Marketing Effectiveness Lab
Marketing effectiveness platform for a UK fashion ecommerce scenario, covering MMM, incrementality evidence, source diagnostics, Bayesian modelling, and budget optimisation. Includes adstock, saturation, contribution, ROI, response curves, uncertainty intervals, a lift-test evidence workflow, and a Streamlit executive dashboard.
- Python
- pandas
- NumPy
- statsmodels
- Plotly
- Streamlit
- MMM
- Bayesian inference
- GitHub Actions
- pytest
Problem
A UK fashion ecommerce team wants to know which marketing channels actually drive incremental revenue — not just which channels happen to correlate with sales — and how to allocate next quarter's budget under realistic constraints. That means turning noisy, multi-source marketing data into cautious, explainable investment recommendations that survive stakeholder scrutiny.
What I built
A portfolio marketing effectiveness platform that runs the full workflow a commercial or marketing data scientist is expected to own:
- Data contracts for weekly marketing, ecommerce, web analytics, paid media, CRM, affiliate, influencer, and external-control exports, with source diagnostics for coverage, missing channels, and modelling readiness.
- Marketing mix modelling (MMM) with adstock and saturation transforms, channel contribution, ROI, and response curves.
- A lightweight Bayesian posterior layer that reports uncertainty intervals instead of single-point ROI claims.
- Incrementality calibration — a lift-test evidence workflow that uploads experiment results, governs them, and folds them back in as experiment-informed priors.
- Budget optimisation — profit-aware scenario planning and constrained allocation, with an executive summary that carries the stakeholder caveats.
All data is synthetic. The platform is intentionally transparent about its assumptions rather than presented as a deployed production system.
Evaluation / evidence
- Time-aware holdout validation on the baseline econometrics, so model fit is judged out-of-sample rather than in-sample.
- Uncertainty intervals on contribution and ROI, so recommendations come with a confidence range.
- A recommendation-readiness scoring gate before any budget recommendation is presented for stakeholder review.
- A machine-readable model-run manifest for reproducibility and run-to-run comparison.
Why it matters
It shows business-facing modelling end to end: turning noisy marketing data into cautious, explainable investment and budget-allocation recommendations, with the uncertainty and governance a real decision would demand.