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Email Marketing

Revenue-focused experts that offer full-service Klaviyo email marketing solutions.

import numpy as np
import pandas as pd

# Load customer data
customer_data = pd.read_csv('customer_data.csv')

# Analyze customer journey
customer_journey = customer_data.groupby('customer_id')['purchase_date'].agg(list)

# Calculate Lifetime Value (LTV)
customer_revenue = customer_data.groupby('customer_id')['order_value'].sum()
customer_lifespan = customer_journey.apply(lambda x: (max(x) - min(x)).days / 365)
ltv = (customer_revenue / customer_lifespan).mean()

# Enhance customer journey
customer_journey['time_between_purchases'] = customer_journey['purchase_date'].apply(lambda x: [y - x[i-1] for i, y in enumerate(x[1:], start=1)])
customer_journey['avg_time_between_purchases'] = customer_journey['time_between_purchases'].apply(lambda x: np.mean(x))

# Optimize for conversion rate
product_data = pd.read_csv('product_data.csv')
product_conversion_rate = product_data.groupby('product_id')['purchased'].mean()

# Foster repeat purchases
customer_journey['repeat_purchases'] = customer_journey['purchase_date'].apply(lambda x: len(x) - 1)
customer_journey['repeat_purchase_rate'] = customer_journey['repeat_purchases'] / customer_journey['repeat_purchases'].max()

# Combine insights
customer_insights = pd.concat([customer_revenue, customer_lifespan, customer_journey['avg_time_between_purchases'], product_conversion_rate, customer_journey['repeat_purchase_rate']], axis=1)
customer_insights.columns = ['Customer LTV', 'Customer Lifespan', 'Avg Time Between Purchases', 'Product Conversion Rate', 'Repeat Purchase Rate']

# Provide recommendations
print('Recommendations:')
print('- Focus on products with high conversion rates to improve overall conversion')
print('- Identify customers with high repeat purchase rates and target them for loyalty programs')
print('- Analyze the customer journey to identify ways to reduce the average time between purchases')

This code provides a high-level approach to optimizing customer journeys to enhance Lifetime Value (LTV), boost conversion rates, and foster repeat purchases. It involves:

  1. Loading customer data and analyzing the customer journey.
  2. Calculating the Lifetime Value (LTV) of customers.
  3. Enhancing the customer journey by analyzing the time between purchases.
  4. Optimizing for conversion rate by analyzing product conversion rates.
  5. Fostering repeat purchases by analyzing the repeat purchase rate.
  6. Combining the insights and providing recommendations.

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