Lead Generation and Conversion Analysis

Overview

This repository contains the work for a lead generation and conversion analysis assignment aimed at identifying insights into lead demographics, sourcing efficiency, and program interest trends for an e-learning platform.

Key Highlights:

• Generated and analyzed a dataset with 10,000 synthetic leads.
• Explored trends in demographics, programs, and lead sources.
• Performed conversion rate analysis and suggested a budget allocation strategy for marketing optimization.
• Summarized findings and actionable recommendations in a structured Summary Report.

Dataset

The dataset used for this analysis was generated using Python's Faker and Numpy libraries. It includes the following features:

• Lead ID: Unique identifier for each lead.
• Location: City or region of the lead.
• College: College/University of the lead.
• Year of Study: Current academic year (e.g., 1st, 2nd, etc.).
• Program Interest: E-learning program interest (e.g., AI, Robotics, etc.).
• Lead Source: Platform or channel through which the lead was acquired.
• Converted: Whether the lead converted into a customer (Yes/No).

The dataset can be found in the repository as Lead_Info_Converted.csv.

Methodology

The analysis was conducted in a Jupyter Notebook (Assignment.ipynb) using Python. Below are the key steps:

1. Data Preparation:

   • Generated the dataset with realistic distributions.
   • Cleaned and preprocessed the data for analysis.

2. Exploratory Data Analysis (EDA):

   • Examined demographic trends (e.g., gender, location, year of study).
   • Analyzed program popularity and lead sourcing efficiency.

3. Conversion Analysis:

   • Computed overall and source-specific conversion rates.
   • Identified high-conversion programs and demographic patterns.

4. Budget Allocation:

   • Designed a budget allocation strategy based on conversion rates and source contributions.
   • Demonstrated allocation using a hypothetical marketing budget of ₹100,000.

5. Recommendations:

   • Provided targeted strategies for improving program reach and lead conversion rates.

Technologies Used

• Python: Data cleaning, analysis, and visualization.
• Pandas, Numpy: Data manipulation.
• Matplotlib, Seaborn: Data visualization.
• Faker: Dataset generation.
• Jupyter Notebook: Interactive development environment.

Author

Prepared by Purvang Majevadiya.

• Email: purvangmajevadiya04@gmail.com
• LinkedIn: Purvang Majevadiya