Customer lifetime value (CLTV) Using GPT3

We can train the model on historical customer transaction data to predict the future spending of each customer. Here’s a step-by-step guide on how to use ChatGPT to define CLTV in Python:

Step 1: Install the necessary libraries

We will be using the pandas library to read and manipulate the transaction data, and the transformers library to interact with the ChatGPT model. You can install these libraries using pip:

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!pip install pandas transformers

Step 2: Load the transaction data

We will load the transaction data from a CSV file using the pandas library. The CSV file should have the following columns:

• customer_id: a unique identifier for each customer
• transaction_date: the date of each transaction in YYYY-MM-DD format
• transaction_amount: the amount of each transaction

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import pandas as pd

transaction_data = pd.read_csv("transaction_data.csv")

Step 3: Preprocess the transaction data

We need to preprocess the transaction data by aggregating the transactions by customer and calculating the following metrics: