The Demand-Led Growth Playbook
This post presents an eight-step framework for implementing demand-led growth in paid media strategies. You’ll learn how to align budgeting, measurement, team roles and AI-driven processes to scale performance beyond traditional campaign thinking.
What is demand-led growth?
Demand-led growth shifts away from rigid budgets and fixed bidding-based thinking. Instead it aligns media spend and strategy with actual market demand signals, enabling fluid investment, responsive pacing and stronger commercial outcomes. According to the blog, it’s the “next wave” of paid-media campaigns.
Step 1: Audit budgeting behaviour, not just settings
It’s not enough to simply set budgets and caps. You need to map how budgets are actually hitting constraints, track manual interventions and test what would happen if those caps were removed. Behaviour around budgeting often limits performance more than technical settings.
Step 2: Align Finance and Trading teams as part of your approach
Growth requires everyone to speak the same commercial language — ROAS, payback, margin thresholds — and for finance and trading teams to see media as a revenue engine rather than cost centre. Shared frameworks and clear budget guardrails are key.
Step 3: Assess structural and signal-based constraints
Beyond budgets, constraints come from weak signals (e.g., poor audience match-rates) and campaign design (e.g., feed issues, asset coverage). Diagnosing these helps unlock scaling potential.
Step 4: Uncap budgets in PMax campaigns
For campaigns like Google Performance Max to truly deliver, budgets must adapt to demand rather than remain fixed. The blog recommends removing tight caps or providing generous buffers so the system can capture opportunity.
Step 5: Set demand-led KPIs and benchmarks
ROAS is a guardrail, not the full growth strategy. You should employ metrics such as incrementality, cost of demand, total addressable market (TAM) coverage and spend elasticity to guide scaling.
Step 6: Deploy incrementality testing frameworks
To justify investment and understand what’s genuinely new and additive, implement testing programmes (geo-splits, media mix modelling and causal impact methodologies) to measure full-funnel effects.
Step 7: Empower your team as AI performance leads
The role of human teams evolves. Their job becomes more about strategy, scenario-planning and guiding AI (rather than continuously adjusting bids). Clear roles around forecast, planning and signal interpretation are critical.
Step 8: Review and refine monthly
Constant tweaking undermines AI. Instead, set monthly strategic reviews that assess what constraints have changed, where marginal returns are shifting and feed insights back into planning for the next cycle.