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Advanced econometric modeling for marketing effectiveness and budget optimization
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npx skills add https://github.com/a5c-ai/babysitter --skill media-mix-modeling명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
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This skill should be used when the user asks to "find skills in the wild", "assimilate popular workflows", "discover SKILL.md files in repos", "research external skills", "find workflow patterns", "survey the skill landscape", "what skills exist out there", or wants to investigate public repositories for extractable processes, babysitter plugins, and reusable procedural insights. Searches GitHub for SKILL.md files, classifies repos by archetype, and maintains structured research under docs/reference-repos/.
SOC 직업 분류 기준
SKILL.md 표시 중
| name | media-mix-modeling |
| description | Advanced econometric modeling for marketing effectiveness and budget optimization |
| allowed-tools | ["Read","Write","Glob","Grep","Bash","WebFetch"] |
| metadata | {"specialization":"marketing","domain":"business","category":"Marketing Analytics","skill-id":"SK-019"} |
| graph | {"domains":["domain:marketing"],"skillAreas":["skill-area:brand-strategy","skill-area:brand-positioning","skill-area:content-marketing"],"workflows":["workflow:brand-campaign-launch"],"roles":["role:marketing-manager","role:marketing-strategist","role:brand-manager"]} |
The Media Mix Modeling Skill provides advanced econometric modeling capabilities for measuring marketing effectiveness and optimizing budget allocation. This skill enables marketing mix model development, channel contribution analysis, saturation curve modeling, and scenario planning using statistical techniques and machine learning approaches including Google Lightweight MMM and custom Python/R implementations.
This skill integrates with the following marketing processes:
skill: media-mix-modeling
action: build-model
parameters:
model_type: bayesian_mmm
framework: lightweight_mmm
data_configuration:
date_column: week
target_variable: revenue
media_variables:
- tv_spend
- digital_display_spend
- paid_search_spend
- paid_social_spend
- radio_spend
control_variables:
- price_index
- competitor_spend
- economic_indicator
- seasonality_index
model_settings:
adstock:
type: geometric
max_lag: 8
saturation:
type: hill
priors:
type: informative
source: prior_mmm_results
validation:
holdout_weeks: 12
cross_validation_folds: 5
skill: media-mix-modeling
action: analyze-contributions
parameters:
model_id: "mmm_2024_q4"
analysis_period:
start_date: "2024-01-01"
end_date: "2024-12-31"
outputs:
- type: contribution_breakdown
format: waterfall_chart
- type: channel_roi
format: bar_chart
- type: contribution_over_time
format: stacked_area
- type: marginal_contribution
format: line_chart
export:
format: [pdf, csv, xlsx]
destination: "reports/mmm_contributions"
skill: media-mix-modeling
action: optimize-budget
parameters:
model_id: "mmm_2024_q4"
optimization_settings:
objective: maximize_revenue
total_budget: 10000000
constraints:
- channel: tv_spend
min_percent: 0.20
max_percent: 0.40
- channel: paid_search_spend
min_percent: 0.15
max_percent: 0.30
- channel: paid_social_spend
min_percent: 0.10
max_percent: 0.25
business_rules:
- type: minimum_presence
channels: [tv, digital_display]
- type: maximum_concentration
single_channel_cap: 0.50
scenarios:
- name: "optimal_allocation"
constraints: default
- name: "digital_first"
overrides:
digital_channels_min: 0.60
- name: "brand_building"
overrides:
tv_min: 0.35
skill: media-mix-modeling
action: run-scenarios
parameters:
model_id: "mmm_2024_q4"
scenarios:
- name: "Budget Cut 20%"
budget_change: -0.20
allocation: optimized
- name: "Budget Increase 30%"
budget_change: 0.30
allocation: optimized
- name: "TV Elimination"
channel_changes:
tv_spend: 0
reallocate: true
- name: "New Channel Test"
new_channels:
- name: connected_tv
estimated_roi: 2.5
test_budget: 500000
- name: "Q1 Seasonal Plan"
period: "2025-01-01 to 2025-03-31"
seasonality_adjustment: true
comparison_metrics:
- total_revenue
- incremental_revenue
- overall_roi
- channel_roi
skill: media-mix-modeling
action: analyze-saturation
parameters:
model_id: "mmm_2024_q4"
channels:
- tv_spend
- paid_search_spend
- paid_social_spend
analysis:
- type: response_curves
spend_range: [0, 2x_current]
granularity: 100_points
- type: optimal_spend
threshold: 0.95_saturation
- type: marginal_roi_curve
spend_range: [0.5x_current, 1.5x_current]
visualization:
charts:
- response_curves_overlay
- marginal_roi_comparison
- saturation_heatmap