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budget-optimizerlisted

Reallocate marketing spend across channels using performance data and diminishing-returns modeling — produces a current-vs-optimized allocation table, projected ROI ranges with confidence intervals, a phased 4-8 week reallocation timeline, and a 10-15% testing reserve. Recommends shifts only; it never changes spend on any platform. Triggers on "/digital-marketing-pro:budget-optimizer", "optimize my marketing budget", "which channels should get more spend", "reallocate budget based on ROAS", "is our channel split right". Reads the brand profile and guidelines, runs scripts/budget-optimizer.py, and pairs with /digital-marketing-pro:budget-tracker for in-flight pacing.
teachskillofskills-ai/DigitalMarketingPro-techshu · ★ 0 · Data & Documents · score 75
Install: claude install-skill teachskillofskills-ai/DigitalMarketingPro-techshu
# /digital-marketing-pro:budget-optimizer ## Purpose Data-driven marketing budget optimization across channels using performance data and industry benchmarks. Analyzes current spend efficiency, models diminishing returns per channel, and produces an optimized allocation with projected ROI improvement and a phased reallocation timeline. ## Input Required The user must provide (or will be prompted for): - **Current budget by channel**: How spend is distributed today (e.g., paid search, paid social, SEO, email, content, display, affiliate, events, etc.) - **Performance data by channel**: Key metrics per channel — spend, revenue or conversions, CPA, ROAS, and conversion volume over the measurement period - **Total budget available**: Overall marketing budget for the optimization period (monthly, quarterly, or annual) - **Business goals**: Primary objective — maximize revenue, minimize CPA, hit a specific lead or revenue target, balance growth with efficiency - **Constraints**: Minimum spend requirements, channel mandates from leadership, seasonal considerations, contractual commitments, or platform minimums - **Measurement period**: Timeframe the performance data covers (last 30, 60, 90 days, or custom range) - **Attribution model**: How conversions are currently attributed (last-click, first-click, linear, data-driven, or unknown) - **Seasonality factors**: Upcoming seasonal peaks, promotional periods, or industry events that affect channel performance - **Historical contex