See where users drop off and why.
Doe queries your database, builds conversion funnels from raw events, and compares what users who convert do differently from those who don't. The analysis runs as real code in a sandbox you can inspect and modify. Results are ranked by effect size so you know what to act on first.
Doe builds conversion funnels from raw event data in your Postgres or MySQL database, identifies which step has the steepest drop-off, and compares feature adoption between users who convert and those who don't. Connects to Neon, Supabase, and PlanetScale. The analysis runs as inspectable Python code in a sandbox, not a black-box dashboard. Results are ranked by effect size so you know what to act on first.
What changes
| Dimension | Before | With Doe |
|---|---|---|
| Depth of analysis | Funnel chart shows where users drop, not why | Behavioral comparison showing which features and actions differentiate converters |
| Segmentation | One segment at a time, each requiring a new chart | Every segment computed in one run: channel, plan, device, cohort |
| Auditability | A chart with no source code or query behind it | Every query and every line of analysis code visible and editable |
| Actionability | "Activation is 34%." OK, but what do we do? | Specific features and actions ranked by how much they differentiate converters. |
How Doe analyzes your funnel and user behavior
NeonEvents grouped by user ID for the analysis window. SQL visible in the execution log.
Doe computes conversion rates at each stage, identifies the steepest drop-off, and segments by channel, plan type, and device. Code inspectable and editable.
Doe computes adoption rates for each feature among both groups and ranks differences by effect size. The biggest gaps surface first.
SlackDoe delivers which funnel step has the biggest drop, which behaviors most differentiate converters, and how that varies by segment. Full data and analysis code attached.
Activation is 34%. The product team wants to know why.
The head of product shows you the Amplitude chart: 34% of signups reach the activation milestone. "Why are we losing the other 66%?" Answering properly means querying every event between signup and activation for three months of users, building a step-by-step funnel, segmenting by channel, plan type, and device, then comparing the event sequences of users who activated versus those who didn't.
In Amplitude, you can build a basic funnel in 20 minutes. The moment you need to go deeper (compare specific feature adoption between converters and non-converters, or re-segment by a dimension Amplitude doesn't track), you're back in SQL and notebooks. By the time you deliver, the product team already shipped a change based on gut feel.
Get started in under 10 minutes
Connect your tools
One-click OAuth for each integration. No API keys, no engineering.
Describe what you need
“Analyze our signup-to-activation funnel: landed on pricing, started trial, created first project, invited a teammate. Show where users drop off and compare conversion rates by referral source.”
It runs on schedule
Updated analysis lands in your team channel every month, with on-demand reruns after product changes.
Funnel & Behavior Analysis FAQ
Doe runs against your raw event data with your definitions, so you're not limited by what your analytics tool tracks or how it segments. The analysis runs as code you can inspect, edit, and extend. And the behavioral comparison (what do converters do differently?) goes beyond what most product analytics dashboards offer out of the box.
Stop doing the work your tools should do for you.
Set it up once. Doe runs it every time.
