E-Commerce Sales Performance & Customer Cohort Analysis
End-to-end analytical project analyzing multi-channel transactional records, calculating customer lifetime value, and identifying revenue bottlenecks.
Retail & E-Commerce
Industry benchmark
PostgreSQL, Power BI, Microsoft Excel, DAX
Reports, SQL & Dashboards
Commercial Problem Statement
Identify top customer cohorts, reduce churn in high-margin categories, and present real-time sales performance metrics.
Dataset & Schema Architecture
Anonymized transactional database containing 250,000+ orders, product SKUs, and return data spanning 24 months.
Note: In accordance with academic honesty and client confidentiality policies, this case study operates on standardized, anonymized synthetic records reflecting enterprise relational schemas.
What the Student Builds & Delivers
Tangible analytical assets completed during this module.
Business Objective
Identify top customer cohorts, reduce churn in high-margin categories, and present real-time sales performance metrics.
- ✓Normalized SQL schema and analytical queries
- ✓Interactive Power BI report with 4 drill-down pages
- ✓Executive summary presentation outlining 3 revenue growth opportunities
Skills Demonstrated
- ✓Relational data querying and window functions
- ✓Cohort retention rate calculation
- ✓Power BI data modeling and dynamic DAX metrics
- ✓Executive KPI visualization
Analytical Methodology Applied
Decomposing general business inquiries into testable mathematical hypotheses and KPI definitions.
Writing multi-table joins and aggregations to extract normalized tabular subsets from the database.
Standardizing date formats, imputing missing values, and validating row-level consistency.
Structuring star schemas or writing vectorized Python transformations to calculate analytical metrics.
Designing intuitive charts with appropriate scales, drill-through pages, and responsive filters.
Presenting quantified commercial conclusions and clear next steps to executive stakeholders.
How to Defend This Project in Technical Interviews
When an interviewer asks, "Tell me about an analytics project you built," use this proven 3-minute structure:
- The Business Challenge: Explain what KPI was lagging or what management wanted to discover.
- Your Technical Contribution: Describe the tools (PostgreSQL, Power BI, Microsoft Excel, DAX) and the specific queries or models you authored.
- The Measurable Insight: State the commercial finding and the operational action recommended.
Build & Defend This Project in Our Live Batches
This project is an integral capstone in the SSSAM Academy Data Analyst Program. Receive 1-on-1 code reviews from experienced analytics faculty.
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Tools Used in This Project
Advanced Excel
Microsoft Excel remains the ubiquitous engine of enterprise calculations, dynamic financial modeling, and operational monitoring.
DatabaseSQL (Structured Query Language)
SQL is the industry-standard language used by analysts and engineers to extract, filter, join, and summarize billions of data points stored in enterprise databases.
Business IntelligencePower BI
Microsoft Power BI transforms raw tables and databases into automated, interactive visuals and drill-through KPI dashboards.
ProgrammingPython for Data Analysis
Python is the leading programming language for data analytics, scientific computation, and exploratory machine learning.
Mathematics & StatisticsStatistics for Data Analysis
Statistics provides the scientific rigor required to separate genuine business trends from random noise, validate sample sizes, and evaluate risk.