Suhaskrishna Reddy | Analytics Portfolio
MSc Business Analytics student at the University of Exeter with UK industry experience delivering data analysis, business intelligence, and market insights to support commercial and operational decision-making. Experienced in Power BI, SQL, Excel, and Python, with a strong focus on transforming data into clear, actionable insights for stakeholders.
Student Housing Market Analysis – Exeter, UK | Data & Business Analytics Project
Business & Data Analyst – MSc Business Analytics Project
Delivered an applied business and data analytics study of the student rental housing market in Exeter, using primary survey data to identify the key drivers of satisfaction, affordability, and service quality. Quantitative analysis and qualitative thematic analysis were combined to produce stakeholder-ready insights and practical recommendations for universities, landlords, and local decision-makers, supporting evidence-based improvement of student accommodation services.
AI-Enabled Student Mental Health & Wellbeing Platform – UK Market
Business & Data Analyst – MSc Business Analytics
Delivered an end-to-end business and data analytics study to design and validate an AI-enabled student mental health platform for the UK higher-education market.
Analysed primary survey data (n=25) and secondary market research to quantify demand, identify service gaps, and assess pricing feasibility.
Built a data-driven business model using market sizing, customer segmentation, and competitor benchmarking, demonstrating strong product-market fit (84% adoption intent) and a scalable solution priced at £4.99/month.
Customer Segmentation & Strategic Positioning for MetroMart (UK Retailer)
Business Analytics Consultant – Customer Segmentation & Strategy
Led an end-to-end customer segmentation project for a UK multi-channel retailer (MetroMart) using survey data from 380 customers to support a data-driven marketing strategy.
Applied data preprocessing, Z-score normalization, and Euclidean distance modelling, followed by hierarchical clustering (Ward.D2) and k-means clustering to identify meaningful customer groups.
Validated optimal segmentation using silhouette analysis, elbow method, and NbClust indices.
Developed and evaluated three-cluster and four-cluster solutions, and selected the three-cluster k-means model based on statistical performance, balance of cluster sizes and business interpretability.
