Abikshit Nayak

Predictive Churn Modeling – STRAIGHT TALK
Python (Pandas, Scikit-Learn), SQL (Snowflake/Data Lake), Seaborn

• Developed and deployed a predictive churn model to identify high-risk active customers likely to “Port-Out,” enabling proactive retention interventions.
• Built a longitudinal time-series dataset using advanced SQL window functions (LAG/LEAD) to generate behavioral features like data-usage variance and accurately label churn outcomes.
• Implemented a Random Forest Classifier with cost-sensitive learning and threshold optimization to effectively manage , improving precision in high-value customer detection.
• Designed and evaluated a customer retention intervention using experimentation-driven analysis—segmented customers, assessed behavioral trends via IVR logs, and built ROI models to support product decision-making for campaign rollout.
• Conducted NLP-based sentiment insights from Qualtrics survey data to uncover key drivers of dissatisfaction and loyalty, and aligned recommendations to product teams for measurable CX improvements.
• Communicated insights through clear storytelling and visualizations, influencing senior leadership on a customer-centric strategy.

• Led full-service market research projects, with a focus on custom/ad-hoc research, from initial proposal to final report.
• Collaborated cross-functionally to understand business needs, translate them into data-driven projects, and communicate insights through interactive visualizations and presentations.
• Conducted data wrangling and analysis to ensure accuracy and relevance of research findings.
• Authored detailed documentation and reports, ensuring reproducibility and enabling stakeholders to derive actionable insights from complex datasets.
• Built and maintained Marketing Mix Models (MMM) to deliver actionable insights on media performance and support client marketing strategies.
• Developed regression-based models to quantify the impact of media channels and other marketing activities on sales outcomes.
• Delivered insightful presentations and communicated strategic recommendations clearly to clients and stakeholders.

• Designed and implemented optimized Python scripts, reducing delivery times by 30% through automation and efficient processing.
• Coordinated with cross-functional teams, leading to a 25% increase in project delivery speed by optimizing workflows and leveraging tools such as SQL and Excel.
• Explained technical data analysis results through visualizations using dashboards, ensuring clear understanding for non-technical stakeholders.

• Planned and managed project tasks, conducting rigorous quality checks to maintain high data integrity using SQL and Python.
• Identifying and rectifying discrepancies in raw data to make it suitable for analysis