For years, the D2C party line has been a singular, unshakeable doctrine: trust the machine. When Meta introduced Campaign Budget Optimisation, media buyers willingly handed over the keys to the allocation engine. The logic was sound - let the algorithm dynamically shift capital toward the lowest-cost conversion opportunities in real time. For scaling seven- and eight-figure brands, this automated efficiency was a primary driver of volume.

However, this total reliance on automation created an operational paradox. The algorithm prioritises short-term historical data, meaning a single creative or ad set capturing early momentum often monopolises 90% of a campaign's total budget. Meanwhile, high-margin product lines, strategic creative iterations, and critical alpha testing variants are completely starved of impressions. Growth teams are left with a frustrating choice: accept the algorithm's narrow focus or fragment their account architecture by building isolated testing campaigns from scratch - a move that can disrupt data aggregation and spike acquisition costs.

Meta is quietly addressing this limitation by rolling out new budget control features at both the ad set and ad level. Currently in beta testing across selected ad accounts, these features signal a major shift in media buying strategy. Advertisers no longer have to choose between full algorithmic automation and absolute manual control; instead, they can apply precise strategic guardrails directly within a consolidated infrastructure.

Technical Architecture: Ad Set and Ad-Level Controls

The update introduced two distinct levers within the Meta Ads Manager interface, designed to introduce structure to Campaign Budget Optimisation setups without breaking the underlying liquidity of the auction.

1. Daily Average Ad Set Spending Limits

Historically, forcing distribution inside a campaign required migrating back to Ad Set Budget Optimisation, which limited Meta's ability to adjust to real-time traffic fluctuations. The new update has introduced an "Ad Set Spending Limits" function directly within the ad set settings of an active campaign.

Advertisers can now configure two precise parameters:

• Minimum Spend Limits: Instructs the algorithm to allocate a guaranteed baseline of capital to an ad set, calculated on an average daily basis.
• Maximum Spend Limits: Enforces a rigid ceiling to prevent a single high-volume ad set from draining total campaign capital, allowing remaining assets a clear path to delivery.

Because these caps operate on an average daily basis, Meta retains the flexibility to scale spend up or down depending on real-time auction opportunities, balancing out across a seven-day window.

2. Ad-Level Budget Push

The more radical shift occurs at the creative level. In selected accounts, Meta has introduced a dedicated prompt within the creative setup pane: "What percentage of daily budget should go to specific ads?"

This feature allows media buyers to bypass Meta's habit of funding only established creative winners. By defining a specific percentage allocation and an explicit runtime duration, brands can guarantee that new creative concepts receive the initial injection of impressions needed to gather statistically significant performance data.

The Kandidly View: The Era of Algorithmic Guardrails

At Kandidly, our media buying philosophy centres on driving incremental growth, not just chasing platform-reported metrics. While the wider industry often swings between blind faith in automation and rigid manual structures, we believe the highest-performing accounts combine machine learning with human strategy.

These updates do not mean abandoning the efficiency of CBO. Instead, they allow growth teams to establish clear guardrails. Implementing minimum spend limits allows a brand to protect high-margin product categories, even if the algorithm prefers to push budget toward cheaper, lower-margin items. Similarly, utilising the ad-level budget push ensures a systematic approach to creative engineering, forcing the algorithm to test new concepts rather than defaulting to old assets. True scale requires pushing boundaries, and these features provide the exact tools needed to manage that risk safely.

Strategic Implementation Matrix

To scale performance without causing data fragmentation, brands must learn when to deploy these controls rather than defaulting to standard campaign setups.

Actionable Execution Guide

To implement these features effectively within your media buying structure, consider the following three tactical approaches:

Establish a Creative Testing Framework

In some cases, instead of running a separate testing campaign that requires moving winners over and resetting the learning phase, introducing your new concepts directly into your mature ad set works incredibly well. By deploying the new creative within an established, high-performing environment, the asset immediately benefits from the historical pixel data and conversion signals already aggregated within that specific ad set architecture. You can then use the ad-level budget push feature to allocate 10% to 15% of your total daily campaign budget specifically to these new creative concepts. Setting the duration for five to seven days gives the algorithm the necessary runway to gather performance data and find its footing, without risking the baseline stability of your proven assets.

Maximise Lifetime Value via Multi-Product Scaling

If your data shows that certain items lead to a higher LTV, don’t let Meta's algorithm favour cheaper, low-consideration products. Create dedicated ad sets for your high-LTV products within your main campaign, and apply a minimum daily spend limit. This ensures a consistent flow of high-value customers while allowing the algorithm to use the rest of the campaign budget on high-volume products.

Protect Your Account from Creative Fatigue

Monitor your ad accounts for signs of creative fatigue, such as rising cost-per-acquisition or declining conversion volume, which often happen when a single asset takes up more than 70% of a campaign's spend. Use an ad set maximum spend limit to cap the exposure of that dominant asset. This forces the platform to diversify its delivery across your secondary creative assets, building a more resilient account structure.