Marketing Lift Bayes Modeler
Bayesian statistician for measuring true incremental marketing impact — campaign lift estimation, Marketing Mix Modeling, geo-based incrementality experiments, and Bayesian A/B testing with properly quantified uncertainty.
SupaScore
83.8Best for
- ▸Measuring true incremental impact of Google Ads campaigns using geo-based holdout experiments
- ▸Building Bayesian Marketing Mix Models to decompose multi-channel attribution with uncertainty quantification
- ▸Running CausalImpact analysis on time-series data to measure campaign lift vs synthetic control
- ▸Designing and analyzing randomized A/B tests with proper Bayesian statistical inference
- ▸Quantifying marketing incrementality beyond last-click attribution using causal inference methods
What you'll get
- ●Bayesian A/B test results with posterior probability distributions, credible intervals, and expected lift ranges with business impact quantification
- ●Marketing Mix Model coefficients with adstock parameters, saturation curves, and channel contribution decomposition with uncertainty bands
- ●CausalImpact time-series analysis showing actual vs predicted counterfactual performance with statistical significance testing
Not designed for ↓
- ×Basic web analytics reporting or dashboard creation without statistical modeling
- ×Simple conversion tracking or attribution without causal measurement requirements
- ×General data science tasks unrelated to marketing measurement
- ×Marketing strategy or creative optimization without quantitative lift measurement
Marketing campaign data with time-series granularity, geographic dimensions, or randomized treatment assignments for causal inference analysis.
Bayesian statistical models with credible intervals, posterior distributions, and actionable recommendations for marketing budget allocation based on measured incrementality.
Evidence Policy
Enabled: this skill cites sources and distinguishes evidence from opinion.
Research Foundation: 7 sources (1 books, 2 paper, 1 official docs, 2 industry frameworks, 1 community practice)
This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.
Version History
Initial release
Prerequisites
Use these skills first for best results.
Works well with
Need more depth?
Specialist skills that go deeper in areas this skill touches.
Common Workflows
End-to-End Campaign Incrementality Measurement
Design geo-based holdout experiments, measure statistical lift with Bayesian methods, then integrate results into broader attribution framework
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