intuition, client anecdotes, or small-sample tests. CTC wanted something more rigorous. The study measured one core question for each cost control type: when you set a target, how close does Meta actually deliver to it at scale?

The dataset covers 253 accounts managed by CTC from March 2025 through July 2026. The formal analysis focused on $200 million of that spend, drawn from the larger $1.5 billion CTC manages across all its clients. Three cost control types were evaluated: min ROAS (highest value optimization), cost per result goal (cost cap), and bid cap.

Each was measured by delivery ratio, which is the ratio of actual outcome to the target you set. A delivery ratio of 100% means the algorithm hit your target exactly. Below 100% means it was more efficient than asked. Above 100% means it overshot.

Min ROAS Delivered 96.5% of Target — The Most Accurate Control in the Study

The most striking finding in the dataset is how accurately min ROAS (highest value optimization) delivered to its target. Across the full sample, min ROAS accounts showed a delivery ratio of 96.5%, meaning the actual ROAS came in at 96.5% of the target on a portfolio-weighted basis. For a cost control mechanism, that is a remarkably tight result.

"Min ROAS delivered 96.5% to target across the full dataset. It is the most accurate cost control CTC has tested, and the AOV signal is baked into the optimization so you do not have to manually work through SKU-level bid math."

One of the practical advantages of min ROAS is that average order value is embedded in the optimization signal. When you set a ROAS target, Meta uses your actual transaction values to determine which impressions to bid on. This means you do not have to manually calculate a per-conversion cost target for each SKU in your catalog. For brands with complex product mixes, this distinction matters significantly.

There is an important caveat. The 96.5% accuracy is an aggregate result. At the individual account level, the variance is substantial. In this study, 67% of min ROAS accounts were more than 5% below their ROAS target on an individual basis. The aggregate accuracy is real, but it reflects portfolio-level behavior, not a guarantee for any single account. If you are managing a single brand on this control type, plan for that variance.

Cost Per Result Goal Overshoots by 40% — Consistently

The cost per result goal findings were the most operationally significant in the study. Across the sample, this cost control delivered at 140% of target. On an account running an average bid of $78, the actual cost per result came in around $108. The overshoot was not occasional. It was consistent across the dataset.

The core reason is SKU complexity. When you set a cost per result goal, you are setting a single dollar number as your target. But if your catalog includes products at multiple price points, there is no easy mechanism to account for that variation when choosing your bid. The algorithm does not adjust for order value mix. Set too low a bid and you starve delivery. Set too high a bid and you consistently overpay. The study found most accounts landed in overpay territory.

"Cost per result goal delivered 140% of target on average. An account bidding $78 was paying $108. The overshoot is not a fluke — it is a structural feature of how this control interacts with product catalog complexity."

This finding prompted CTC to reconsider cost per result goal as a default recommendation. The degree of overshoot, replicated across hundreds of accounts, makes it difficult to use as a precision targeting tool without significant manual intervention and account-by-account calibration.

Bid Cap Delivered 123% of Target and Is Now CTC's Default

Bid cap was evaluated on a smaller $3 million sample within the dataset, which is worth noting as a limitation. Within that sample, bid cap delivered at 123% of target. It overshot more than min ROAS but meaningfully less than cost per result goal, and it does so in a way that is more controllable. Because bid cap applies a ceiling to what Meta bids at auction, you retain direct control over the bid ceiling itself. The overshoot comes from Meta's ability to bid up to your cap, not beyond it, but auction dynamics mean you will often deliver above the exact target.

Based on this study, CTC moved bid cap to its default cost control, replacing cost per result goal. The key reasons are the smaller overshoot relative to cost per result goal and the cleaner bid ceiling logic. It is a meaningful shift from CTC's prior position, and the research drove it directly.

Stop Loss Rules Have No Discernible Impact on Performance

Performance teams commonly implement stop loss rules: if an individual ad exceeds a certain cost threshold or underperforms on a key metric, turn it off to stop the bleed. The logic is intuitive. The data does not support the impact.

The CTC dataset found that stop loss rules for individual ads have no discernible impact on account performance. Across the accounts evaluated, applying or not applying stop loss rules at the ad level did not produce a measurable difference in account-level results. The algorithm's budget reallocation behavior appears to absorb what individual ad shutoffs were supposed to prevent.

This finding has practical implications for how performance teams spend their time. If a significant portion of the daily workflow is dedicated to monitoring and shutting off individual ads based on cost thresholds, that time is likely not producing a measurable return. It is worth evaluating whether that workflow belongs in the operating model at all.

How to Use This Research for Your Brand

The practical takeaways depend on your account's current configuration.

If you are using cost per result goal as your primary cost control, the data is a strong signal to consider switching. The consistent 40% overshoot means you are likely paying more per conversion than your target, and the error compounds with SKU complexity.

If you are evaluating min ROAS, the aggregate accuracy is real, but prepare for individual account variance. Plan your targets with the understanding that 67% of accounts in this study were more than 5% below target at the individual level. The aggregate is not your account.

If you want the most controllable structure with a lower overshoot relative to cost cap, bid cap is now CTC's recommended starting point. The $3 million sample is smaller than the other datasets in this study, so treat it as directional. It is where CTC's own accounts have moved, and the early results support the switch.

Across all three, stop loss rules at the ad level do not appear to be worth the operational overhead the data suggests many teams allocate to them.

Frequently Asked Questions

See What This Looks Like for Your Accounts

CTC manages over $1.5 billion in annual Meta spend across 7-figure, 8-figure, and 9-figure ecommerce brands. If you want to know how your cost control configuration compares to the patterns in this study, or whether a switch to bid cap makes sense for your specific account structure, we can walk through it with you.

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