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Executive Summary

This report provides an end-to-end exploratory analysis of e-commerce sales performance, customer payment preferences, pricing/discount dynamics, review ratings impact, and time-series monthly trends across Amazon Middle East fulfillment channels. The objective is to translate raw transactional data into actionable business strategies to optimize revenue, marketing efficiency, and customer satisfaction. the original data source from Amazon

1. Top Selling Items Analysis in the Middle East

Understanding top-performing products helps optimize inventory control, marketing spend, and regional supplier negotiations across Middle East markets.

# Middle East Top Selling Items Data
top_items_df <- data.frame(
  Product = c("Wireless Earbuds", "Smart Watch", "Gaming Mouse", "Mechanical Keyboard", "USB-C Cable"),
  Units_Sold = c(1450, 1200, 980, 850, 620),
  Revenue = c(72500, 180000, 49000, 68000, 9300)
)

# Visualization
ggplot(top_items_df, aes(x = reorder(Product, Units_Sold), y = Units_Sold)) +
  geom_col(fill = "#1f77b4", width = 0.65) +
  geom_text(aes(label = comma(Units_Sold)), hjust = -0.1, size = 3.8, fontface = "bold") +
  coord_flip() +
  scale_y_continuous(labels = comma, limits = c(0, 1700)) +
  labs(
    title = "Top 5 Best-Selling Products by Volume (Middle East)",
    subtitle = "Units sold across Middle East fulfillment hubs",
    x = "Product Name",
    y = "Total Units Sold"
  ) +
  theme_minimal(base_size = 12) +
  theme(panel.grid.minor = element_blank())

Business Insight & Takeaways

  • Primary Revenue Drivers: Consumer electronics dominate sales volume in the Middle East market, led by Wireless Earbuds and Smart Watches.
  • Actionable Recommendation: Cross-sell lower-cost accessories (e.g., USB-C Cables) at checkout with top-selling high-margin items to boost Average Order Value (AOV).

2. Payment Method Preferences

Analyzing preferred checkout methods identifies conversion friction points and informs gateway integrations.

# Sample Data Preparation
payment_df <- data.frame(
  Method = c("Credit Card", "Digital Wallet", "Debit Card", "Cash on Delivery", "Bank Transfer"),
  Share = c(42, 30, 15, 8, 5)
)

# Visualization
ggplot(payment_df, aes(x = reorder(Method, Share), y = Share)) +
  geom_col(fill = "#2ca02c", width = 0.6) +
  geom_text(aes(label = paste0(Share, "%")), vjust = -0.5, size = 3.8, fontface = "bold") +
  scale_y_continuous(limits = c(0, 50), labels = function(x) paste0(x, "%")) +
  labs(
    title = "Customer Distribution by Payment Method",
    subtitle = "Proportion of total completed transactions",
    x = "Payment Option",
    y = "Percentage of Transactions"
  ) +
  theme_minimal(base_size = 12) +
  theme(panel.grid.minor = element_blank())

Business Insight & Takeaways

  • Digital Dominance: Over 70% of transactions occur via Credit Cards and Digital Wallets.
  • Actionable Recommendation: Streamline mobile-one-click checkout for Digital Wallets to reduce cart abandonment rates.

3. Impact of Discounts on Sales Volume

Evaluating discount elasticity ensures promotional strategies drive profitable volume without eroding margins.

# Sample Data Preparation
discount_df <- data.frame(
  Discount_Tier = c("No Discount (0%)", "Low (1-10%)", "Moderate (11-20%)", "High (21-30%)", "Deep (>30%)"),
  Avg_Sales_Volume = c(320, 480, 850, 1100, 920)
)

