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Funnel analysis

| Article: 3 min read | Analytics

Funnel analysis is counting how many visitors get through each step of the path to purchase (product page → cart → checkout → payment) and at which step the most of them "drop off." Its goal is to find the biggest loss and check why it happens.

In plain words

A funnel shows not "why" people leave but "where." Knowing that out of 1,000 people who added a product to the cart only 150 reached payment, you understand where to look next: the checkout form, delivery, payment. The cause is then looked for in other ways: session recordings, a survey, a usability check.

How to build one

  1. Define the steps. Usually: `view_item` → `add_to_cart` → `begin_checkout` → `purchase`. The events must be set up correctly (E-commerce events).
  2. Choose the funnel type. GA4 has open and closed funnels: in an open one users can enter at any step, in a closed one only at the first. Users are counted only at the steps they completed in the set order (Google Analytics Help).
  3. Break it down by segments. GA4 lets you break down each step by a dimension, for example device category; each user is assigned to the first value they have. You can also show the average time between steps.
  4. Find the step with the biggest loss and compare it by device, traffic source, new and returning shoppers.

Funnel data in GA4 Explorations depends on the retention period (up to 14 months for a standard property), see GA4 for a store (Google Analytics Help).

Example (illustrative numbers)

An electronics online store over a month:

Step

Users

Move to the next step

Product view

20,000

—

Added to cart

1,800

9%

Started checkout

900

50%

Purchased

400

44%

After the cart 50% of people are lost, and after starting checkout 56% (500 out of 900). So in this example the biggest loss is at checkout. If this step is worse on mobile than on desktop, check the mobile form first.

How different sources see it

  • Baymard Institute compiles the results of 50 studies: the average cart abandonment rate is 70.22% (September 2025 update). Of these, 42% of respondents answered "just browsing" when asked about the reasons. Other reasons include extra costs that were too high (40%), a requirement to create an account (18%), a checkout that was too long or complicated (17%), site errors (17%), an unclear total (12%) (Baymard, 2025). This is a survey of American shoppers; Baymard gives no separate data for Ukraine. Do not take 70% as a norm to "reach": some people in the cart are only comparing prices.
  • GA4 and Microsoft Clarity have their own funnel tools; their results may differ because of different rules for counting users and cookie consent. In Clarity, without consent sessions are split into separate ones, so funnels are less complete (Microsoft documentation).

Common mistakes

  • Looking at overall conversion and not breaking it down by device and source.
  • Comparing your abandonment rate with someone else's average without accounting for the type of product (an expensive product takes more thought).
  • Looking for the cause of a loss only in the numbers. A funnel shows the place, not the cause.
  • Changing several things at once and then being unable to tell what worked: an A/B test is better.

FAQ

What is a sales funnel in an online store? The sequence of steps from the first view to purchase, with the number of people at each step.

What is the average cart abandonment? According to Baymard's compilation (updated September 2025), 70.22% across the stores studied. For your store the benchmark is your own trend, not this figure.

How do an open and a closed funnel differ? A closed funnel counts only those who started at the first step; an open one also counts those who entered later.

How much data do you need? Enough that the difference between steps does not change randomly from day to day. For a small store it is better to look at a month, not a week.

Sources

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