Mohit Kumar

Project: E-commerce Customer Churn Analysis

• Objective: Analyzed and mitigated customer churn in e-commerce setting by implementing Recency, Frequency, and Monetary (RFM) model.
• Methodology: Utilized transactional data to calculate Recency, Frequency, and Monetary values for each customer.
  Segmented customer base to identify distinct groups based on RFM metrics.
  Developed and trained machine learning model for churn prediction using historical data.
• Results: Identified high-churn segments through RFM analysis.
• Technologies Used: Employed[Python,Numpy,Pandas, Scikit-Learn] for data preprocessing, analysis, and model training.
• Key Contributions: Provided actionable insights for marketing strategies through RFM segmentation.
  Implemented data-driven retention initiatives resulting in increased customer loyalty.
• Learnings: Gained proficiency in customer segmentation methodologies and predictive modeling.
  Developed a strategic approach to addressing customer churn in e-commerce sector.
• Future Applications: Recognized potential for broader application in personalized marketing, customer engagement, and product recommendations.

• Conducting marketing campaign reviews and gathering customer feedback
• Streamlined business processes by identifying inefficiencies and implementing targeted solutions.
• Enhanced workflow efficiency for increased productivity and cost savings through process optimization.

CampaignToCash

• Increased campaign engagement by developing and implementing targeted marketing strategies.
• Improved conversion rates with creation of compelling content for various channels.
• Assignment was to evaluate customer's response towards products and services which was achieved through sentimental analysis