Narayan Jalan

Project Title: Customer Lifetime Value Modeling for E-commerce

Project Description:

Problem Statement:
The e-commerce industry is highly competitive, with numerous online retailers vying for customers' attention. To thrive in this competitive landscape, our organization needs to better understand and leverage the value of our customer base. We lack a comprehensive Customer Lifetime Value (CLTV) model, hindering our ability to make informed marketing and business decisions. We… Show more

Project Title: Customer Lifetime Value Modeling for E-commerce

Project Description:

Problem Statement:
The e-commerce industry is highly competitive, with numerous online retailers vying for customers' attention. To thrive in this competitive landscape, our organization needs to better understand and leverage the value of our customer base. We lack a comprehensive Customer Lifetime Value (CLTV) model, hindering our ability to make informed marketing and business decisions. We need to develop a robust CLTV model to identify high-value customers, optimize marketing efforts, and enhance overall profitability.

Methodology Adopted:

1. Data Collection and Integration:
- Gather customer data from various sources, including transaction history, website behavior, and demographics.
- Integrate and clean the data to create a unified dataset for analysis.

2. Feature Engineering:
- Identified relevant customer features such as purchase frequency, average order value, GMV per customer
- Calculate additional variables like customer churn rate and customer acquisition cost (CAC).

3. CLTV Model Selection:
- Explore various CLTV models, such as the RFM (Recency, Frequency, Monetary) model, cohort analysis, and machine learning-based models.
- Choose the most suitable model based on the organization's specific needs and data characteristics.

4. Model Development:
- Developed and trained the selected CLTV model using historical customer data.
- Fine-tune the model parameters for accuracy and reliability.

5. Validation and Testing:
- Validated the CLTV model's performance using holdout data and cross-validation techniques.
- Tested the model against historical data to assess its predictive capabilities.

6. Integration with Business Processes:
- Implemented the CLTV model within the organization's existing systems and processes.
- Ensure that marketing & sales teams can access CLTV predictions for decision-making.

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