Funnel Analysis on Google Analytics Data

Tatevik Khachatryan

Completed work

Data Analyst

Dash Plotly

Google Analytics

Google BigQuery

E-Commerce

Funnel Analysis on Google Analytics Sample Data

🧠 Project Overview

Two Types of Funnels: Standard vs Fast-Track

📱 Device-Level Funnel Drop-Off

🌍 Funnel Drop-Offs by Country

🌐 Drop-Offs by Browser

🧠 Project Overview

This project uses Google Analytics session data from BigQuery's public dataset GA_Sessions_Demo to perform a funnel analysis across key ecommerce steps:

Landing Page → Product Page → Cart → Checkout → Thank You

With over 10,000 sessions, this dataset offers a rich opportunity to identify conversion drop-offs, assess user behavior by segment, and suggest UX and technical improvements based on actual funnel performance.

Defining the Funnel

The raw GA data doesn't come with ready-made funnel steps, so I had to build them manually using the pagePath column. I tried two approaches:

Manual Keyword Mapping

I initially built a dictionary mapping funnel steps to specific pagePath patterns (e.g., Cart → /basket.html, Thank You → /ordercompleted.html).

However, this approach lost over half of the sessions due to overly narrow matching.

Smarter Path Classification

To fix this, I implemented a classifier function using regex and keyword heuristics.

It labeled each pagePath into funnel steps, then aggregated at the session level.

def classify_page_path(path):
    if 'basket' in path or 'cart' in path:
        return 'Cart'
    elif 'checkout' in path or 'payment' in path or 'signin' in path or 'yourinfo' in path or 'revieworder' in path:
        return 'Checkout'
    elif 'ordercompleted' in path:
        return 'Thank You'
    elif any(x in path for x in ['apparel', 'bags', 'accessories', 'electronics', 'drinkware', 'notebooks']):
        return 'Product Page'
    elif any(x in path for x in ['home', 'store', 'search', 'quickview']):
        return 'Landing Page'
    else:
        return 'Other'
results['step_type'] = results['pagePath'].apply(classify_page_path)

This method captured significantly more sessions and improved funnel fidelity.

Two Types of Funnels: Standard vs Fast-Track

While analyzing, I noticed 114 sessions went straight to Checkout, skipping Cart entirely — likely due to quick-buy buttons or direct checkout links. So I split the funnel into two paths:

📉 Fast-Tracked Funnel Results

Fast-tracked sessions had 0 completions, suggesting:

Users dropped off mid-checkout

Something broke in the quick checkout flow Or possibly bot/misclassified sessions

Suggestion: Investigate fast-track checkout UX and session origins. Consider bot filtering.

📉 Standard Funnel Results

From 1574 standard sessions:

1086 reached a Product Page

252 reached the Cart (76% drop-off)

51 reached the Thank You page

That’s a 3.2% overall completion rate.

Suggestions:

Improve Cart transition UX (e.g., clearer CTAs, trust badges)

Simplify checkout forms & reduce steps

Run A/B tests on Product Page layout and CTA visibility

📱 Device-Level Funnel Drop-Off

Segmenting by deviceCategory revealed huge behavioral differences.

🔍 Insights:

Mobile conversion = 0.5%, vs Desktop = 4.2%

Tablet = 0% conversion

Suggestions:

Prioritize mobile UX and error tracking

Consider deprioritizing tablets unless traffic increases

🌍 Funnel Drop-Offs by Country

Using country-level segmentation, I found that while the US has the highest drop-off volume, international users like Germany, India, and the UK show higher drop-off rates, pointing to potential trust or localization issues.

Suggestions:

Run localized A/B tests on product + cart flow

Add country-specific shipping info and currency displays

🌐 Drop-Offs by Browser

I categorized 15+ browsers into High and Low volume groups.

Among High-volume browsers:

Chrome:

Largest drop-off volume across the funnel

654 users dropped from Product → Cart

191 dropped at Checkout → Thank You

Safari & Firefox:

Drop-off rates > 80% at both Cart and Checkout stages

Internet Explorer:

100% abandonment at Checkout

Likely poor compatibility or broken UX

Common Drop-Off Issues:

Tracking gaps (negative drop-offs → session stitching issues)

Checkout flow inconsistencies

Cart not tracked properly

Suggestions:

QA checkout pages in Firefox, Safari, and IE

Improve session stitching and tag firing reliability

Final Takeaways

✅ Mapped a 5-step ecommerce funnel across 10K+ GA sessions

✅ Identified drop-offs by segment: device, country, browser

✅ Created actionable insights for product, UX, and data teams

✅ Built two funnel models: Standard and Fast-Track

✅ Used BigQuery + Colab for data processing and Plotly for visualization

Got thoughts, feedback, or just want to say hi? Drop me a “Hi 👋” on LinkedIn.

Like this project

Completed work

Posted May 20, 2025

Performed funnel analysis on GA data to identify conversion drop-offs and suggest improvements.

Likes

Views

Timeline

Apr 10, 2025 - Apr 20, 2025

Funnel Analysis on Google Analytics Data

Funnel Analysis on Google Analytics Sample Data

🧠 Project Overview

Two Types of Funnels: Standard vs Fast-Track

📱 Device-Level Funnel Drop-Off

🌍 Funnel Drop-Offs by Country

🌐 Drop-Offs by Browser

Final Takeaways

Join 50k+ companies and 1M+ independents