If Your Funnel Looks Like a Funnel, You’re Doing It Wrong

‍The Problem With Static Funnels

Funnels are the darling of dashboards. Everyone from CMOs to interns gets seduced by their simplicity; wide at the top, narrow at the bottom, with one big, bold red drop-off in the middle screaming “FIX ME.”

But here’s the truth:
That funnel? It’s a lie.

Why?

1. It’s a snapshot, not a story.
   It shows you where people drop off. It doesn’t tell you when, why, or how that’s changed over time.
2. It hides context.
   You see Step 3 bleeding users. But maybe Step 2 is where the real issue began. Maybe the cause lives upstream, and the funnel just spits out symptoms.
3. It assumes independence between stages.
   Each stage looks isolated, like fixing one part won’t ripple through the rest. In reality, funnel stages are entangled. Improving Add-to-Cart might improve Checkout starts two weeks later. A static funnel will never show you that.
4. It creates false urgency.
   “This drop is biggest, so fix it.”
   That logic only works if each stage is a sealed chamber. It’s not. And acting on one stage without understanding the ecosystem can lead to wasted sprints and bad bets.

Real funnels aren’t static. Real funnels breathe.
And if your visualisation doesn’t account for time, causality, and progression flow, you’re limiting the insights you could return.

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‍The Trap of Over-Simplification

Here’s how the trap works:

You build a neat little funnel:

• Page view
• Product view
• Add to cart
• Checkout
• Purchase

You run it, and there it is - 60% drop-off at “Add to cart.”
Cue the Slack ping: “Can we optimise this step?”
Cue the PM: “Let’s A/B test the button.”

But this might be a bad idea... Because that drop-off?
Might be normal, seasonal, or caused by a marketing campaign two weeks ago that drove unqualified traffic.
A funnel doesn’t tell you any of that.
It just hands you a percentage.

Funnels Ignore Trajectory

You don’t know if your conversion rate is:

• Improving
• Declining
• Volatile
• Responding to changes upstream

There’s no momentum, no direction, no diagnosis.
Just a number with no context.

Funnels Assume Users Are Water

Funnels suggest that users flow naturally from top to bottom. But they don’t.
They bounce around. They skip steps.
They might come back days later, on a different device, logged out.
None of that’s captured in a classic funnel.

You're not looking at behaviour. You’re looking at a moment in time.

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Time-Series Funnel Progressions (The Better Weapon)

Let’s stop pretending the funnel is a diagnostic tool.
It’s not. It’s a summary.

If you want to understand behaviour, momentum, and causality, you don’t need a funnel.
You need a line graph.

Here’s what you do instead:

1. Take each stage of the funnel.
   Not just the drop-off percentages, but the absolute number of users reaching each stage.
2. Plot each stage as a separate line over time.
   One line for page views.
   One for product views.
   One for add-to-carts.
   One for checkouts.
   One for purchases.
3. Now watch.
   • See where each stage trends.
   • See when one line diverges from the others.
   • See how a lift in one stage cascades to others.
   • Spot the lag between improvement and downstream impact.

The beauty of the line graph:

• You can compare stages directly.
  If Add-to-Cart is stable but Checkout drops mid-month, you don’t waste time on product pages.
• You see cause and effect.
  Fix the product page. A week later, cart starts to lift. Then checkout lifts.
  Funnels would’ve missed all of that.
• You detect time-based patterns.
  Maybe Add-to-Cart always slumps on weekends. Maybe Checkout breaks every time there’s a sitewide discount.
  Funnels flatten those truths.

It’s not a funnel. It’s a system.

A system where stages talk to each other. Where behaviour ripples across steps.
A line graph shows the dialogue between steps.

You want to make smarter decisions?
Stop asking “Where do they drop off?”
Start asking “How do the steps impact each other?”

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Why the Line Graph Approach Wins

A funnel gives you one number per stage.
A line graph gives you a timeline of behaviour.

1. You See Systemic Impact of Changes

Funnels highlight one thing:

“Drop-off here! Fix this step!”

But line graphs reveal relationships:

“When product views increase, add-to-carts lag two days behind, but only when traffic is organic.”

You’re now understanding relationships.

2. You Spot the Timing of Breakdowns and Wins

Funnels don’t tell you when something went wrong.
Line graphs do.

Example:

• On June 14, checkouts cratered.
• On June 13, devs shipped a layout change.
• On June 15, carts bounced back after a hotfix.

Funnels would show a generic drop.
Line graphs show the moment something happened.

3. You Detect Upstream Problems

If Purchases drop, funnels send you to the last step.
Line graphs often show it started way earlier, maybe during Product View, or even in the targeting of traffic.

4. You Learn How Changes Effect Multiple Areas

Run a CRO test?
Funnels will tell you if purchases went up.
Line graphs will tell you:

• Did more people reach Add-to-Cart?
• Did checkout conversion stay steady?
• Did one stage improve while another collapsed?

You don’t just see the result. You see the side effects.

5. You Think in Trends

Funnels are obsessed with now.
Line graphs train you to spot:

• Weekly rhythms
• Campaign lag
• Platform shifts (e.g., mobile vs desktop behavior)
• Natural vs forced conversion paths

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Making It Actionable

Step 1: Define Your Funnel Stages Clearly

Before you trend anything, tighten your definitions:

• Stage 1: page_view (e.g. product page)
• Stage 2: view_item
• Stage 3: add_to_cart
• Stage 4: begin_checkout
• Stage 5: purchase

Step 2: Get Daily Counts Per Stage

In GA4 BigQuery, your query should look like:

SELECT
  event_date,
  event_name,
  COUNT(DISTINCT user_pseudo_id) AS users
FROM `your_project.analytics_your_property.events_*`
WHERE event_name IN ('view_item', 'add_to_cart', 'begin_checkout', 'purchase')
GROUP BY event_date, event_name
ORDER BY event_date, event_name

Step 3: Visualise As a Multi-Line Graph

Use Looker Studio, Tableau, or Python/JS for visuals.
Each line = one funnel stage.
X-axis = time.
Y-axis = user count or sessions.

What to look for:

• When do lines diverge?
• Which lines are flat vs volatile?
• Do downstream stages rise/fall after changes upstream?

Step 4: Overlay Campaigns, Tests, and Changes

Add annotations:

• Paid campaigns
• Landing page changes
• Checkout redesign
• Consent banner updates

Optional: Add Conversion Rates Between Stages

Create derived metrics:

• Add-to-Cart Rate = add_to_cart ÷ view_item
• Checkout Rate = begin_checkout ÷ add_to_cart
• Purchase Rate = purchase ÷ begin_checkout

Flat is the new sharp.

So flatten the funnel.
Stretch it out.
Plot it over time.
And finally, understand what’s really happening in your user journey.

Because once you do?

You’ll never look at a triangle the same way again.

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