The "Upstream/Downstream" Data Context Framework
by Shaun Clowes • Chief Product Officer at Confluent at Confluent
Shaun is the CPO at Confluent and previously held CPO roles at MuleSoft and Metromile. He spent six years at Atlassian where he built the industry's first B2B growth team and is a creator of popular Reforge courses on retention and data.
🎙️ Episode Context
Shaun Clowes discusses the stagnation of the product management craft and how to elevate from average to top-tier by focusing outside the building. He explores the nuances of using AI for synthesis versus creation, arguing that data management is the real bottleneck for AI utility. Additionally, he shares his "Bingo Card" approach to career growth and why B2B SaaS moats lie in business workflows rather than UI.
Problem It Solves
Prevents PMs from making bad decisions based on "vanity metrics" or isolated positive experiment results that don't drive actual business value.
Framework Overview
When analyzing a metric change or experiment result, never look at the data point in isolation. You must analyze the context surrounding the data—where users came from, where they went, and the higher-level business impact.
🧠 Framework Structure
Check Upstream: Look at what happened...
Check Downstream: Look at what happen...
Click Up (Macro View): Look at the bu...
When to Use
Whenever an A/B test shows a positive result, or when observing an anomaly in product usage data.
Common Mistakes
Celebrating a localized lift (e.g., onboarding completion) without realizing it attracts low-quality users who churn later or pay less.
Real World Example
Shaun describes looking at an onboarding success metric, but upon 'clicking up', realizing that while retention improved slightly, the ASP (Average Sales Price) dropped, hurting revenue.
If you look at data as a way of giving you the answer, you're always wrong... Data is more like a compass than a GPS.
— Shaun Clowes