Ingestion
Python
Repeatable extraction, normalization and quality checks.
Commerce Lens
A compact intelligence layer combining revenue, customer and product signals into a more useful ecommerce operating view.
Concept case study
signal / overview
SIGNAL
Revenue$284kOrders4,219ROAS4.8×
Client typeEcommerce
ScopeAnalytics
SourcesMulti-store
FocusCommercial insight
01 / The briefOne commercial picture
Connect store data to business questions.
Orders, customers and product data often live in separate exports and dashboards. Commerce Lens brings the core signals into one consistent model.
The experience prioritizes trend, exception and comparison so operators can spot what changed and where to investigate next.
02 / TechnologyWhat powered the delivery
Built with the right tools for the job.
The stack is documented here so the case study explains how the work was delivered — not only what it looked like.
Modeling
SQL
Shared commerce definitions across orders, products and customers.
Warehouse
PostgreSQL
Central analytical storage for multi-source commerce data.
Transformation
dbt
Tested models that turn raw commerce records into reusable business metrics.
Reporting
Power BI
Commercial monitoring, trends and investigation views.
Tools are selected around the brief, maintainability, performance and the client team.
03 / Case studyChallenge / approach / solution
From problem to production.
A case study should show the thinking between the brief and the final interface — not only the polished screens.
01
01 / Challenge
The starting point
A compact intelligence layer combining revenue, customer and product signals into a more useful ecommerce operating view.
02
02 / Approach
How the system was shaped
Orders, customers and product data often live in separate exports and dashboards. Commerce Lens brings the core signals into one consistent model.
The experience prioritizes trend, exception and comparison so operators can spot what changed and where to investigate next.
03
03 / Solution
What the final direction delivers
A reporting layer that turns fragmented store data into one commercial picture teams can actually use.
04 / OutcomeConnected intelligence
A reporting layer that turns fragmented store data into one commercial picture teams can actually use.
3Core data domains
1Shared KPI layer
24hRefresh target
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