---
title: "Funnel analysis"
description: "Funnel analysis measures how users progress through an ordered sequence of steps toward a goal — such as viewing a product, adding it to cart, and checking out — identifying where the largest drop-offs occur."
canonical: "https://motherduck.com/glossary/funnel-analysis/"
related:
  - title: "MotherDuck Window Functions"
    url: "https://motherduck.com/blog/motherduck-window-functions-in-sql/"
  - title: "Salesforce | MotherDuck Docs"
    url: "https://motherduck.com/docs/integrations/ingestion/salesforce/"
  - title: "DuckLake Lakehouse: Getting Started to Going Fast | MotherDuck"
    url: "https://motherduck.com/videos/ducklake-lakehouse-getting-started/"
---

# Funnel analysis

> Funnel analysis measures how users progress through an ordered sequence of steps toward a goal — such as viewing a product, adding it to cart, and checking out — identifying where the largest drop-offs occur.

## Overview

A funnel is an ordered sequence of steps a user is expected to move through toward some goal: for example, "viewed product" -> "added to cart" -> "started checkout" -> "completed purchase." Funnel analysis measures, for a group of users, how many reach each step and, critically, the conversion rate between consecutive steps — which reveals exactly where the biggest drop-off happens, rather than just reporting an overall conversion rate from first to last step. This makes it a core technique in product and growth analytics for prioritizing where to invest in fixing friction.

## Ordered versus unordered funnels

Most funnel definitions require steps to happen in order for a given user (and often within some time window), which distinguishes funnel analysis from simply counting how many users ever performed each event — a user who adds an item to their cart before viewing the product page (e.g., via a shared link) may or may not count, depending on the funnel's rules.

## Computing a funnel in DuckDB

A common pattern uses conditional aggregation or window functions to find whether, and when, each user completed each step in order:

```sql
WITH steps AS (
    SELECT
        user_id,
        min(event_time) FILTER (WHERE event_name = 'viewed_product')   AS t_view,
        min(event_time) FILTER (WHERE event_name = 'added_to_cart')    AS t_cart,
        min(event_time) FILTER (WHERE event_name = 'completed_order')  AS t_order
    FROM events
    GROUP BY ALL
)
SELECT
    count(*)                                             AS total_users,
    count(*) FILTER (WHERE t_cart IS NOT NULL
                      AND t_cart > t_view)                AS reached_cart,
    count(*) FILTER (WHERE t_order IS NOT NULL
                      AND t_order > t_cart)                AS reached_order
FROM steps;
```

From there, step-to-step conversion rates are simple divisions, and `QUALIFY` or window functions can be layered in for more complex ordering or time-window constraints (e.g., "completed checkout within 24 hours of adding to cart").

## Why it matters

Funnel analysis turns a single conversion-rate number into a diagnostic: instead of just knowing that 2% of visitors buy something, it shows whether the problem is that people don't add items to cart, or that they add items but abandon at checkout — two very different problems requiring very different fixes.