budget-allocator

name	budget-allocator
description	Launch budget optimization using portfolio theory and scenario analysis with experimentation reserves. Use when: budget allocation, marketing budget, launch budget, how much to spend, budget planning, channel budget.

Budget Allocator (APEX Allocation Model)

A rigorous budget optimization engine that applies portfolio theory principles to marketing spend allocation, producing scenario-modeled investment plans with built-in experimentation reserves and continuous rebalancing triggers. APEX ensures every dollar is allocated to its highest-impact use while maintaining optionality for emerging opportunities.

When to Use

• Planning marketing budget for a product launch
• Allocating spend across channels and timeframes
• Building ROI projections for budget approval
• Designing structured marketing experiments with kill criteria
• Stress-testing budget assumptions through sensitivity analysis
• Rebalancing mid-campaign when channels over- or underperform
• Justifying budget requests to finance or leadership

What You'll Need

Critical inputs (ask if not provided):

• Total available budget and time horizon
• Target metrics (pipeline, revenue, CAC targets, ROI floor)
• Channel performance data or benchmarks (from demand-engine WAVE scores)
• Product and launch context (launch type, audience, market)
• Financial constraints or guardrails (max spend per channel, minimum ROI)

Nice-to-have:

• Historical channel performance data (CAC, conversion rates, LTV by channel)
• Competitive spend intelligence (from battle-scanner)
• Seasonal or market timing data (from signal-radar)
• Customer journey stage mapping (from journey-architect)
• Previous launch budgets and actuals for calibration

Process

Step 1: Allocate -- Define the Five Spend Buckets

Every launch budget is divided into five strategic buckets. The percentages flex based on launch type and maturity.

Bucket	Range	Purpose	Examples
Foundation	15-20%	Infrastructure that enables all other spend	Website, landing pages, tracking, tooling, creative assets
Awareness	25-30%	Top-of-funnel reach and brand visibility	Content marketing, PR, social media, display, sponsorships
Acquisition	30-35%	Direct pipeline and demand generation	Paid search, paid social, email campaigns, events, webinars
Enablement	10-15%	Sales and partner activation	Sales tools, partner co-marketing, demo environments, training
Experiment Reserve	10-15%	Structured tests on unproven channels	New channels, messaging tests, audience tests, creative tests

Bucket Allocation by Launch Type:

Launch Type	Foundation	Awareness	Acquisition	Enablement	Experiment
New Product (GA)	20%	30%	25%	15%	10%
Major Feature	15%	25%	35%	15%	10%
Market Expansion	15%	30%	30%	10%	15%
PLG/Self-Serve	20%	20%	30%	10%	20%
Enterprise Upmarket	15%	20%	30%	25%	10%

Step 2: Allocate -- Channel-Level Distribution Using WAVE Scores

Within each bucket, distribute budget across channels using WAVE scores from demand-engine (or estimate if not available).

Channel Scoring Matrix:

Channel	WAVE Score (1-10)	Historical CAC	Est. Pipeline	Confidence	Budget Share
Paid Search
Paid Social (LinkedIn)
Paid Social (Meta)
Content/SEO
Email Marketing
Events/Webinars
Partner Co-marketing
PR/Analyst Relations
Community/PLG
Direct Outbound

Budget Share Formula:

Channel_Budget_Share = (WAVE_Score_i / SUM(all WAVE_Scores)) x Bucket_Budget

Apply minimum allocation floor of 5% per active channel to avoid spreading too thin.

Step 3: Predict -- Three Scenarios Per Channel

For each channel, model three outcomes to build a range of expected returns.

Channel	Scenario	Budget	Est. CAC	Est. Leads	Est. Pipeline	Est. ROI	Probability
Paid Search	Conservative						25%
Paid Search	Expected						50%
Paid Search	Optimistic						25%
Paid Social	Conservative						25%
Paid Social	Expected						50%
Paid Social	Optimistic						25%

Scenario Definitions:

Scenario	Conversion Assumption	CAC Assumption	Lead Volume	Probability Weight
Conservative	70% of benchmark	130% of benchmark	70% of target	25%
Expected	100% of benchmark	100% of benchmark	100% of target	50%
Optimistic	140% of benchmark	75% of benchmark	130% of target	25%

Expected Value Calculation:

Expected_Pipeline = (Conservative x 0.25) + (Expected x 0.50) + (Optimistic x 0.25)
Expected_ROI = Expected_Pipeline / Channel_Budget

Step 4: Predict -- Aggregate Budget Scenarios

Roll up channel-level scenarios into three overall budget scenarios.

