| import os
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| import random
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| import numpy as np
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| import pandas as pd
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|
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| def generate_customer_dataset(num_customers=500, random_seed=42):
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| np.random.seed(random_seed)
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| random.seed(random_seed)
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|
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| first_names = ["Aarav", "Ananya", "Rohan", "Priya", "Vikram", "Neha", "Aditya", "Sanya", "Kabir", "Meera",
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| "Rahul", "Pooja", "Arjun", "Kavya", "Siddharth", "Ishita", "Varun", "Riya", "Karan", "Tanvi"]
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| last_names = ["Sharma", "Verma", "Gupta", "Patel", "Mehta", "Singh", "Kumar", "Chawla", "Joshi", "Rao"]
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|
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| channels = ["WhatsApp", "Email", "Push", "SMS", "Phone"]
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| categories = ["Electronics", "Fashion", "Home & Kitchen", "Beauty & Care", "Sports & Fitness"]
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|
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| customers = []
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|
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| for i in range(1001, 1001 + num_customers):
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| customer_id = f"C{i}"
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| fname = random.choice(first_names)
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| lname = random.choice(last_names)
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| name = f"{fname} {lname}"
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| email = f"{fname.lower()}.{lname.lower()}{random.randint(10, 99)}@example.com"
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|
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| join_days_ago = random.randint(30, 700)
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| join_date = "2024-01-15"
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|
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| preferred_channel = random.choice(channels)
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| preferred_category = random.choice(categories)
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|
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| archetype = np.random.choice(["loyal", "at_risk", "high_intent", "low_active", "dormant"], p=[0.25, 0.20, 0.20, 0.20, 0.15])
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|
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| if archetype == "loyal":
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| recency_days = random.randint(1, 20)
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| frequency_purchases = random.randint(12, 45)
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| avg_order_val = float(np.random.uniform(2500, 15000))
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| website_visits = random.randint(15, 60)
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| product_views = random.randint(25, 100)
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| cart_additions = random.randint(5, 20)
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| cart_abandonments = random.randint(0, 3)
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| email_opens = random.randint(8, 25)
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| email_clicks = random.randint(4, 15)
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| support_tickets = random.randint(0, 2)
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| engagement_change_pct = float(np.random.uniform(5, 40))
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| return_rate = float(np.random.uniform(0.01, 0.08))
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| discount_dependency = float(np.random.uniform(0.1, 0.35))
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| elif archetype == "at_risk":
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| recency_days = random.randint(45, 120)
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| frequency_purchases = random.randint(8, 30)
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| avg_order_val = float(np.random.uniform(3000, 20000))
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| website_visits = random.randint(1, 8)
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| product_views = random.randint(2, 12)
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| cart_additions = random.randint(1, 4)
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| cart_abandonments = random.randint(2, 6)
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| email_opens = random.randint(0, 4)
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| email_clicks = random.randint(0, 2)
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| support_tickets = random.randint(2, 7)
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| engagement_change_pct = float(np.random.uniform(-75, -25))
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| return_rate = float(np.random.uniform(0.10, 0.30))
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| discount_dependency = float(np.random.uniform(0.3, 0.70))
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| elif archetype == "high_intent":
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| recency_days = random.randint(3, 30)
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| frequency_purchases = random.randint(2, 10)
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| avg_order_val = float(np.random.uniform(1500, 8000))
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| website_visits = random.randint(20, 70)
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| product_views = random.randint(40, 120)
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| cart_additions = random.randint(8, 25)
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| cart_abandonments = random.randint(4, 10)
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| email_opens = random.randint(10, 30)
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| email_clicks = random.randint(6, 20)
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| support_tickets = random.randint(0, 1)
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| engagement_change_pct = float(np.random.uniform(20, 90))
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| return_rate = float(np.random.uniform(0.02, 0.10))
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| discount_dependency = float(np.random.uniform(0.4, 0.8))
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| elif archetype == "low_active":
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| recency_days = random.randint(15, 60)
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| frequency_purchases = random.randint(1, 5)
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| avg_order_val = float(np.random.uniform(800, 3000))
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| website_visits = random.randint(4, 15)
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| product_views = random.randint(5, 20)
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| cart_additions = random.randint(1, 5)
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| cart_abandonments = random.randint(0, 3)
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| email_opens = random.randint(2, 8)
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| email_clicks = random.randint(1, 4)
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| support_tickets = random.randint(0, 2)
