Backend Automation for Marketing Mix Modeling
Budget: $8 – $15 USD
Project: Marketing Mix Modeling (MMM) — Automation Backend (Service Industry Use Cases)
Goal
Develop a production-ready MMM engine that runs end-to-end in automation: data prep → model training → diagnostics → budget optimization → scheduled re-training. Deliver clean, well-tested code that can run headless (CLI/cron/Airflow/Step Functions), configured by YAML/JSON—no UI required.
Scope of Work
1) Data layer (code, not pipelines)
Implement schema-aware loaders that read canonical tables (daily/weekly):
spend, sales, price, promo, optional reach_freq, search_query_volume.
Provide transform utilities (carryover windows, seasonality/calendar features, holidays).
Configurable data contracts (validate columns, grain, nulls, ranges) with clear errors.
2) MMM core modeling
Implement Bayesian MMM with per-channel adstock/carryover and saturation (Hill/logistic).
Support hierarchical pooling (brand/region) and priors; option for frequentist baseline.
Optional bias controls for paid search (e.g., GQV/back-door controls).
Robust uncertainty: posterior samples/intervals for ROAS and elasticities.
3) Calibration & validation
Hooks to calibrate with lift/geo experiments (when provided).
Time-split backtests; out-of-sample diagnostics; fit/forecast metrics.
Automated model selection across adstock/shape families.
4) Budget optimization & what-if
Deterministic optimizer (e.g., constrained nonlinear / Bayesian expected utility) that:
maximizes expected sales/profit under budget & min/max channel constraints,
supports diminishing returns and uncertainty (risk-aware plans).
Fast what-if simulator given proposed spends; emits response curves & KPIs.
5) Automation & packaging
CLI commands (examples below) and config-driven runs; no hard-coded paths.
Idempotent outputs: model_bundle/ (parameters, curves, diagnostics) + plan/.
Containerized (Docker) with reproducible environments; ready for CI.
Comprehensive unit/integration tests; dataset fixtures (synthetic).
6) Documentation
README + quick-start + config reference.
Model math note (one pager) and interpretation guide (curves/ROAS/uncertainty).
Goal
Develop a production-ready MMM engine that runs end-to-end in automation: data prep → model training → diagnostics → budget optimization → scheduled re-training. Deliver clean, well-tested code that can run headless (CLI/cron/Airflow/Step Functions), configured by YAML/JSON—no UI required.
Scope of Work
1) Data layer (code, not pipelines)
Implement schema-aware loaders that read canonical tables (daily/weekly):
spend, sales, price, promo, optional reach_freq, search_query_volume.
Provide transform utilities (carryover windows, seasonality/calendar features, holidays).
Configurable data contracts (validate columns, grain, nulls, ranges) with clear errors.
2) MMM core modeling
Implement Bayesian MMM with per-channel adstock/carryover and saturation (Hill/logistic).
Support hierarchical pooling (brand/region) and priors; option for frequentist baseline.
Optional bias controls for paid search (e.g., GQV/back-door controls).
Robust uncertainty: posterior samples/intervals for ROAS and elasticities.
3) Calibration & validation
Hooks to calibrate with lift/geo experiments (when provided).
Time-split backtests; out-of-sample diagnostics; fit/forecast metrics.
Automated model selection across adstock/shape families.
4) Budget optimization & what-if
Deterministic optimizer (e.g., constrained nonlinear / Bayesian expected utility) that:
maximizes expected sales/profit under budget & min/max channel constraints,
supports diminishing returns and uncertainty (risk-aware plans).
Fast what-if simulator given proposed spends; emits response curves & KPIs.
5) Automation & packaging
CLI commands (examples below) and config-driven runs; no hard-coded paths.
Idempotent outputs: model_bundle/ (parameters, curves, diagnostics) + plan/.
Containerized (Docker) with reproducible environments; ready for CI.
Comprehensive unit/integration tests; dataset fixtures (synthetic).
6) Documentation
README + quick-start + config reference.
Model math note (one pager) and interpretation guide (curves/ROAS/uncertainty).