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Full-stack automation (design → deploy)
Generate technical specs and project plans up front; auto-provision IAM roles, Glue triggers, jobs, and S3 layouts—no brittle manual scripts.
Zero-downtime snapshot + sync
Migrate historical data and continuously capture live changes with idempotent, resumable jobs for clean, repeatable cutovers.
Integrity by default
Built-in reconciliation (schema + data), precision/scale checks, and automated rollbacks protect production and ensure 1:1 fidelity.
Performance & cost optimization
Intelligent micro-batching and Iceberg compaction reduce tiny files, keep metadata lean, and sustain query performance at scale.
Unified, analysis-ready dataset
Bring historical spends, conversions/revenue, & controls (seasonality, holidays, market events) into one clean Databricks table for modeling.
Transparent Bayesian MMM (PyMC Marketing)
Flexible, inspectable models—no black boxes—tailored priors and features to your business context for credible, defensible ROI.
Adstock & saturation built-in
Capture carry-over (half-life) and diminishing returns per channel to detect overspend thresholds and underfunded opportunities.
Optimization & scenario simulator
Explore “+30% Search / −40% Display?” and get the recommended mix, forecasted impact, & clear analyst visuals plus marketer-friendly summaries.
The 4 Models of The Solution
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Data Foundation & Feature Engineering Model
Aggregates multi-channel spend, outcomes, and control variables; builds adstocked features and saturation transforms for robust inference. Inputs: channel spends, revenue/conversions, seasonality/holiday flags. Outputs: analysis-ready tables (Databricks), feature store artifacts.
Bayesian MMM Estimation Model (PyMC Marketing)
Estimates channel contributions with priors tuned to your domain; separates true marketing impact from noise and external factors. Inputs: engineered features, controls.
Outputs: ROI by channel, half-life estimates, saturation curves, uncertainty intervals.
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Budget Optimization & Scenario Planning Model
Runs constrained optimizations and fast simulations to recommend the optimal mix under your budget and guardrails. Inputs: fitted MMM, constraints (min/max spend, pacing). Outputs: recommended allocation, forecasted revenue/ROI, sensitivity views.
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Governance, Explainability & Delivery Model
Packages results as analyst dashboards and executive briefs, with reproducible runs, versioned priors, and clear documentation. Inputs: MMM outputs, metadata, run configs.
Outputs: dashboards/plots, business summaries, model cards and runbooks
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