MKTG3P98_Proj2_Part1

library(tidyverse)   # Data manipulation

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library(ggplot2)     # Data visualization
library(gridExtra)  # Load the package

## 
## Attaching package: 'gridExtra'
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##     combine

#### **Step 2: Load the Dataset**
rfm_data <- read.csv("retail_rfm.csv")

#### **Step 3: Assign RFM Scores**
rfm_data <- rfm_data %>%
  rename(
    Monetary = revenue,            # Rename for consistency
    Frequency = number_of_orders,  # Rename for consistency
    Recency = recency_days         # Rename for consistency
  ) %>%
  mutate(
    R_Score = ntile(-Recency, 5),   # Lower recency (recent purchases) is better
    F_Score = ntile(Frequency, 5),  # Higher frequency (higher transactions) better
    M_Score = ntile(Monetary, 5)    # Higher monetary value (increased spending) is better
  )

#### **Step 4: Assign the Customer Segments**
rfm_data <- rfm_data %>%
  mutate(
    Segment = case_when(
      R_Score == 5 & F_Score == 5 & M_Score == 5 ~ "Best Customers",
      R_Score >= 4 & F_Score >= 4 & M_Score >= 4 ~ "Loyal Customers",
      R_Score >= 3 & F_Score >= 3 & M_Score >= 3 ~ "Potential Loyalists",
      R_Score == 1 & F_Score == 1 ~ "Lost Customers",
      TRUE ~ "Other"
    )
  )

#### **Step 5: View the Segmented Data**
head(rfm_data)

##   customer_id Monetary Frequency Recency purchase zip_code R_Score F_Score
## 1           1   737.75        19      93        0    22181       4       5
## 2           2   299.75        10     419        0    21117       2       4
## 3           3    74.00         1     724        0    20850       1       1
## 4           4   178.00         2     793        0    22032       1       2
## 5           5   552.40        15      68        1     8527       4       5
## 6           6   137.00         3     120        0    22124       4       2
##   M_Score         Segment
## 1       5 Loyal Customers
## 2       4           Other
## 3       2  Lost Customers
## 4       3           Other
## 5       5 Loyal Customers
## 6       3           Other

### **4. Add the Visualizations**

#### **Bar Chart: The Customer Segment Distribution**

ggplot(rfm_data, aes(x=Segment, fill=Segment)) +
  geom_bar() +
  theme_bw() +
  geom_text(stat="count", aes(label=..count..), vjust=0) +
  labs(title ="RFM Customer Segments", x="Segment", y="Count") +
  theme(axis.text.x = element_text(angle = 45, hjust=1))

## Warning: The dot-dot notation (`..count..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(count)` instead.
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## generated.

ggplot(rfm_data, aes(x=Recency, y=Frequency, color=Segment)) +
  geom_point(size=3) +
  theme_bw() +
  labs(title="Recency vs Frequency by Segment", 
       x="Recency (Days Since Last Purchase)", 
       y="Frequency (Total Transactions)")

ggplot(rfm_data, aes(x=Segment, y=Monetary, fill=Segment)) +
  geom_boxplot() +
  theme_bw() +
  labs(title="Monetary Value by Customer Segment", x="Segment", 
       y="Total Monetary Value") +
  theme(axis.text.x = element_text(angle = 45, hjust=1))

# Calculate average customer spending
average_spending <- mean(rfm_data$Monetary)

# Print the result
print(paste("The average customer spending is:", round(average_spending, 2)))

## [1] "The average customer spending is: 358.83"

# Calculate average spending for each customer segment
segment_avg_spending <- rfm_data %>%
  group_by(Segment) %>%
  summarise(Average_Spending = mean(Monetary, na.rm = TRUE))

# Print average spending for each segment
for (i in 1:nrow(segment_avg_spending)) {
  print(paste("Average spending for", segment_avg_spending$Segment[i], ":", 
              round(segment_avg_spending$Average_Spending[i], 2)))
}

## [1] "Average spending for Best Customers : 1341.91"
## [1] "Average spending for Lost Customers : 54.83"
## [1] "Average spending for Loyal Customers : 716.3"
## [1] "Average spending for Other : 154.79"
## [1] "Average spending for Potential Loyalists : 360.58"

### **5. Pie Charts: Purchasing Behavior by Segment**

# Filter the customers who purchased and did not purchase
purchased_data <- rfm_data %>% filter(purchase == 1)
not_purchased_data <- rfm_data %>% filter(purchase == 0)

# Count of the segments for each group
purchased_counts <- purchased_data %>% count(Segment)
not_purchased_counts <- not_purchased_data %>% count(Segment)

# Create the Pie Chart Function
plot_pie_chart <- function(data, title) {
  ggplot(data, aes(x="", y=n, fill=Segment)) +
    geom_bar(stat="identity", width=1) +
    coord_polar(theta="y") +
    theme_void() +
    labs(title = title, fill = "Segment") +
    geom_text(aes(label = paste0(round(n/sum(n)*100, 1), "%")),
              position = position_stack(vjust=0.5))
}

# Generate the Pie Charts
p1 <- plot_pie_chart(purchased_counts, "Customers Who did Purchase (Segmented)")
p2 <- plot_pie_chart(not_purchased_counts, "Customers Who Didn't Purchase (Segmented)")

grid.arrange(p1, p2, ncol=2)