Budget Efficiency Strategy: How to Scale Ad Spend Without Scaling Waste
22-01-20264 min readClick Fortify Team
Key Takeaways
- The Efficiency-Scale Paradox: Scaling ad spend inevitably lowers ROAS due to the Advertising Elasticity of Demand (AED), typically between 0.11 and 0.22.
- Marginal ROAS (mROAS): Stop scaling based on Average ROAS. Use mROAS to find the exact dollar where cost exceeds value.
- The Solution: Transition from pixel-based attribution to Marketing Mix Modeling (MMM) and Geo-Lift Testing to measure true incrementality.
The Economic Theory of Advertising Scale
Scaling is not a linear function of budget; it is an economic problem governed by the elasticity of demand.
Advertising Elasticity of Demand (AED)
AED measures how responsive sales are to a change in ad spend. The formula is:
ε_A = % ΔQ / % ΔA
ε_A: Advertising elasticity coefficient.- Reality Check: Empirical studies show
ε_Ais typically 0.11 to 0.22. This means a 100% increase in budget often yields only an 11-22% increase in sales.
The Dorfman-Steiner Theorem
This theorem defines the theoretical limit of efficient spend:
A / S = ε_A / |ε_P|
- Strategic Implication: If you operatre in a commoditized market (High Price Elasticity
|ε_P|) but ads don't easily sway demand (Low Ad Elasticityε_A), your optimal ad budget should be minimal.
Comparitive Analysis of Response Curves
| Curve Model | Market Context | Optimal Scaling Strategy | Primary Risk |
|---|---|---|---|
| **Concave (Diminishing)** | Established markets, retargeting. | Continuous optimization of marginal returns. | Over-saturation; engaging users without incremental gain. |
| **S-Shaped (Threshold)** | New market entry, high noise. | 'Pulse' spending to breach visibility thresholds. | Underspending; small budgets fail to reach the inflection point. |
The Measurement Crisis: Why Standard Metrics Fail
As budgets scale, "Average ROAS" becomes a vanity metric.
Metric Hierarchy for Scaling
| Metric | Level of Analysis | Primary Use Case | Scaling Signal |
|---|---|---|---|
| **ROAS** | Tactical / Ad Set | Day-to-day optimization of creative. | Drop indicates local saturation or creative fatigue. |
| **mROAS** | Financial / Budget | Deciding to increase/decrease total budget. | Drop below break-even signals the **absolute limit** of profitable scale. |
| **MER** | Strategic / Executive | Assessing business health. | Stable MER despite dropping ROAS indicates successful Top-of-Funnel expansion. |
Advanced Modeling: Marketing Mix Modeling (MMM)
In a privacy-first world, MMM allows you to measure impact without tracking users.
- Robyn (Meta): Best for teams with R expertise; excellent for adstock and saturation modeling.
- LightweightMMM (Google): Python-based; uses Bayesian inference and allows integration of "priors" from Geo-Lift tests.
Triangulation Strategy: Use MTA for daily tactics, Incrementality for quarterly calibration, and MMM for annual planning.
Platform-Specific Scaling Protocols
Meta: The Liquidity Doctrine
- The Rule: Ad sets need ~50 conversions/week to exit the learning phase.
- The Fix: Use valid daily budgets. If CPA is $50, Min Daily Budget = (50 * 50) / 7 = $357/day. Spending less guarantees inefficiency.
Google Ads: PMax Scaling
- Brand Exclusions: Force PMax to find net-new customers by excluding your brand terms.
- New Customer Acquisition: Use the goal setting to bid higher for new users, preventing the algorithm from harvesting existing demand.
LinkedIn Ads: The Data Significance Barrier
- Audience Size: Target 30,000 - 100,000 for cold layer scaling.
- Automated Rules: Implement "Pause if Spend > 2x CPA with 0 conversions" to prevent waste in high-CPM auctions.
Conclusion
Scaling ad spend without scaling waste requires a shift from deterministic (pixel-based) to probabilistic (MMM/Lift) measurement. The brands that win at scale are not those that spend the most, but those that measure the Marginal ROAS of every incremental dollar.
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