Breadcrumbs

Scenario Planner Methodology

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Scenario Planner in Alli answers one core question: where should the next dollar go to drive the most impact, without sacrificing long-term growth?

It does this through a structured pipeline that turns raw data into continuously updated budget recommendations.

Stage 1: Inputs — Performance + Brand,

The system starts with two inputs:

  • Brand Equity: A composite signal built from Brand Salience, emotional resonance, economic power, and Brand Durability.
    This is not directional or qualitative. It’s a quantified input that the model uses alongside revenue.

  • Performance Data: Two years of daily spend and revenue data.
    This captures full seasonal cycles and prevents short-term spikes from distorting decisions.

Why this matters: Most systems optimize only for revenue. This ensures we’re balancing immediate performance with future demand.

Stage 2: Pre-Processing — Normalizing Before Modeling

Before modeling, both inputs are conditioned:

  • Brand signals are consolidated into a single, usable metric

  • External factors (seasonality, holidays, macro trends) are separated from media impact

Why this matters: The model focuses on what media actually drives, not what would have happened anyway.

Stage 3: Modeling — Capturing Real Incremental Impact

The core engine combines two approaches:

  • Hierarchical channel and platform modeling
    Shares learnings across channels, accounts for carryover effects and diminishing returns.

  • Two-stage modeling (Brand + Revenue)
    Keeps long-term brand signals from being overridden by short-term revenue performance

Why this matters: Produces stable, realistic estimates of incremental impact across the full media mix.

Stage 4: Optimization — Turning Insight into Decisions

The optimization layer determines how to allocate budget by:

  • Evaluating marginal returns across channels

  • Applying real-world constraints (caps, floors, locked spend)

Constraints are applied here, not in the model, so they guide decisions without biasing the underlying math.

Why this matters: Outputs are actionable, not theoretical.

Output: Actionable, Continuously Updated Recommendations

The result is a recommended budget allocation across channels and platforms, refreshed weekly as new data comes in.

Teams use it to:

  • Reallocate budget with confidence

  • Test scenarios before committing spend

  • Build plans grounded in data, not intuition

Bottom Line

Scenario Planner in Alli connects brand and performance into a single decision system.

It continuously updates how budget should be allocated to:

  • Maximize short-term efficiency

  • Protect long-term growth

Result: Faster, clearer, and more defensible planning decisions.