Dimension	Conservative (-20%)	Base Case	Aggressive (+30%)
Total Budget
Expected Leads
Expected Pipeline
Expected Revenue
Blended CAC
Overall ROI
Payback Period
Risk Level	Low	Medium	High
Confidence	85%	70%	55%

Step 5: Experiment -- Design Structured Tests

The experiment reserve (10-15% of budget) is allocated to structured tests with clear hypotheses and kill criteria.

Experiment Portfolio Template:

#	Experiment Name	Hypothesis	Budget Cap	Duration	Success Metric	Kill Criteria	Status
1		If we [action], then [outcome] because [reason]				Stop if [metric] < [threshold] after [time]	Planned
2
3
4
5

Experiment Evaluation Criteria:

Criterion	Weight	Scoring (1-5)
Learning value (even if fails)	25%	1=Low, 5=Transformative insight
Scalability if successful	25%	1=Niche, 5=10x scalable
Speed to signal	20%	1=>90 days, 5=<14 days
Budget efficiency	15%	1=>10% reserve, 5=<2% reserve
Strategic alignment	15%	1=Tangential, 5=Core strategy

Experiment Priority Score = SUM(Criterion_Score x Weight)

Run top 3-5 experiments. Graduate winners into main budget; kill losers at criteria thresholds.

Step 6: X-ray -- Sensitivity Analysis

Identify the top 3 assumptions that most impact ROI and stress-test each.

Sensitivity Analysis Framework:

Assumption	Base Value	-30%	-15%	Base	+15%	+30%	Impact on ROI
Conversion rate
Average deal size
Sales cycle length
CAC by channel
Retention rate

Tornado Chart Data (rank by ROI swing):

Rank	Assumption	Downside ROI	Base ROI	Upside ROI	Swing
1
2
3

For each high-sensitivity assumption, define:

• Monitoring metric: How will you track this assumption in real time?
• Rebalancing trigger: At what threshold do you adjust spend?
• Response protocol: What specific action do you take?

Step 7: Monthly Rebalancing Protocol

Budget is not static. Apply these rebalancing rules monthly.

Rebalancing Decision Matrix:

Channel Performance	Duration	Action	Budget Change
Underperform target by >25%	1 month	Monitor, optimize creative/targeting	No change
Underperform target by >25%	2 months	Reduce allocation	-30% from channel
Underperform target by >25%	3 months	Pause channel	Reallocate 100%
At target (+/- 10%)	Any	Maintain	No change
Outperform target by >25%	1 month	Validate signal is real	No change
Outperform target by >25%	2+ months	Increase allocation	+20% to channel

Rebalancing Source/Destination Rules:

• Freed budget goes first to experiment reserve (up to 20% of total)
• Then to highest-ROI performing channel (up to 150% of original allocation)
• Never concentrate >40% of total budget in a single channel

Output

Save to outputs/budget-allocator/

Deliverables:

1. Budget Allocation Model -- Five-bucket allocation with channel-level distribution, WAVE-score-weighted, with minimum floors and maximum caps per channel
2. ROI Projection Matrix -- Three scenarios (conservative/base/aggressive) per channel and aggregate, with expected values, CAC, pipeline, and payback calculations
3. Experiment Portfolio -- 3-5 structured experiments with hypotheses, budget caps, success metrics, kill criteria, and priority scores
4. Sensitivity Analysis -- Tornado chart of top assumptions, stress-test results, monitoring metrics, and rebalancing triggers with response protocols

Chain Connections

• Receives from: demand-engine (WAVE scores, channel strategy), financial-analyst (unit economics, ROI thresholds), battle-scanner (competitive spend intel), signal-radar (market timing)
• Feeds into: launch-command (budget as input to launch readiness), demand-engine (rebalancing feedback)
• Enhanced by: launch-pulse (actual performance data for rebalancing), launch-debrief (historical calibration data)