Madhuri Kallikota

• Acquisition Metrics: Analyzed key metrics such as Conversion Rate and Cost of Customer Acquisition to optimize customer acquisition strategies and reduce acquisition costs.
• Engagement Metrics Analysis: Monitored engagement metrics, including Daily Active Users (DAU), Monthly Active Users (MAU), and Stickiness Ratio (DAU/MAU), to evaluate customer interaction and engagement over time.
• Business Metrics: Conducted market research and implemented quality control processes, ensuring that business metrics aligned with company objectives. Analyzed probability distributions to predict customer behavior and optimize decision-making processes.
• Average Revenue Per User (ARPU): Tracked ARPU to measure the revenue generated per user over a given period and used this data to inform revenue growth strategies.
• Subscriber Growth Rate: Monitored Subscriber Growth Rate to assess the overall growth of the subscriber base and identify trends in user acquisition.

Projects Completed: Churn Model - Customer Churn Analysis

Flow of the Project:

• Data Collection: Gathered relevant data, including customer demographics, usage patterns, billing information, service complaints, downtimes, and call details.
• Feature Engineering: Developed key features for analysis, such as average call duration, monthly data usage, number of support calls, and payment frequency, to enhance the model’s predictive accuracy.
• Segmentation: Used clustering techniques to segment customers based on usage patterns and demographics, identifying high-risk groups with a higher likelihood of churn.
• Exploratory Data Analysis (EDA): Conducted comprehensive EDA to identify key factors contributing to churn. Analyzed correlations between high churn rates and factors like lower service usage or increased complaints.
  

Actionable Insight:

• Recommendations: Delivered detailed recommendations to the business team, focusing on improving user retention by addressing key areas of service dissatisfaction, such as reducing service errors and enhancing customer support.
• Project Outcome: Achieved a 25.6% churn rate reduction over the last 3 months through targeted interventions based on data insights and customer segmentation.
  

• Monitored key insurance metrics, including claims-to-premium ratios, and tracked claim frequency and average claim size to ensure profitability.
• Collected and analyzed daily customer data, such as policyholder renewals, claims, and cancellations, identifying high-risk groups and retention strategies.
• Utilized data visualization to create dashboards for tracking key metrics and trends in churn and customer retention.

Key Contributions:

• Streamlined daily metrics reporting for policy renewals and claims, resulting in more accurate forecasting and trend analysis.
• Provided insights that informed retention strategies, contributing to improved customer retention and reduced churn rates in critical segments.