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| engagement_change_pct = float(np.random.uniform(-15, 15))
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| return_rate = float(np.random.uniform(0.03, 0.12))
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| discount_dependency = float(np.random.uniform(0.2, 0.5))
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| else:
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| recency_days = random.randint(90, 200)
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| frequency_purchases = random.randint(1, 3)
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| avg_order_val = float(np.random.uniform(500, 2500))
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| website_visits = random.randint(0, 2)
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| product_views = random.randint(0, 4)
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| cart_additions = random.randint(0, 1)
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| cart_abandonments = random.randint(0, 2)
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| email_opens = random.randint(0, 2)
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| email_clicks = random.randint(0, 1)
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| support_tickets = random.randint(0, 4)
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| engagement_change_pct = float(np.random.uniform(-90, -40))
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| return_rate = float(np.random.uniform(0.05, 0.25))
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| discount_dependency = float(np.random.uniform(0.3, 0.9))
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|
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| monetary_total_spend = round(frequency_purchases * avg_order_val, 2)
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| spend_30d = round(monetary_total_spend * (0.3 if recency_days <= 30 else 0.0), 2)
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| spend_90d = round(monetary_total_spend * (0.7 if recency_days <= 90 else 0.1), 2)
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| purchase_velocity = float(round(max(5.0, join_days_ago / max(1, frequency_purchases)), 1))
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|
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| churn_raw = (
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| 0.035 * recency_days
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| + 0.15 * cart_abandonments
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| + 0.25 * support_tickets
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| - 0.02 * website_visits
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| - 0.02 * email_clicks
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| - 0.03 * engagement_change_pct
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| + (15.0 if recency_days > 60 else 0.0)
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| )
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| churn_prob = 1.0 / (1.0 + np.exp(-(churn_raw - 1.5)))
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| churn_label = 1 if churn_prob > 0.50 else 0
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|
|
| prop_raw = (
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| 0.04 * product_views
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| + 0.08 * cart_additions
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| + 0.05 * email_clicks
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| + 0.02 * engagement_change_pct
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| - 0.02 * recency_days
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| - 0.30 * support_tickets
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| )
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| prop_prob = 1.0 / (1.0 + np.exp(-(prop_raw - 1.0)))
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| purchase_intent_label = 1 if prop_prob > 0.50 else 0
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|
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| expected_years = 2.5
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| retention_factor = max(0.1, 1.0 - churn_prob)
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| annual_spend = (monetary_total_spend / max(1.0, join_days_ago / 365.0))
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| clv_val = round(max(1000.0, annual_spend * expected_years * retention_factor), 2)
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|
|
| row = {
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| "customer_id": customer_id,
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| "name": name,
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| "email": email,
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| "join_date": join_date,
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| "preferred_channel": preferred_channel,
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| "preferred_category": preferred_category,
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| "recency_days": recency_days,
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| "frequency_purchases": frequency_purchases,
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| "monetary_total_spend": monetary_total_spend,
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| "avg_order_value": round(avg_order_val, 2),
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| "website_visits_30d": website_visits,
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| "product_views_30d": product_views,
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| "cart_additions_30d": cart_additions,
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| "cart_abandonments_30d": cart_abandonments,
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| "email_opens_30d": email_opens,
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| "email_clicks_30d": email_clicks,
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| "support_tickets_30d": support_tickets,
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| "engagement_change_pct": round(engagement_change_pct, 1),
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| "return_rate": round(return_rate, 3),
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| "discount_dependency_ratio": round(discount_dependency, 3),
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| "spend_30d": spend_30d,
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| "spend_90d": spend_90d,
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| "purchase_velocity": purchase_velocity,
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| "churn_label": churn_label,
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| "purchase_intent_label": purchase_intent_label,
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| "clv": clv_val,
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| "archetype": archetype
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| }
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| customers.append(row)
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|
|
| df = pd.DataFrame(customers)
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|
|
| os.makedirs("ml/data", exist_ok=True)
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| csv_path = "ml/data/customer_signals.csv"
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| df.to_csv(csv_path, index=False)
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| print(f"[Dataset Generator] Successfully generated {len(df)} customer records saved to {csv_path}")
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| return df
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|
|
| if __name__ == "__main__":
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| generate_customer_dataset()
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|
|
