Case Study — Amazon PPC Automation Platform

Amplitude running. Questions still unanswered. A Python analytics framework and ML expansion model built from the data up.

How an Amazon PPC automation platform moved from valueless dashboards to ML-driven expansion analysis — with a custom Python analytics framework and Amplitude API integration.

2,100+

Lines of production Python code delivered

Custom analytics modules built

118+

Decision-ready dashboards built

1.9x

Retention lift for early-activated users

Before.

The platform had analytics infrastructure — Amplitude was instrumented and data was flowing. But the team couldn't answer the questions that actually drove product decisions: which users expand, where does activation break, and what predicts churn.

Existing dashboards provided no actionable insight into user maturity or expansion probability. Decisions were being made on intuition because the standard reporting layer failed to capture the complexity of the research-heavy user workflow.

The Situation

Amplitude instrumented but outputting valueless vanity metrics
Standard dashboards failing to handle complex sequential user behavior
Dashboards focused on session counts rather than activation milestones
API query limitations preventing deeper cohort and ML analysis

What we did.

Built a production-ready analytics layer to bridge the gap between data and insight.

Step 1 — Strategic Analytics Audit

Audited existing event taxonomy and dashboard utility; identified critical gaps in activation and expansion tracking that were preventing data-driven product decisions.

Step 2 — Python Analytics Framework

Built a 2,100+ line Python package with 7 core modules for API connection, sequential funnel analysis, and cohort tracking.

Step 3 — Sequential Funnel Algorithm

Engineered a custom "Single Export" algorithm that sequences events by timestamp, ensuring mathematically correct conversion rates (no >100% results).

Step 4 — Expansion ML Modelling

Developed a strategic expansion model identifying a $3.3M annual revenue opportunity by targeting discovery gaps in advanced features.

Step 5 — Automated Reporting Pipeline

Deployed daily scheduled analysis jobs with exponential backoff and timeout protection to maintain 100% real-time data availability.

After.

2,100+

Lines of tested, production-ready Python code delivered

1.9x

Higher 6-month retention for users activated within 31 days

$3.3M+

Projected annual revenue gain from expansion optimization

118+

Decision-ready charts and dashboards deployed

~70%

Reduction in manual analysis time for the product team

38%

Validated activation rate baseline (signup to first automation)

The Installed System.

Custom Analytics Framework

A production Python package that bypasses API limitations, allowing the team to run complex sequential cohort analysis that standard BI tools can't handle.

Expansion ML Pipeline

An automated model that identifies which users are at the "discovery gap" — ready for advanced features but haven't found them yet, sizing a $3.3M revenue opportunity.

Decision-Ready Reporting

Daily scheduled reporting that monitors activation velocity and retention lift, delivering data before the Monday product meeting without manual work.

Jake McMahon

ProductQuant

10 years building growth systems for B2B SaaS companies at $1M–$50M ARR. BSc Behavioural Psychology, MSc Data Science. This engagement required building a custom Python analytics engine from scratch to overcome API limitations and transform valueless dashboards into a high-confidence growth model.

Data running. Questions still unanswered?

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