Lab 05: Digital Analytics with GA4¶

Overview¶

Data is the lifeblood of e-commerce decision-making. Every pricing change, campaign launch, and UX redesign should be evaluated against metrics. In this lab you will explore the Google Analytics 4 Demo Account, analyze a simulated purchase funnel, compute key e-commerce KPIs, and write a data-driven recommendations report — the same workflow used by analytics teams at e-commerce companies.

Part A — GA4 Demo Account Setup (10 pts)¶

Step 1: Access the GA4 Demo Account 1. Go to https://analytics.google.com/analytics/web/demoAccount 2. Click "Access Demo Account" — this is Google's real analytics data for the Google Merchandise Store 3. Make sure you select the "GA4 - Google Merchandise Store" property (not the UA property)

Step 2: Navigate and orient yourself. Take screenshots of: - The Home overview panel - The Reports → Life cycle → Acquisition section - The Reports → Life cycle → Monetization → Purchase journey section

Step 3: Answer in analytics_report.md: 1. What is the current 30-day revenue? 2. What is the top traffic source by sessions? 3. What is the overall purchase conversion rate?

Part B — Purchase Funnel Analysis (30 pts)¶

Navigate to Reports → Life cycle → Monetization → Purchase journey.

This funnel shows:

Session Start → Product View → Add to Cart → Begin Checkout → Purchase

B1. Record the abandonment rate at each stage:

B2. Where is the biggest drop-off? What are 3 possible reasons for that specific drop?

B3. Navigate to Explore → Funnel exploration. Create a custom funnel with these events: - session_start - view_item - add_to_cart - begin_checkout - purchase

Set the breakdown dimension to Device category (mobile/desktop/tablet). Screenshot the result.

B4. Compare mobile vs desktop conversion rates. Which device converts better? What UX differences explain this gap?

Part C — Python Funnel Simulation (25 pts)¶

Simulate funnel analysis with Python using data modeled on typical e-commerce benchmarks:

# funnel_analysis.py
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches

# Simulated monthly funnel data (3 months)
data = {
    "month": ["Jan", "Feb", "Mar"],
    "sessions":      [125000, 138000, 142000],
    "product_views": [ 68000,  75000,  79000],
    "add_to_cart":   [ 18500,  21000,  23500],
    "checkout_start":[  8200,   9100,  10200],
    "purchases":     [  2460,   2730,   3060],
    "revenue":       [185400, 205600, 230400]
}

df = pd.DataFrame(data)

# ── Conversion rates ─────────────────────────────────────────────
df["view_rate"]     = df["product_views"]   / df["sessions"]      * 100
df["atc_rate"]      = df["add_to_cart"]     / df["product_views"] * 100
df["checkout_rate"] = df["checkout_start"]  / df["add_to_cart"]   * 100
df["purchase_rate"] = df["purchases"]       / df["checkout_start"]* 100
df["overall_cvr"]   = df["purchases"]       / df["sessions"]      * 100
df["aov"]           = df["revenue"]         / df["purchases"]

print("=== E-Commerce Funnel Analysis ===\n")
print(df[["month","sessions","purchases","overall_cvr","aov"]].to_string(index=False))
print(f"\nAverage CVR: {df['overall_cvr'].mean():.2f}%")
print(f"Average AOV: ${df['aov'].mean():.2f}")
print(f"Revenue MoM growth: {((df['revenue'].iloc[-1]/df['revenue'].iloc[0])-1)*100:.1f}%")

# ── Funnel visualization ─────────────────────────────────────────
fig, axes = plt.subplots(1, 2, figsize=(14, 6))

# Chart 1: Funnel chart (March data)
stages = ["Sessions", "Product Views", "Add to Cart", "Checkout", "Purchase"]
values = [df["sessions"].iloc[-1], df["product_views"].iloc[-1],
          df["add_to_cart"].iloc[-1], df["checkout_start"].iloc[-1],
          df["purchases"].iloc[-1]]
colors = ["#1a237e","#283593","#3949ab","#5c6bc0","#7986cb"]

ax1 = axes[0]
for i, (stage, val, color) in enumerate(zip(stages, values, colors)):
    width = val / values[0]
    ax1.barh(i, width, color=color, height=0.6)
    ax1.text(width + 0.01, i, f"{stage}: {val:,}", va="center", fontsize=9)

ax1.set_xlim(0, 1.4)
ax1.set_yticks([])
ax1.set_xlabel("Proportion of Sessions")
ax1.set_title("March Purchase Funnel")
ax1.invert_yaxis()

# Chart 2: CVR trend over 3 months
ax2 = axes[1]
ax2.plot(df["month"], df["overall_cvr"], marker="o", linewidth=2, color="#0f3460", label="Overall CVR")
ax2.plot(df["month"], df["atc_rate"], marker="s", linewidth=2, color="#c8a951", label="Add-to-Cart Rate")
ax2.set_title("Conversion Rate Trends")
ax2.set_ylabel("Rate (%)")
ax2.legend()
ax2.grid(True, alpha=0.3)

plt.tight_layout()
plt.savefig("funnel_analysis.png", dpi=150)
print("\nFunnel chart saved: funnel_analysis.png")

# ── Revenue impact calculator ────────────────────────────────────
print("\n=== Revenue Impact Calculator ===")
baseline_cvr = df["overall_cvr"].iloc[-1]
baseline_rev = df["revenue"].iloc[-1]
sessions     = df["sessions"].iloc[-1]
aov          = df["aov"].iloc[-1]

for improvement in [0.1, 0.2, 0.5]:
    new_cvr = baseline_cvr * (1 + improvement)
    new_purchases = sessions * new_cvr / 100
    new_revenue = new_purchases * aov
    uplift = new_revenue - baseline_rev
    print(f"  CVR +{improvement*100:.0f}%: ${uplift:,.0f} additional monthly revenue")

Run and include output + chart in submission.

Part D — KPI Dashboard Design (25 pts)¶

Design a one-page KPI dashboard for a hypothetical e-commerce director's weekly review. Write analytics_report.md with:

1. KPI Selection — justify choosing exactly these 8 metrics (explain what each measures and why it matters):

2. Alert Thresholds — for each KPI, define a "yellow flag" and "red flag" threshold that would trigger investigation

3. Week-over-Week Narrative — using your Part B GA4 data and Part C simulation data, write a 300-word executive summary as if presenting to the director: - What's working - What needs attention - Recommended actions for next week

4. Instrumentation Gaps — what data does your dashboard NOT have that would make it more valuable? List 3 missing signals and how you'd collect them.

Part E — Market Research Tool (10 pts)¶

Use Google Trends (trends.google.com) to research seasonality for one product category.

1. Search for two competing products/brands in the same category
2. Set the time range to 5 years, geography to United States
3. Export the data as CSV
4. Load it in Python and plot both trends on the same chart

In analytics_report.md, answer: - What seasonal pattern do you observe? - When should an e-commerce store in this category ramp up ad spend? - What is the year-over-year trend (growing, stable, declining)?

Submission Checklist¶

• [ ] GA4 demo account screenshots (Home, Acquisition, Purchase Journey)
• [ ] funnel_analysis.py — runs, outputs analysis + chart
• [ ] funnel_analysis.png — 2-panel chart
• [ ] analytics_report.md — Parts A, D, E complete
• [ ] Google Trends chart (Python-generated)

Grading¶