Customer Segmentation Analysis

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import LinearSegmentedColormap

# Customer RFM data
np.random.seed(42)
n_customers = 8000

# Purchase frequency and monetary value
frequency = np.random.lognormal(1.5, 0.8, n_customers)
frequency = np.clip(frequency, 1, 100)

monetary = frequency * np.random.lognormal(3, 0.6, n_customers)
monetary = np.clip(monetary, 10, 10000)

# Modern light theme
fig, ax = plt.subplots(figsize=(10, 8), facecolor='#ffffff')
ax.set_facecolor('#fafbfc')

# Soft pastel colormap
colors = ['#fafbfc', '#e8f4f8', '#d0e8f0', '#a8d8ea', '#72c3dc', 
          '#4aa8c7', '#3d8eb3', '#2d7399', '#1d5980', '#0d3f66']
cmap = LinearSegmentedColormap.from_list('soft_blue', colors, N=256)

# Hexbin plot
hb = ax.hexbin(frequency, monetary, gridsize=35, cmap=cmap, mincnt=1,
               edgecolors='white', linewidths=0.3)

# Styled colorbar
cbar = plt.colorbar(hb, ax=ax, pad=0.02, shrink=0.85)
cbar.set_label('Customer Count', fontsize=11, color='#374151', labelpad=10)
cbar.ax.yaxis.set_tick_params(color='#6b7280')
cbar.outline.set_edgecolor('#e5e7eb')
plt.setp(plt.getp(cbar.ax.axes, 'yticklabels'), color='#6b7280', fontsize=9)

# High-value segment
from matplotlib.patches import Rectangle
vip = Rectangle((30, 3000), 70, 7000, fill=False, edgecolor='#ef4444', 
                linewidth=2, linestyle='--', alpha=0.8)
ax.add_patch(vip)
ax.text(65, 8500, 'VIP Segment', fontsize=10, color='#ef4444', ha='center', fontweight='600')

# Labels with modern typography
ax.set_xlabel('Purchase Frequency', fontsize=12, color='#374151', fontweight='600', labelpad=12)
ax.set_ylabel('Monetary Value ($)', fontsize=12, color='#374151', fontweight='600', labelpad=12)
ax.set_title('Customer Segmentation Analysis', fontsize=16, color='#111827', 
             fontweight='700', pad=20)

# Clean minimal axes
ax.tick_params(colors='#6b7280', labelsize=10, length=0)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_color('#e5e7eb')
ax.spines['bottom'].set_color('#e5e7eb')

# Subtle grid
ax.grid(True, alpha=0.3, color='#e5e7eb', linestyle='-', linewidth=0.5)
ax.set_axisbelow(True)

plt.tight_layout()
plt.show()