Data & Business Analyst · JIIT, CGPA 9.3
Turning Data into
Product Decisions.
I'm a B.Sc. (Hons.) Computer Science student at JIIT (CGPA 9.3) focused on data and business analysis. I analyse product data to identify patterns, quantify problems, and recommend measurable improvements — then present findings and recommendations to product and operations teams.
My work spans retention analysis, KPI dashboards, cohort reporting, and product usability research. I've cleaned and analysed large datasets, built BI dashboards for the operations team, and worked with product and engineering teams to clarify requirements and frame measurable questions. Comfortable working with ambiguous product problems and translating them into measurable hypotheses.
A structured, repeatable mental model for turning business questions into data-backed recommendations.
A full walkthrough of how I identified churn-prone customer segments, quantified revenue exposure, and translated findings into actionable retention recommendations.
A subscription-style customer dataset lacked visibility into which users were at risk of leaving and how much revenue was exposed to churn. The objective was to identify churn-prone customer segments and quantify potential revenue loss — turning raw customer data into a prioritised retention view.
The analysis used a structured customer dataset containing key attributes across tenure, billing, and support behaviour. Each field was selected for its relevance to churn likelihood — contract type and tenure as structural risk indicators, monthly charges and payment method for revenue sizing, and support usage as a signal of friction or dissatisfaction.
Before any analysis, the metrics were defined clearly so findings would map directly to retention decisions. The primary metric was customer churn probability — which customers are most likely to leave based on their profile. Supporting metrics gave the business dimension: how much revenue was concentrated in high-risk segments, and whether any high-value customers were disproportionately exposed.
The analysis followed a structured sequence to avoid jumping to conclusions — cleaning first, then understanding distributions, then segmenting and sizing the risk.
The segmentation surfaced four distinct findings, each with a different retention implication. Rather than a single root cause, the data showed that churn risk was concentrated across specific contract and tenure profiles — meaning retention interventions would need to be targeted, not blanket.
Recommendations were grounded directly in the segment findings — each action mapped to a specific at-risk profile rather than a generic retention campaign.
Produced an interactive Power BI dashboard summarising churn segments and revenue exposure, allowing business teams to quickly identify risk areas and prioritise retention strategies. The dashboard translated the full analysis into a format decision-makers could act on directly — without needing to interpret the underlying data themselves.