import numpy as np from sklearn.cluster import KMeans import matplotlib.pyplot as plt # Sample data: customer_id, total_purchases, avg_time_between_purchases (hours) customer_data = np.array([ [1, 25, 0.5], [2, 10, 5], [3, 15, 2], [4, 30, 1], [5, 7, 10], [6, 20, 3], [7, 12, 7], [8, 28, 0.8], [9, 6, 12], [10, 18, 1.5] ]) # Extract customer IDs and feature data customer_ids = customer_data[:, 0].astype(int) features = customer_data[:, 1:] # Perform k-means clustering n_clusters = 2 kmeans = KMeans(n_clusters=n_clusters, random_state=42) cluster_labels = kmeans.fit_predict(features) # Find the cluster with the lowest average time between purchases impulsive_cluster = np.argmin(kmeans.cluster_centers_[:, 1]) # Get the customer IDs in the impulsive cluster impulsive_buyers = customer_ids[cluster_labels == impulsive_cluster] print("Impulsive buyers:", impulsive_buyers) # Visualize the clustering results (optional) plt.scatter(features[:, 0], features[:, 1], c=cluster_labels, cmap='viridis') plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], c='red', marker='x') plt.xlabel('Total Purchases') plt.ylabel('Average Time Between Purchases (hours)') plt.show()
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Loyalty

Earn points and turn them into rewards

  1. 01

    Sign Up

    • Sign up as a member to start enjoying the loyalty program

  2. 02

    Earn Points

    • Purchase a product

      Get 1 point for every $1 spent

    • Sign up to the site

      Get 50 points

    • Order a plan

      Get 1 point for every $1 spent

  3. 03

    Redeem Rewards

    • 10% off all store products

      10 Points = 10% off the lowest priced item in cart

    • 10% off all events

      10 Points = 10% off the lowest priced item in cart

    • 10% off all bookings

      10 Points = 10% off the lowest priced item in cart

Loyalty: Loyalty
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