Cohort & Funnel Analysis · User Journeys in GA4

Standard GA4 reports show aggregate metrics — total users, average engagement time, overall conversion rate. These aggregate views are useful for monitoring trends but hide the most important patterns: when do users fall away, and where do they stop converting?

Cohort analysis breaks aggregate retention into time-based groups, revealing whether user behaviour is improving or deteriorating over time — and whether the users acquired through different channels have different retention profiles. Funnel analysis breaks the conversion journey into steps, revealing not just that "70% of users who begin checkout don't complete it" but exactly which step (payment entry, shipping selection, coupon field) causes the most abandonment. The specific step matters because the fix for each step is different.

Both analyses move from "what" to "where" and "when" — and those more precise answers are what drive the right optimisation decisions rather than guesswork improvements to the overall experience.

A cohort is a group of users who share a common characteristic at a specific point in time — typically users who were first acquired during a specific week or month. Cohort analysis tracks what these users do in subsequent periods: what proportion of users acquired in week 1 returned in week 2? Week 4? Week 8? This retention pattern over time is the cohort chart.

Why retention by cohort rather than overall retention? Because overall retention is an average of all users at different stages of their lifecycle, which can produce misleading numbers. A business could show stable "average retention" while actually declining in retention for new users — if early cohorts have high retention that offsets the declining retention of more recent ones. Cohort analysis makes this visible by separating the retention data by acquisition period.

In GA4: Explore → New Exploration → Cohort Exploration template (or start blank and select Cohort chart technique).

Key configuration options

• Cohort inclusion criterion. The event that defines cohort membership — typically first_visit (users grouped by first visit date) or first_open for apps. For e-commerce, you might define cohorts by first purchase date rather than first visit.
• Return criterion. The event that defines a "return" — typically any_active_users (any subsequent session), or a specific event like purchase (for e-commerce cohort analysis of repeat purchase behaviour).
• Cohort granularity. Daily, weekly, or monthly cohorts. Weekly is most informative for most businesses — daily cohorts have too much noise; monthly cohorts obscure important weekly patterns.
• Metric. Active users (count of users who returned) or conversion rate (proportion who returned). Both are useful — active users shows absolute volume; rate shows the proportion, which is comparable across cohorts of different sizes.

A cohort retention table has cohorts as rows and time periods (weeks or months after acquisition) as columns. Each cell shows the proportion of users in that cohort who returned in that period.

What patterns to look for

A funnel analysis defines a sequence of steps in the conversion journey and measures the proportion of users who progress from each step to the next. The output is a visualisation showing the absolute count and drop-off percentage between each step.

Open vs closed funnels

The most important funnel configuration decision is open vs closed:

• Closed funnel: users must complete each step in sequence, in the order defined, without visiting other steps out of order. If a user visits step 3 before step 2, they are excluded from the funnel. This shows the proportion of users who follow the exact intended journey.
• Open funnel: users can complete steps in any order, and can skip steps. A user who visits step 3 directly without step 2 is still counted at step 3. This shows how many users reach each step by any path — not just the intended one.

For most e-commerce funnels (cart → checkout → payment → confirmation), closed funnels are more meaningful — you want to know the conversion rate of the intended sequential journey. For informational funnels (article → pricing page → contact form), open funnels may be more appropriate since users may arrive at any step from different sources.

In GA4: Explore → New Exploration → Funnel Exploration template. Define each step using the step condition options:

• Event name. A specific GA4 event — e.g. "view_item" for the product page step, "add_to_cart" for the cart step, "purchase" for the conversion step.
• Page location contains / matches. A specific URL pattern — e.g. "page_location contains /checkout/" for the checkout step. Useful when the journey is page-based rather than event-based.
• Compound conditions. Multiple conditions for a single step using AND — e.g. "event is form_submit AND page_location contains /contact/". This prevents false step completions from form submissions on unrelated pages.

Name each step descriptively ("1. Product Page" rather than "Step 1") — descriptive names make the funnel chart immediately readable without a legend. Add as many steps as there are meaningful conversion stages — typically 3–6 steps captures the key drop-off points without becoming unwieldy.

Funnel drop-off reveals where to prioritise optimisation. The steps where the most users drop off are the highest-leverage points — improving conversion from 30% to 40% at a step through which 10,000 users pass adds 1,000 additional users to the next step; the same 10 percentage point improvement at a step through which only 100 users pass adds only 10 users.

Prioritise by: absolute drop-off count (how many users are lost at this step); relative drop-off rate (what percentage of users who reached this step did not proceed); and the value of each conversion (a 1% improvement at the payment step is worth more if the average order value is £500 than if it is £20). The product of these three factors gives an effort-adjusted prioritisation framework.

The most analytically powerful use of funnel analysis is comparing funnel performance between user segments. GA4 allows applying two comparison segments to a funnel, displaying the funnel side-by-side for each segment. This answers: is the drop-off equally distributed, or is one segment performing significantly worse?

Useful funnel segment comparisons:

• Mobile vs desktop — if mobile drop-off is significantly higher at a specific step, the mobile UX at that step needs specific attention
• New users vs returning users — returning users typically convert at higher rates; a large gap suggests onboarding or trust building issues for new users
• Traffic channel segments — which acquisition channels produce users who convert most efficiently through the funnel? Do paid search users drop off at different steps than organic users?
• Geographic segments — for businesses with international audiences, do specific countries show unusually high drop-off at the shipping or payment steps?

Funnel analysis tells you where users drop off; path analysis tells you where they go instead. After identifying a high drop-off step in the funnel, use GA4's Path Exploration to examine the forward paths from that step's page or event — specifically looking at what non-converting users do when they abandon the funnel.

If 60% of users who reach the payment page do not complete the purchase, path analysis of the payment page's forward paths reveals: do they go back to the cart? To a different product page? To an FAQ? To the delivery information page? Each destination suggests a different problem — returning to the cart suggests payment friction; going to FAQ suggests information gaps; going to delivery info suggests shipping cost concerns. The path data transforms an abstract drop-off percentage into a specific investigatable hypothesis.

The purpose of cohort and funnel analysis is not the analysis itself — it is the actions it informs. Specific action frameworks:

From cohort findings

• Low Week 1 retention → implement or improve welcome/onboarding email sequence; review first-session experience for new users
• Declining cohort quality → audit traffic sources for quality changes; investigate whether landing pages have changed; review product or UX changes in the same period
• Strong retention for specific cohorts → identify what was different about their acquisition or onboarding and systematically replicate it

From funnel findings

• High drop-off at form/checkout step → A/B test form simplification; add trust signals (security badges, return policy) at the drop-off point; test alternative payment methods
• High drop-off at product/pricing page → test clearer value communication; add social proof; review price presentation
• Mobile-specific drop-off → audit the mobile UX at the specific step with a real device; test form field accessibility and keyboard behaviour on mobile