File size: 8,467 Bytes
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import random
import numpy as np
import pandas as pd
def generate_customer_dataset(num_customers=500, random_seed=42):
np.random.seed(random_seed)
random.seed(random_seed)
first_names = ["Aarav", "Ananya", "Rohan", "Priya", "Vikram", "Neha", "Aditya", "Sanya", "Kabir", "Meera",
"Rahul", "Pooja", "Arjun", "Kavya", "Siddharth", "Ishita", "Varun", "Riya", "Karan", "Tanvi"]
last_names = ["Sharma", "Verma", "Gupta", "Patel", "Mehta", "Singh", "Kumar", "Chawla", "Joshi", "Rao"]
channels = ["WhatsApp", "Email", "Push", "SMS", "Phone"]
categories = ["Electronics", "Fashion", "Home & Kitchen", "Beauty & Care", "Sports & Fitness"]
customers = []
for i in range(1001, 1001 + num_customers):
customer_id = f"C{i}"
fname = random.choice(first_names)
lname = random.choice(last_names)
name = f"{fname} {lname}"
email = f"{fname.lower()}.{lname.lower()}{random.randint(10, 99)}@example.com"
join_days_ago = random.randint(30, 700)
join_date = "2024-01-15"
preferred_channel = random.choice(channels)
preferred_category = random.choice(categories)
archetype = np.random.choice(["loyal", "at_risk", "high_intent", "low_active", "dormant"], p=[0.25, 0.20, 0.20, 0.20, 0.15])
if archetype == "loyal":
recency_days = random.randint(1, 20)
frequency_purchases = random.randint(12, 45)
avg_order_val = float(np.random.uniform(2500, 15000))
website_visits = random.randint(15, 60)
product_views = random.randint(25, 100)
cart_additions = random.randint(5, 20)
cart_abandonments = random.randint(0, 3)
email_opens = random.randint(8, 25)
email_clicks = random.randint(4, 15)
support_tickets = random.randint(0, 2)
engagement_change_pct = float(np.random.uniform(5, 40))
return_rate = float(np.random.uniform(0.01, 0.08))
discount_dependency = float(np.random.uniform(0.1, 0.35))
elif archetype == "at_risk":
recency_days = random.randint(45, 120)
frequency_purchases = random.randint(8, 30)
avg_order_val = float(np.random.uniform(3000, 20000))
website_visits = random.randint(1, 8)
product_views = random.randint(2, 12)
cart_additions = random.randint(1, 4)
cart_abandonments = random.randint(2, 6)
email_opens = random.randint(0, 4)
email_clicks = random.randint(0, 2)
support_tickets = random.randint(2, 7)
engagement_change_pct = float(np.random.uniform(-75, -25))
return_rate = float(np.random.uniform(0.10, 0.30))
discount_dependency = float(np.random.uniform(0.3, 0.70))
elif archetype == "high_intent":
recency_days = random.randint(3, 30)
frequency_purchases = random.randint(2, 10)
avg_order_val = float(np.random.uniform(1500, 8000))
website_visits = random.randint(20, 70)
product_views = random.randint(40, 120)
cart_additions = random.randint(8, 25)
cart_abandonments = random.randint(4, 10)
email_opens = random.randint(10, 30)
email_clicks = random.randint(6, 20)
support_tickets = random.randint(0, 1)
engagement_change_pct = float(np.random.uniform(20, 90))
return_rate = float(np.random.uniform(0.02, 0.10))
discount_dependency = float(np.random.uniform(0.4, 0.8))
elif archetype == "low_active":
recency_days = random.randint(15, 60)
frequency_purchases = random.randint(1, 5)
avg_order_val = float(np.random.uniform(800, 3000))
website_visits = random.randint(4, 15)
product_views = random.randint(5, 20)
cart_additions = random.randint(1, 5)
