Description

A focused course, tailored for you

The Experimentation Manager's Course on Boosting Marketplace Efficiency When Quarterly Growth Slows

Turn the pressure of tightening margins into a repeatable system that scales your marketplace experiments without extra headcount.

Stop rebuilding the experiment dashboard every Monday while leadership doubts your function's impact.

$199 one-time

Tailored to your situation. Access within 24 hours. 30-day money-back.

Includes a hand-built implementation playbook delivered alongside course access, generated for your specific situation.

Why this course

Shopify announced a 10% workforce reduction this month, and the experimentation team is suddenly asked to deliver more insights with fewer hands. Daily stand-ups are filled with manual SQL queries, fragmented BigQuery dashboards, and ad-hoc spreadsheet mash-ups that stall decision-making. If the velocity drops, senior leadership will question the value of the experimentation function, risking further cuts.

Your current workflow relies on copying queries between notebooks, chasing data owners for missing tables, and re-creating the same KPI reports for each stakeholder meeting. The lack of a single source of truth forces you to spend hours reconciling numbers instead of designing new tests, and every missed deadline adds pressure from product and growth leads who need actionable insights fast.

What you walk away with

A unified experiment tracking dashboard that updates automatically each sprint.
A reusable SQL library that reduces query build time by 70%.
A stakeholder-ready insight pack that delivers clear recommendations in under five minutes.
A documented workflow that aligns data owners, analysts, and product leads on experiment priorities.
A measurable increase in experiment throughput that can be shown to leadership.

The 12 modules

Module 1. Experiment Tracking Dashboard

85% of high-growth teams cite lack of a live view as a bottleneck. Imagine the weekly planning meeting where data gaps still surface. This module walks through building a live dashboard that aggregates test status, conversion lift, and confidence intervals. The deliverable is a ready-to-share dashboard.

Module 2. SQL Library for Common Metrics

During the Monday data sync you stare at a half-hour of copy-paste queries. Here you construct a shared library of vetted metric definitions, revenue, cart add-to-cart, churn, that any analyst can call. Output: a populated SQL library.

Module 3. Stakeholder Insight Pack

What does the product lead ask themselves when the deck feels too dense? This module designs a concise one-page pack that translates raw lift numbers into actionable next steps. What you ship from this module: an insight pack template.

Module 4. Data Ownership RACI

By module end a RACI matrix sits in your drive.

Module 5. Experiment Prioritization Framework

Balancing revenue impact against implementation effort creates tension between growth and engineering. Learn to score each test using a weighted matrix that aligns with quarterly OKRs. The deliverable is a populated prioritization sheet.

Module 6. Automated Data Refresh Pipeline

The fastest path from fragmented extracts to a single source is an automated pipeline that pulls raw tables nightly. You’ll configure a scheduled BigQuery job and a validation step. Output: an automated refresh script.

Module 7. CFO Dashboard View

What you ship from this module: a CFO-ready impact dashboard.

Module 8. Experiment Documentation Register

Sitting at the end of this module: a populated experiment register.

Module 9. Result Validation Checklist

The deliverable is a validation checklist.

Module 10. Cross-Team Communication Playbook

Stakeholders from product, growth, and finance each expect different granularity. This module creates a playbook that maps communication cadence, format, and decision owners. Output: a communication playbook.

Module 11. Performance Scorecard

What you ship from this module: a scorecard template.

Module 12. Continuous Improvement Loop

A stakeholder asks, "What’s next after this sprint?" Design a loop that captures learnings, updates the prioritization matrix, and feeds back into the tracking dashboard. The deliverable is a documented improvement loop guide.

How this addresses your situation

Specific modules that map to what you said you are dealing with.

Module 1 covers Experiment Tracking Dashboard , exactly the live view you need when the weekly planning meeting still shows blank charts.

Module 4 covers Data Ownership RACI , precisely the ownership confusion you hit every time a data source changes without notice.

Module 7 covers CFO Dashboard View , the exact high-level impact sheet the finance lead asks for before each quarterly review.

What you get with this course

A live experiment tracking dashboard template.
A reusable SQL library with 30 pre-built metric queries.
A one-page stakeholder insight pack.
A data ownership RACI matrix.
An experiment prioritization scoring sheet.
An automated BigQuery refresh script.
A CFO impact dashboard.
A comprehensive experiment documentation register.
A statistical result validation checklist.
A cross-team communication playbook.
A quarterly performance scorecard.
A continuous improvement loop guide.

What you will have in hand by Day 1, Week 1, Month 1

Day 1: tailored playbook in hand, experiment register template pre-populated for your environment, SQL library ready for immediate use.

Week 1: first live experiment tracking dashboard live and shared with product leads, stakeholder insight pack generated for the current sprint.

Month 1: recurring quarterly scorecard reporting running automatically, with a documented improvement loop ready for the next planning cycle.

Before and after

Before

Your experiment data lives in separate notebooks, ad-hoc spreadsheets, and scattered BigQuery tables. Every sprint you waste hours reconciling numbers, chasing data owners, and manually assembling reports. Leadership sees inconsistent metrics, and the team loses credibility when deadlines slip.

After

All experiments are logged in a single register, the dashboard updates automatically, and the insight pack delivers clear recommendations in minutes. Stakeholders receive a unified view each week, and you can demonstrate measurable lift and ROI to leadership without extra headcount.

What happens if you do not address this

If you ignore this now, the next sprint will lose another two days to manual data stitching, the Q3 leadership review will flag your team’s lack of measurable impact, and the ongoing headcount reduction could target the experimentation function.

Who it is for

A mid-level manager who runs daily experiment planning, data extraction, and reporting for a high-traffic e-commerce marketplace. She coordinates cross-functional squads, juggles tight sprint deadlines, and must translate raw query results into clear recommendations for product, growth, and finance teams.

Who this is NOT for. This is not for someone who needs a basic introduction to SQL or a generic data-analysis course.

How it arrives

Within 24 hours of purchase your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it. The playbook is hand-built around your specific situation, not LLM-generated boilerplate.

Time investment. 6 hours of focused work spread over a week, saving an estimated 40-60 hours of manual reporting effort.

Why $199 is the right number

A half-day consultant would charge $2,500 to map your experiment workflow, a generic data-analysis certification runs $1,200, and building this yourself could consume 60+ hours. At $199 you get a proven system and ready-to-use artefacts for a fraction of the cost.

FAQ

Do I need advanced SQL skills to complete the course?

Basic SELECT statements are enough; the modules provide reusable snippets you can adapt.

Will the templates work with my existing BigQuery datasets?

Yes, each artefact is built to connect to standard Shopify analytics tables.

Can I apply this if my team is already using a different experimentation platform?

The framework is platform-agnostic and focuses on data flow and reporting, not the tool itself.

How much time will I need each week to finish the course?

About 6 hours total, spread over a week, with immediate payoff in saved manual work.

30-day money-back guarantee. If after a week of working through the materials this is not what you needed, reply to the receipt email and a full refund is processed. No questions, no forms.

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.