Case Study — Amazon PPC Automation Platform

Amplitude API running. Questions still unanswered. A 2,100+ line Python analytics framework built from the data up.

From vanity counts to a decision-ready growth engine. How an Amazon PPC platform bypassed API limitations with a custom ETL layer and AWS SageMaker infrastructure to identify a 2.2x expansion lift.

Stack

2,100+

Lines of production Python delivered

0.82

Churn prediction model AUC

2.2x

Higher expansion probability for automated users

~70%

Reduction in manual analysis time

Success Framing.

The platform had succeeded to $10M+ ARR with standard instrumentation. But they had outgrown reactive dashboards. Amplitude was firing, but the API couldn't handle the complexity of the research-heavy user workflow.

The product team could see aggregate counts, but they couldn't answer the question that mattered for expansion: do users who create their first automation rule actually upgrade faster? Standard BI tools were producing >100% conversion rates due to out-of-order event sequencing.

The Technical Ceiling

Amplitude API failing to sequence research events correctly
Inability to calculate exact user overlaps across siloed events
Chi-square tests using estimates rather than raw event data
Manual analysis taking 15+ hours per product cycle

Technical Implementation.

We built a production-ready analytics layer to bridge the gap between raw data and growth decisions.

Step 1 — Custom Amplitude ETL Framework

Engineered a 2,100+ line Python framework. Built a custom AmplitudeClient that bypasses standard API limitations by handling NDJSON streams and implementing progressive fallback for multi-million event exports.

Step 2 — Sequential Funnel Algorithm

Developed a "Single Export" sequencing algorithm. Unlike standard funnels that count events, this sequences every user action by timestamp, ensuring conversion rates never exceed 100% and reflect actual behavior.

Step 3 — AWS Analytics Infrastructure

Deployed a secure AWS environment using S3 (encrypted storage), Secrets Manager (API security), and SageMaker. This allowed for exact user-level overlaps that Amplitude's aggregate endpoints couldn't provide.

Step 4 — Statistical Rigor & ML Modeling

Ran real Chi-Square tests on raw cohort data. Developed a logistic regression churn model (AUC 0.82) and a survival analysis LTV model to quantify the expansion opportunity.

Step 5 — Automated Pipeline Deployment

Deployed daily scheduled analysis jobs with exponential backoff and timeout protection. The system now delivers decision-ready data before the Monday product meeting without manual work.

The Impact.

2.2x

Higher probability of upgrade for users who activate automation rules (verified via p < 0.05)

0.82

Churn prediction model power (AUC), identifying at-risk accounts 30 days before cancellation

$3.3M+

Projected annual revenue gain from expansion optimization based on LTV survival modeling

~70%

Reduction in manual analysis time for the product and data engineering teams

The Installed System.

Python Analytics Package

A production-ready framework with 7 core modules for API connection, sequential funnel analysis, and cohort tracking. Fully documented and tested for the Amazon advertising SaaS data schema.

AWS SageMaker ML Pipeline

An automated environment that runs complex statistical tests and churn models. Secured via IAM roles and KMS encryption, ensuring PII compliance while maintaining data depth.

Sequential Funnel Algorithm

A proprietary sequencing engine that bypasses Amplitude's out-of-order counting. It provides visibility into the exact order of research tasks that leads to successful activation.

Statistical Rigor: The Expansion Lift.

Moving from estimates to raw event overlaps revealed the first statistically significant proof of the platform's value proposition.

User Segment	Sample Size	Upgrade Rate	Lift	Significance
Non-Automated Users	3,758	0.64%	Baseline	—
Automated Users	711	2.11%	3.3x	p = 0.022

The Chi-Square test (χ² = 5.21, p = 0.022) confirmed that users who adopt automation are 2.2x to 3.3x more likely to expand. This insight justified a complete shift in acquisition spend toward high-maturity agency segments.

"ProductQuant built the analytics layer our internal team didn't have the bandwidth to engineer. Bypassing the Amplitude API limitations with a custom Python framework was the only way to get real statistical results. We can now see exactly which behavioral patterns predict a $20k expansion before it happens."

Product Team

Amazon PPC Automation Platform

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 vanity dashboards into a high-confidence growth model.

What this looks like for your company

Analytics Audit.

A structured review of your entire analytics stack — events, properties, dashboards, and gaps — with a prioritised roadmap of what to instrument next and what to fix first.

Stack assessment: tool configuration, integration health, and current-state summary
Event audit: every event reviewed with status, issues, and recommendations
Gap analysis: 5–10 biggest analytical blind spots revenue-sized
Implementation roadmap: exact event names, properties, and dashboard charts prioritised by impact
Six decision-ready dashboards: activation funnel, retention cohorts, feature adoption, churn early warning

$3,497 · 10 days

Right for you if

Analytics data exists but nobody is confident it’s accurate or complete
Product decisions made from instinct because dashboards don’t answer the right questions
Building ML-driven features and need clean, structured event data as the foundation

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