Streamline LTV Calculation with GA4 and Monday.com CRM

LTV calculation

To automate the Customer Lifetime Value (LTV) calculation using APIs from Google Analytics and Monday.com CRM, you can follow the steps outlined below

Overview

The automation involves:

• Extracting behavioral metrics (e.g., average order value, purchase frequency) from Google Analytics using its API.
• Retrieving customer data (e.g., customer lifespan, demographics) from Monday.com CRM via its GraphQL API.
• Merging and processing the data to calculate LTV for each customer.
• Storing or visualizing the results in a dashboard or database for further analysis.

Prerequisites

Before starting, ensure:

• Access to the Google Analytics Data API and Monday.com API with valid credentials.
• Programming knowledge in Python (or another language).
• Libraries installed for API requests (e.g., requests, pandas).

Steps to Automate LTV Calculation

Step 1: Set Up Google Analytics API

1. Enable the Google Analytics Data API:
   • Go to Google Cloud Console and enable the API for your project.
2. Obtain API Credentials:
   • Download the JSON key file for authentication.
3. Retrieve Required Metrics:
   • Use the API to extract metrics such as average_order_value and purchase_frequency.

Example Code for Google Analytics Data API:

from google.oauth2 import service_account
from googleapiclient.discovery import build

def fetch_google_analytics_data(property_id):
    # Authenticate using service account
    credentials = service_account.Credentials.from_service_account_file('path/to/credentials.json')
    analytics = build('analyticsdata', 'v1beta', credentials=credentials)

    # Query Google Analytics for required metrics
    request = {
        "property": f"properties/{property_id}",
        "dateRanges": [{"startDate": "2023-01-01", "endDate": "2023-12-31"}],
        "metrics": [{"name": "averagePurchaseRevenue"}, {"name": "purchaseFrequency"}]
    }
    response = analytics.properties().runReport(body=request).execute()

    # Extract metrics
    data = []
    for row in response['rows']:
        data.append({
            'user_id': row['dimensionValues'][^0]['value'],
            'average_order_value': float(row['metricValues'][^0]['value']),
            'purchase_frequency': float(row['metricValues'][^1]['value'])
        })
    return data

Step 2: Set Up Monday.com API

1. Enable Monday.com GraphQL API:
   • Obtain your personal API key from Monday.com’s developer console.
2. Retrieve Customer Data:
   • Use GraphQL queries to fetch customer lifespan or other relevant fields.

Example Code for Monday.com API:

import requests

def fetch_monday_data(api_key):
    url = "https://api.monday.com/v2"
    headers = {"Authorization": api_key}
    query = """
    query {
        items {
            id
            name
            column_values {
                id
                text
            }
        }
    }
    """
    response = requests.post(url, headers=headers, json={"query": query})
    data = response.json()

    # Parse customer lifespan from column values
    customers = []
    for item in data['data']['items']:
        customer_lifespan = next((col['text'] for col in item['column_values'] if col['id'] == 'customer_lifespan'), None)
        customers.append({
            'user_id': item['id'],
            'customer_lifespan': int(customer_lifespan) if customer_lifespan else 0
        })
    return customers

Step 3: Merge and Calculate LTV

Combine data from both APIs and calculate LTV using the formula:

Example Code for Merging and Calculating LTV:

import pandas as pd

def calculate_ltv(ga_data, monday_data):
    # Convert data to DataFrames
    ga_df = pd.DataFrame(ga_data)
    monday_df = pd.DataFrame(monday_data)

    # Merge on user_id
    combined_df = pd.merge(ga_df, monday_df, on='user_id')

    # Calculate LTV
    combined_df['LTV'] = (
        combined_df['average_order_value'] *
        combined_df['purchase_frequency'] *
        combined_df['customer_lifespan']
    )

    return combined_df[['user_id', 'LTV']]

Step 4: Automate Reporting

Export the calculated LTV values to a dashboard or database for visualization or further use.

Example of exporting to a CSV file:

def export_to_csv(dataframe, filename="ltv_report.csv"):
    dataframe.to_csv(filename, index=False)

Integration Platforms

If coding is not feasible, consider using integration platforms like:

• Zapier: Automate workflows between Google Analytics and Monday.com.
• Latenode or ApiX-Drive: Set up no-code integrations to sync data between platforms.

Use Cases of Automated LTV Calculation

1. Customer Segmentation:
   • Identify high-LTV customers and focus marketing efforts on them.
2. Budget Allocation:
   • Allocate ad spend toward campaigns attracting high-LTV customers.
3. Retention Strategies:
   • Develop loyalty programs targeting customers with high potential but low engagement.
4. Product Development:
   • Use insights from high-LTV customers to guide product improvements or new offerings.

Benefits of Automation

• Saves time by eliminating manual calculations.
• Ensures real-time updates with accurate data.
• Enables scalable analysis across large datasets.
• Enhances decision-making with actionable insights.

By leveraging APIs from Google Analytics and Monday.com CRM, businesses can automate LTV calculations and unlock powerful insights into customer behavior and value, driving smarter marketing strategies and improved ROI.