AI for Work🎯 Leadership & StrategyLearnAspire Certified

AI Marketing Engineering: GPT-4o Pipelines, CDP Personalisation, and Shapley Attribution on GA4 BigQuery

“From Jupyter notebook experiment to production MarTech system — shipped.”

Build and ship a cost-controlled content generation pipeline, a CDP-connected personalisation layer, and a Shapley-value multi-touch attribution model your CMO can run weekly — all against production APIs.

Intermediate11h6 modules36 slides18 exercises24 quiz Qs
🔥 Launch Price — 63% off. Limited time.
₹2,999₹7,999

One-time · Lifetime access · Certificate included

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  • ✓6 modules of content
  • ✓36 concept slides
  • ✓18 practical exercises
  • ✓24 quiz questions
  • ✓Capstone project
  • ✓LearnAspire certificate

Learning Outcomes

What you'll learn

→You will be able to design and execute an OpenAI Batch API job that generates 1,000 personalised email subject line variants segmented by Segment CDP trait values, enforces a structured output schema via JSON mode, and stays under a $0.003-per-output cost ceiling — with retry logic for 429s and malformed JSON responses baked in
→You will be able to build a Segment CDP → personalisation mapping layer in Python that reads audience traits from the Segment Public API, constructs per-segment prompt templates with injected first-party signals, and writes output variants to a BigQuery staging table in a schema ready for direct HubSpot sequence injection via the HubSpot Engagements API v3
→You will be able to write and execute a Shapley-value multi-touch attribution model in Python (using the exact itertools coalitions approach) over a full GA4 BigQuery export — handling missing utm_source nulls, direct-traffic cannibalisation, and session deduplication — and schedule it as a weekly BigQuery scheduled query your analytics team can run without your intervention
→You will be able to pull campaign creative performance data from the Meta Marketing API v20.0 (ad account insights endpoint with breakdowns by age, gender, and placement), join it against your attribution model output in BigQuery, and publish a Looker Studio dashboard that surfaces AI-generated variant performance by audience segment with week-over-week delta metrics
→You will be able to harden a production AI content pipeline against prompt injection in customer-facing outputs, PII leakage from CDP trait injection into prompts, OpenAI token budget overruns at 50K-user scale (with exact monthly cost projections), and Meta Ads API rate limit exhaustion — with circuit breaker logic and a dead-letter queue pattern implemented in Python

The day after you finish

The day after completing this course, you can open a PR that adds a Segment CDP audience trait → OpenAI Batch API → BigQuery staging table pipeline to your team's repo, with a Shapley attribution SQL query your analytics team can schedule in BigQuery, and a Meta Ads API pull feeding a Looker Studio dashboard — and walk your CMO through what the attribution numbers mean and why last-click was lying to them.

Who this is for

  • Primary: MarTech or marketing data engineers with 2–4 years of experience who can write Python and SQL against a data warehouse but have never shipped an AI content or attribution pipeline into production
  • Secondary: Data engineers or backend engineers embedded in a growth or demand-gen team who are being asked to own the AI tooling layer for the first time
  • Tertiary: Marketing analytics leads and RevOps engineers who will consume, QA, or extend the attribution models and content pipelines this course produces

Prerequisites

  • Python fluency: comfortable writing async functions, dataclasses, Pydantic models, and calling REST APIs with httpx or requests — not just reading them
  • SQL against BigQuery: comfortable writing multi-step CTEs, window functions, and UNNEST on repeated RECORD fields — you have run at least one GA4 BigQuery export query in anger
  • Has an OpenAI API key with billing configured and a GCP project with BigQuery enabled — the course starts at the first API call, not account setup
  • Familiarity with at least one CDP (Segment, mParticle, or Rudderstack) at the conceptual level — you know what a trait, an event, and an audience are; you do not need to have built a pipeline
  • Basic familiarity with HubSpot or any CRM at the data-model level — contacts, deals, sequences, properties — not necessarily admin access

Curriculum

6 modules · full breakdown

💼 Part of: AI for Business Professionals Path

Step 1 — AI Foundations
→Step 2 — Prompting
→Step 3 — Sales AI
→Step 4 — HR AI
→Step 5 — Marketing AI
← Previous: Step 4 — HR AI
🏆

Capstone Project

Veloxa AI Marketing System: End-to-End Content Pipeline, CDP Personalisation, and Shapley Attribution Dashboard

You build the complete AI marketing stack for Veloxa: a cost-governed GPT-4o Batch API pipeline that reads three Segment CDP audience segments (Enterprise, Mid-Market, SMB) and generates 300 personalised email subject line variants per segment at under $0.003 each, stored in a BigQuery staging table with a schema that maps directly to HubSpot sequence properties; a Shapley-value attribution model that runs over a provided 90-day synthetic GA4 BigQuery export (3.2M events, realistic UTM noise, 12% null utm_source rate) and produces a channel_credit table your team can query; and a Looker Studio report connected to the Meta Ads API v20.0 that shows creative performance broken down by the three Segment audience segments. The entire system is implemented as a Python package with a pyproject.toml, environment-variable-based secrets management, idempotent BigQuery writes, and a documented runbook for the one junior engineer who will own it after you.

What you'll deliver

A GitHub repository containing: (1) a Python package with an async OpenAI Batch API client, a Segment CDP trait reader, a BigQuery writer, and a cost-guard decorator that raises if projected spend exceeds a configurable ceiling; (2) a BigQuery SQL file containing the Shapley attribution model as a scheduled query with parameterised date ranges; (3) a Looker Studio dashboard template (exported as JSON) connected to the BigQuery attribution output and the Meta Ads API insights; (4) a RUNBOOK.md that a junior engineer can follow to re-run, extend, or debug the pipeline — this file is graded as part of the submission

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