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Strategy12 min read5 chapters

Budget Allocation Across Meta, Google, and TikTok

A framework for distributing spend based on incremental ROAS, creative fatigue, and audience overlap.

Shubham Raghav

Chapter 1The Static Budget Trap

Most brands set their channel budget at the beginning of the quarter and don't touch it until the next planning cycle. “60% Meta, 30% Google, 10% TikTok” gets written on a slide deck and becomes gospel for three months. This is exactly wrong.

The optimal allocation between channels changes weekly, sometimes daily. Creative fatigue on Meta can shift your best marginal dollar to Google. A competitor pulling TikTok spend can open up cheap inventory. Seasonal demand patterns hit different channels at different times. A static budget ignores all of this, leaving meaningful efficiency on the table.

The best allocation today isn't the best allocation next week. Channels experience fatigue, saturation, competitive shifts, and seasonal demand changes. Static quarterly budgets are a guaranteed way to leave money on the table.

Chapter 2Understanding Diminishing Returns

Every advertising channel follows an S-curve of diminishing returns. The first chunk of spend on Meta can generate strong ROAS. The next chunk generates less. The chunk after that less still. At some point, the marginal dollar on Meta generates less return than the marginal dollar on Google, and that's where you should shift spend.

The problem is that the point of diminishing returns is different for every channel, changes over time, and isn't visible in platform dashboards. Platforms show you average ROAS, not marginal ROAS. Average ROAS can look great even when your marginal dollar is generating negative returns, because past efficient spend masks current inefficiency.

Interactive

Budget allocation simulator

Adjust allocation to see how diminishing returns affect blended iROAS.

Meta Ads$50K → 2.4x iROAS
Google Ads$35K → 2.8x iROAS
TikTok Ads (remainder)$15K → 2.6x iROAS

Blended Incremental ROAS

2.6x

Based on $100K total monthly spend

Simplified model showing diminishing returns. Actual curves vary by vertical, audience, and creative quality.

Marginal vs Average ROAS

This is the single most important distinction in budget allocation. Average ROAS tells you how your total spend performed. Marginal ROAS tells you whether your next dollar should go to this channel or another. A channel with 3.5x average ROAS but 1.2x marginal ROAS is being overspent. A channel with 2.0x average ROAS but 3.8x marginal ROAS is being underspent. Always think in terms of the marginal dollar.

Chapter 3The Allocation Framework

Here's the framework Sam uses to determine optimal allocation. It runs continuously, but you can apply the same logic manually on a weekly basis:

  1. Start with corrected data

    Use Parker's correction factors, not platform-reported ROAS. If you're allocating based on inflated numbers, you're optimizing for the wrong thing.

  2. Calculate marginal ROAS per channel

    Look at the incremental ROAS of your most recent spend increase (or decrease) on each channel. This tells you the actual return of your marginal dollar.

  3. Equalize marginal returns

    The optimal allocation is where the marginal iROAS is equal across all channels. If Meta's marginal iROAS is 2.8x and Google's is 1.9x, shift money from Google to Meta until they equalize.

  4. Apply constraints

    Account for minimum viable spend (you need enough on each channel to clear the learning phase), creative availability, and strategic considerations (brand presence, new channel testing).

  5. Monitor and rebalance weekly

    Creative fatigue, competitive shifts, and seasonal changes mean the optimal point shifts constantly. Check marginal returns weekly and rebalance when the gap between channels exceeds 15%.

Chapter 4Cross-Channel Effects

Channels don't operate in isolation. When you increase Meta prospecting spend, branded search volume on Google goes up. When you pause TikTok, you lose the awareness effect that was feeding your retargeting funnel. These cross-channel effects are invisible in single-channel analysis but critically important for allocation.

Meta → Google Branded

Branded search lift

Meta prospecting drives brand awareness. Increasing Meta spend lifts branded Google search volume. If you attribute those conversions to Google, you undervalue Meta.

TikTok → Full Funnel

Halo effect

TikTok awareness creates demand that converts across all channels. Pausing TikTok often causes a delayed drop in total conversions that shows up in Meta and Google, not TikTok.

Audience Overlap

Audience overlap

Meta and TikTok audiences overlap meaningfully in younger demographics. Increasing spend on both creates frequency fatigue faster than either alone. Account for overlap when setting combined budgets.

Any allocation model that treats channels independently will misallocate spend. The true value of Meta includes the branded search it drives. The true value of TikTok includes the full-funnel demand it creates. Sam's simulations account for these interaction effects, simple spreadsheet models can't.

Chapter 5The Rebalancing Decision

The hard part of allocation is not the arithmetic, it is knowing when a difference between channels is real enough to move money against. Most teams either never rebalance or rebalance on noise. These are the rules that separate the two.

Move budget when all three hold

  • Marginal return, not average return, differs between the two channels. Average ROAS favours whichever channel you have already optimised, which is why it points the wrong way.

  • The gap has held for at least two reporting periods. One week of divergence is usually creative rotation or a competitor's flight, not a change in the curve.

  • The receiving channel has headroom. If it is already past its inflection point, moving spend into it buys volume at a worse rate than the channel you took it from.

Do not move budget when any of these is true

  • The difference comes from platform-reported ROAS alone. That number cannot settle a cross-channel question, because each platform is scoring its own work.

  • You are inside a learning phase you would reset. The cost of restarting optimisation frequently exceeds the gain you are chasing.

  • The change is smaller than your measurement error. If you cannot distinguish the result from noise afterwards, you have made an untestable decision.

  • It is the last week of the month or quarter. Pacing pressure is not evidence about the curve.

Dynamic allocation that responds to creative fatigue, competitive shifts, and diminishing returns in real time. Continuous optimization based on where your marginal dollar works hardest.

Written by Shubham Raghav, Founder & CEO, Cresva. Questions? Email us.