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Local Shops Analytics

Live in production

US ยท Seattle ยท Business Analytics

"Shop Local", a multi-state US grocery and local-shop retail network, had sales, returns and customer data scattered across store-level systems with no way to compare performance across stores, categories or customer segments โ€” nobody could answer which store or product line was actually losing money without a manual pull per store. We built an Azure Data Factory + Azure Functions ETL pipeline that lands and transforms the data into Azure SQL Database on a schedule, then a Power BI suite on top โ€” a Summary page, a Product Analysis drilldown, a Customer Analysis view, and a store-level map โ€” so category managers and store ops can see revenue, returns and customer behaviour down to a single store and product.

Build something like this

ETL

Azure Data Factory + Azure Functions

WAREHOUSE

Azure SQL Database

REVENUE TRACKED

$2bnacross 8,803 customers

COVERAGE

Store-level drill-down, US-wide

Real screens, not mockups

Summary โ€” total customers, revenue, returns and best/least performing store, brand and product
Summary โ€” total customers, revenue, returns and best/least performing store, brand and product
Monthly trend โ€” sales, returns and product revenue month over month across two years
Monthly trend โ€” sales, returns and product revenue month over month across two years
Product Analysis โ€” category to brand to product drilldown, with low-fat/recyclable breakdown
Product Analysis โ€” category to brand to product drilldown, with low-fat/recyclable breakdown
Customer Analysis โ€” revenue by gender, marital status, occupation and top customers by state
Customer Analysis โ€” revenue by gender, marital status, occupation and top customers by state

What the system does

Automated ETL

Azure Data Factory pipelines trigger Azure Functions to clean and load store, product and transaction data into Azure SQL Database on a schedule โ€” no manual exports.

Executive summary

Total revenue, sales quantity, returns and best/least performing category, product, brand and store in one page.

Product analysis

Sales and returns drilldown by category, brand and product, plus a low-fat/recyclable breakdown for compliance-minded buyers.

Customer analysis

Revenue by gender, marital status and occupation, plus a top-15 customer leaderboard by sales quantity.

Store drill-down map

Every store plotted geographically, sized by quantity sold, drillable from state down to individual address.

How it is put together

Ingestion

Azure Data Factory pipelines pull raw store, product and transaction data on a schedule

Transform

Azure Functions clean, validate and reshape the data before load

Warehouse

Azure SQL Database holds the modelled star schema

Reporting

Power BI connects live to Azure SQL โ€” Summary, Product Analysis and Customer Analysis pages

Stack

  • Azure Data Factory
  • Azure Functions
  • Azure SQL Database
  • Power BI

Where it stands

Pipeline
Fully automated Azure Data Factory + Functions ETL โ€” no manual data pulls
Scale
$2bn in tracked revenue across 8,803 customers and 815K units sold
Granularity
Store-level drill-down from national total to a single address

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