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  • Gainshare Performance Marketing
  • Chicago, Illinois

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alexmarsh92/README.md

Alex Marsh

Analytics & Marketing Science Leader | Building Scalable Decision Systems

I lead analytics initiatives that turn messy, high-volume marketing data into decision-ready systems. My work sits at the intersection of marketing measurement, data engineering, and applied AI—with a strong bias toward things that actually ship and get used.

I care deeply about:

  • Making complex models operational
  • Designing analytics platforms that scale across clients and use cases
  • Reducing analyst toil through automation, standardization, and AI
  • Translating technical rigor into executive-level clarity

What I’m Working On

  • 📊 Marketing Mix Modeling (MMM) at scale
    Multi-KPI models, configurable Adstock/response curves, and scenario planning designed for real-world planning workflows.

  • 🧠 AI-augmented analytics pipelines
    Agentic systems embedded in ETL to clean data, detect anomalies, summarize performance, and assist analysts—not replace them.

  • ⚙️ Modern analytics architecture
    BigQuery + GCP pipelines optimized for incremental loads, performance, and long-term maintainability—no Tableau gymnastics required.

  • 📈 Measurement education for exec teams
    Helping leaders understand attribution, causality, and forecasting without buzzwords or false precision.

Tech I Use (and Care About)

Data & Analytics

  • BigQuery, SQL, Tableau
  • Google Cloud Dataprep / Dataflow
  • Incremental ETL & large-scale data modeling

Modeling & Measurement

  • Marketing Mix Modeling (MMM)
  • Adstock & saturation curves
  • Attribution systems (UTM, GA4 MTA, DSP-based)
  • Forecasting & experimentation frameworks

AI & Automation

  • LLM-powered agents for data cleaning and analysis
  • Structured prompt systems
  • AI-assisted ETL and reporting workflows

How I Think

  • Accuracy > novelty
  • Systems > dashboards
  • Clarity beats complexity
  • If it can’t scale, it doesn’t count

I’m especially interested in problems where data quality, modeling assumptions, and organizational reality collide—because that’s where analytics actually matters.

Currently Exploring

  • AI-driven creative attribute analysis
  • Automated MMM refresh & monitoring
  • Forecast-based KPI governance
  • Scenario planning interfaces that non-analysts can trust

Let’s Connect

If you’re working on:

  • Marketing measurement at scale
  • Analytics platform design
  • AI in real production workflows
  • Turning models into decisions

…I’m always happy to compare notes.

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