Funnel and conversion analytics is the discipline of mapping sequential user actions toward a defined goal, measuring how many users complete each step, and diagnosing where and why they drop off. It sits at the core of product analytics, marketing measurement, and revenue operations β giving teams a precise vocabulary for what "conversion" means and a structured method for improving it. Unlike high-level metrics that tell you something changed, a well-built funnel tells you where it changed, for which segment, and by how much. The key mental model to carry into every funnel analysis: aggregate numbers lie β always segment before acting, and always anchor your window logic to real user behavior rather than calendar convenience.
What This Cheat Sheet Covers
This topic spans 13 focused tables and 100 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.
Table 1: Funnel Core Concepts and Definitions
The vocabulary of funnel analytics is deceptively precise. Terms like "conversion rate," "drop-off," and "step" have exact meanings that determine what your data actually measures β getting them wrong produces misleading reports.
| Concept | Example | Description |
|---|---|---|
Steps: Visit β Sign Up β Activate β Purchase | β’ An ordered sequence of user actions (events) leading to a defined goal β’ the "funnel" shape emerges as users fall off at each step | |
event_name = 'Add to Cart' for step 3 | β’ A specific, trackable user action that represents a meaningful decision point β’ steps should reflect active user choices, not passive page loads | |
Step 2: 142 of 593 = 24% | β’ Percentage of users completing the previous step who also complete the current step β’ the inverse of drop-off rate | |
6 purchases from 593 visitors = 1% | β’ Percentage of users who entered the funnel at step 1 and completed the final step β’ the headline KPI, but least actionable on its own | |
1 β (142 / 593) = 76% drop-off | β’ Percentage of users who reached a step but did not complete it β’ more useful than conversion rate for identifying problems | |
Step 2 loses 451 users vs. step 4 losing 121 | β’ Raw count of users lost at each step β’ prioritize fixes by absolute loss, not percentage β higher volume losses have greater business impact |