Driver Churn Analysis for Ola
Budget: ₹1,500 – ₹12,500 INR
Objective:
To identify patterns and factors contributing to driver churn, focusing on actionable insights to improve retention strategies.
Scope of Work:
Data Understanding and Cleaning:
Review the dataset for inconsistencies, missing values, and outliers.
Clean and preprocess the data using Python (Pandas, NumPy) to ensure accuracy and reliability for analysis.
Exploratory Data Analysis (EDA):
Analyze key variables such as age, employment history, income, and loan data to identify correlations with churn.
Identify trends and patterns, such as the high churn rate among drivers aged 30-35.
Data Visualization:
Create interactive dashboards using Power BI to present findings.
Visualizations will include churn rates by age group, employment length, and other demographic and operational factors.
Insight Generation and Recommendations:
Provide actionable insights to reduce churn, such as targeted engagement programs for high-risk age groups.
Suggest strategies for improving driver satisfaction and retention, tailored to specific patterns uncovered in the analysis.
Deliverables:
A cleaned and preprocessed dataset ready for analysis.
Detailed EDA findings in both a report and visual format.
Power BI dashboard showcasing key insights and trends.
A summary of actionable recommendations to address churn.
Tools and Technologies:
Python: For data cleaning, preprocessing, and statistical analysis.
Power BI: For creating dashboards and presenting visual insights.
Additional Details:
Please ensure data privacy and compliance throughout the analysis process. The focus should remain on identifying high-impact variables that can inform targeted interventions for reducing churn.
To identify patterns and factors contributing to driver churn, focusing on actionable insights to improve retention strategies.
Scope of Work:
Data Understanding and Cleaning:
Review the dataset for inconsistencies, missing values, and outliers.
Clean and preprocess the data using Python (Pandas, NumPy) to ensure accuracy and reliability for analysis.
Exploratory Data Analysis (EDA):
Analyze key variables such as age, employment history, income, and loan data to identify correlations with churn.
Identify trends and patterns, such as the high churn rate among drivers aged 30-35.
Data Visualization:
Create interactive dashboards using Power BI to present findings.
Visualizations will include churn rates by age group, employment length, and other demographic and operational factors.
Insight Generation and Recommendations:
Provide actionable insights to reduce churn, such as targeted engagement programs for high-risk age groups.
Suggest strategies for improving driver satisfaction and retention, tailored to specific patterns uncovered in the analysis.
Deliverables:
A cleaned and preprocessed dataset ready for analysis.
Detailed EDA findings in both a report and visual format.
Power BI dashboard showcasing key insights and trends.
A summary of actionable recommendations to address churn.
Tools and Technologies:
Python: For data cleaning, preprocessing, and statistical analysis.
Power BI: For creating dashboards and presenting visual insights.
Additional Details:
Please ensure data privacy and compliance throughout the analysis process. The focus should remain on identifying high-impact variables that can inform targeted interventions for reducing churn.