Google Analytics

Portfolio Project: Google Analytics Insights for Google Merchandise Store

Project Overview

For this project, I used Google Analytics to gather insights on user acquisition, engagement, and monetization for the Google Merchandise Store. The goal was to analyze key performance metrics and provide data-driven insights to improve marketing and sales strategies.

1. Acquisition Metrics

📊 Key Findings:

• Google was the largest driver of new users (32.23%).

• Bing and Baidu each contributed less than 1% of new users.

• YouTube had minimal impact (only 3 users), indicating a need for better YouTube marketing strategies.

💡 Recommendations:
✔ Optimize for Google Search: Since Google drives most new users, invest in SEO & Google Ads.
✔ Reassess YouTube Marketing: Explore video ads & influencer partnerships to improve traffic.
✔ Expand Bing & Baidu Efforts: Bing & Baidu contribute low traffic—consider localized paid search ads for international reach.

2. Engagement Metrics

📊 Key Findings:

• Cart abandonment is high (714 users abandoned checkout).

• Sales conversion rate is only 2.56%, indicating room for improvement.

• Many users engage but don’t convert, showing potential friction in the checkout process.

💡 Recommendations:
✔ Improve Checkout Process: Simplify checkout steps & offer guest checkout.
✔ Abandoned Cart Emails: Send reminders with limited-time discounts to recover lost sales.
✔ Optimize for Mobile Users: Ensure smooth checkout for mobile shoppers.

3. Monetization Metrics

📊 Key Findings:

• Total revenue reached $94K.

• First-time purchasers were 678, indicating steady new customer acquisition.

• Google-branded items (stickers & pens) were top sellers, suggesting strong brand loyalty.

💡 Recommendations:
✔ Expand Popular Merchandise: Create more Google-branded products based on top sellers.
✔ Run First-Time Buyer Promotions: Offer discounts & incentives to encourage repeat purchases.
✔ Upsell & Cross-Sell: Bundle best-selling stickers with other office accessories.

4. Summary & Action Plan

🔹 Acquisition: Google is the primary source—focus on SEO & Google Ads.
🔹 Engagement: Cart abandonment is high—optimize checkout & introduce retargeting emails.
🔹 Monetization: Google-branded items dominate sales—expand product line & offer bundles.

📌 Next Steps:
✔ Launch targeted ads for Google & Bing
✔ Optimize checkout UX & send cart recovery emails
✔ Expand Google-branded product collection

What I Learned

This project gave me hands-on experience in analyzing website performance using Google Analytics, specifically for the Google Merchandise Store. I learned how to:

✅ Extract key acquisition, engagement, and monetization metrics to understand user behavior.
✅ Analyze user acquisition sources to see which search engines drive the most traffic.
✅ Evaluate customer engagement through session, checkout, and purchase events to assess conversion rates.
✅ Calculate cart abandonment and sales conversion rates to identify potential drop-off points in the customer journey.
✅ Identify top revenue-generating products to understand sales performance.

This exercise reinforced how data-driven insights can optimize digital marketing strategies and improve user experience.

Mistakes I Made & What I Learned from Them

🔴 Overlooking the significance of smaller traffic sources

• Initially, I focused mostly on Google as the top traffic source and ignored smaller contributors like Bing and Baidu.

• Lesson: Even small traffic sources can impact conversions, and diversifying acquisition strategies can be beneficial.

🔴 Not considering user intent in acquisition metrics

• At first, I assumed more traffic meant higher conversions, but I later realized that not all traffic converts at the same rate.

• Lesson: Traffic quality matters more than quantity—some users may browse but not purchase, so refining acquisition strategies is key.

🔴 Misinterpreting session_start data

• Initially, I didn’t account for how sessions differ from unique users, which led to overestimating actual user engagement.

• Lesson: Session data should be analyzed alongside engagement and conversion metrics to get a full picture of user behavior.

How I Improved

🔹 Looked beyond surface-level data – Instead of focusing solely on total new users, I analyzed how different traffic sources contributed to engagement and revenue.
🔹 Refined insights on conversion rates – By comparing begin_checkout vs. purchase events, I was able to better understand cart abandonment issues.
🔹 Connected acquisition and engagement data – I learned that not all traffic sources lead to high conversions, which helped me make better recommendations for future campaigns.
🔹 Improved segmentation skills – I filtered data by search engines, events, and purchase behavior to uncover actionable trends rather than just reporting numbers.