Analysis
Python
Cleaning, transformation, analysis and automation.
E-commerce Analytics
A data analysis concept focused on connecting product, customer and order data for better decisions, with emphasis on revenue, AOV, cohorts, product performance and repeat purchase analysis.
Concept case study
signal / overview
SIGNAL
Revenue$284kOrders4,219ROAS4.8×
DisciplineData Analysis
Project typeConcept case study
Primary goalconnecting product, customer and order data for better decisions
Focusrevenue, AOV, cohorts, product performance and repeat purchase analysis
01 / The briefProject brief
A clearer system for connecting product, customer and order data for better decisions.
A concept case study for connecting product, customer and order data for better decisions. The project explores revenue, AOV, cohorts, product performance and repeat purchase analysis with a structured, scalable approach.
This starter case study is written as meaningful portfolio content, not as a claim of completed client work. Add real screenshots, constraints, delivery details and evidence before switching the project status to a live client case study.
02 / TechnologyWhat powered the delivery
Technology and tools
A practical stack for a data analysis project like this, selected around maintainability, workflow clarity and the needs of the experience.
Data querying
SQL
Reliable extraction, joins and business logic.
BI
Power BI
Interactive dashboards and semantic models.
Business analysis
Excel
Flexible operational analysis and validation.
Warehouse
PostgreSQL
Structured storage for reusable reporting datasets.
Final technology choices should follow real project constraints, integrations, hosting, team capability and maintenance requirements.
03 / Case studyChallenge / approach / solution
Problem, approach and solution
A useful case study explains the decisions between the brief and the final result — not only the polished screen.
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01 / Challenge
The problem to solve
The analytics challenge is to turn product and sales insights into information people can trust and act on. The work must support connecting product, customer and order data for better decisions while resolving inconsistent definitions, fragmented sources and dashboards that show numbers without enough context.
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02 / Approach
How the work is structured
The concept starts by defining business questions, KPIs and data grain before visualization. For E-commerce Analytics, the analysis model is organized around revenue, AOV, cohorts, product performance and repeat purchase analysis, with cleaning rules, reusable calculations and validation steps documented before dashboard design.
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03 / Solution
What the concept delivers
The proposed analytics layer combines a clean data model with decision-focused reporting. It supports revenue, AOV, cohorts, product performance and repeat purchase analysis, consistent KPI definitions, drill-down paths and outputs that can be refreshed or extended without rebuilding the analysis from scratch.
04 / OutcomeIntended outcome
This concept demonstrates how ALIFY would approach e-commerce analytics with a clearer strategy, structured delivery and maintainable system. It is intentionally presented as concept work: real screenshots, implementation constraints, client attribution and measured outcomes should be added before the project is promoted as completed client work.
Next / Start a projectHave something worth building?
Bring the brief.
We’ll bring the system.
Tell us what you are building, what is getting in the way and where you want it to go.
Start aproject