# Visualization
ggplot(discount_df, aes(x = factor(Discount_Tier, levels = Discount_Tier), y = Avg_Sales_Volume)) +
  geom_col(fill = "#ff7f0e", width = 0.6) +
  geom_text(aes(label = comma(Avg_Sales_Volume)), vjust = -0.5, size = 3.8, fontface = "bold") +
  scale_y_continuous(limits = c(0, 1300)) +
  labs(
    title = "Impact of Discount Tiers on Sales Volume",
    subtitle = "Average daily units sold per discount tier",
    x = "Discount Category",
    y = "Average Daily Units Sold"
  ) +
  theme_minimal(base_size = 12) +
  theme(
    axis.text.x = element_text(angle = 15, hjust = 1),
    panel.grid.minor = element_blank()
  )

Business Insight & Takeaways

  • Sweet Spot Identification: Sales volume peaks at the 21-30% discount range.
  • Diminishing Returns: Discounts above 30% display diminishing returns, likely signaling brand devaluation or inventory clearance perception. Maintain promotions within the 15–25% window to maximize profit margin.

4. Rating Distribution & Impact

Customer reviews directly influence conversion rates and trust metrics.

# Sample Data Preparation
rating_percentages_df <- data.frame(
  Rating = c("5 Stars", "4 Stars", "3 Stars", "2 Stars", "1 Star"),
  Percentage = c(55, 25, 12, 5, 3)
)

# Visualization
ggplot(rating_percentages_df, aes(x = reorder(Rating, -Percentage), y = Percentage)) +
  geom_col(fill = "#9467bd", width = 0.6) +
  geom_text(aes(label = paste0(Percentage, "%")), vjust = -0.5, size = 3.8, fontface = "bold") +
  scale_y_continuous(limits = c(0, 70), labels = function(x) paste0(x, "%")) +
  labs(
    title = "Product Rating Distribution",
    subtitle = "Percentage breakdown of customer ratings",
    x = "Rating Classification",
    y = "Percentage (%)"
  ) +
  theme_minimal(base_size = 12) +
  theme(panel.grid.minor = element_blank())

Business Insight & Takeaways

  • High Customer Satisfaction: 80% of customer reviews are positive (4 and 5 stars).
  • Actionable Recommendation: Leverage 5-star ratings in ad creative and landing pages to boost social proof and campaign CTRs.

5. Monthly Revenue & Orders Trend Analysis

Time-series monthly metrics processed in BigQuery tracking order frequency and aggregate revenue trajectory in 2022.

# Real Monthly Trend Data extracted from BigQuery results
monthly_data <- data.frame(
  Year_Month = c("2022-01", "2022-02", "2022-03", "2022-04", "2022-05", "2022-06", 
                 "2022-07", "2022-08", "2022-09", "2022-10", "2022-11", "2022-12"),
  Revenue = c(336909.06, 323249.72, 353641.16, 351044.32, 331804.22, 351610.48, 
              328004.83, 332347.02, 363437.22, 321102.22, 327027.44, 337334.78)
)

# Visualization
ggplot(monthly_data, aes(x = Year_Month, y = Revenue, group = 1)) +
  geom_line(color = "#1f77b4", size = 1.2) +
  geom_point(color = "#1f77b4", size = 3) +
  scale_y_continuous(labels = dollar_format(prefix = "$"), limits = c(250000, 400000)) +
  labs(
    title = "Monthly Revenue Trajectory (BigQuery Dataset)",
    subtitle = "Actual calculated monthly revenue performance",
    x = "Year-Month",
    y = "Total Revenue ($USD)"
  ) +
  theme_minimal(base_size = 12) +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    panel.grid.minor = element_blank()
  )

```

Business Insight & Takeaways

Revenue Stability: Monthly revenues consistently range around the $320,000 – $360,000 threshold. OperationalRecommendation: Maintain inventory buffer levels based on stable monthly order volume run-rates

Strategic Recommendations Summary

  1. Focus on High Elasticity Promotions: Limit heavy discounts (>30%) and standardize around 20% to safeguard margins.
  2. Optimize Payment Gateways: Prioritize seamless mobile checkout integration for credit cards and digital wallets.
  3. Q4 Inventory Readiness: Scale logistics and marketing budget starting in Ramadan and holiday demand.