cart_abandonments = random.randint(0, 3)
email_opens = random.randint(2, 8)
email_clicks = random.randint(1, 4)
support_tickets = random.randint(0, 2)
engagement_change_pct = float(np.random.uniform(-15, 15))
return_rate = float(np.random.uniform(0.03, 0.12))
discount_dependency = float(np.random.uniform(0.2, 0.5))
else: # dormant
recency_days = random.randint(90, 200)
frequency_purchases = random.randint(1, 3)
avg_order_val = float(np.random.uniform(500, 2500))
website_visits = random.randint(0, 2)
product_views = random.randint(0, 4)
cart_additions = random.randint(0, 1)
cart_abandonments = random.randint(0, 2)
email_opens = random.randint(0, 2)
email_clicks = random.randint(0, 1)
support_tickets = random.randint(0, 4)
engagement_change_pct = float(np.random.uniform(-90, -40))
return_rate = float(np.random.uniform(0.05, 0.25))
discount_dependency = float(np.random.uniform(0.3, 0.9))
monetary_total_spend = round(frequency_purchases * avg_order_val, 2)
spend_30d = round(monetary_total_spend * (0.3 if recency_days <= 30 else 0.0), 2)
spend_90d = round(monetary_total_spend * (0.7 if recency_days <= 90 else 0.1), 2)
purchase_velocity = float(round(max(5.0, join_days_ago / max(1, frequency_purchases)), 1))
churn_raw = (
0.035 * recency_days
+ 0.15 * cart_abandonments
+ 0.25 * support_tickets
- 0.02 * website_visits
- 0.02 * email_clicks
- 0.03 * engagement_change_pct
+ (15.0 if recency_days > 60 else 0.0)
)
churn_prob = 1.0 / (1.0 + np.exp(-(churn_raw - 1.5)))
churn_label = 1 if churn_prob > 0.50 else 0
prop_raw = (
0.04 * product_views
+ 0.08 * cart_additions
+ 0.05 * email_clicks
+ 0.02 * engagement_change_pct
- 0.02 * recency_days
- 0.30 * support_tickets
)
prop_prob = 1.0 / (1.0 + np.exp(-(prop_raw - 1.0)))
purchase_intent_label = 1 if prop_prob > 0.50 else 0
expected_years = 2.5
retention_factor = max(0.1, 1.0 - churn_prob)
annual_spend = (monetary_total_spend / max(1.0, join_days_ago / 365.0))
clv_val = round(max(1000.0, annual_spend * expected_years * retention_factor), 2)
row = {
"customer_id": customer_id,
"name": name,
"email": email,
"join_date": join_date,
"preferred_channel": preferred_channel,
"preferred_category": preferred_category,
"recency_days": recency_days,
"frequency_purchases": frequency_purchases,
"monetary_total_spend": monetary_total_spend,
"avg_order_value": round(avg_order_val, 2),
"website_visits_30d": website_visits,
"product_views_30d": product_views,
"cart_additions_30d": cart_additions,
"cart_abandonments_30d": cart_abandonments,
"email_opens_30d": email_opens,
"email_clicks_30d": email_clicks,
"support_tickets_30d": support_tickets,
"engagement_change_pct": round(engagement_change_pct, 1),
"return_rate": round(return_rate, 3),
"discount_dependency_ratio": round(discount_dependency, 3),
"spend_30d": spend_30d,
"spend_90d": spend_90d,
"purchase_velocity": purchase_velocity,
"churn_label": churn_label,
"purchase_intent_label": purchase_intent_label,
"clv": clv_val,
"archetype": archetype
}
customers.append(row)
df = pd.DataFrame(customers)
os.makedirs("ml/data", exist_ok=True)
csv_path = "ml/data/customer_signals.csv"
df.to_csv(csv_path, index=False)
print(f"[Dataset Generator] Successfully generated {len(df)} customer records saved to {csv_path}")
return df
if __name__ == "__main__":
generate_customer_dataset()
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