Case study 03 · Retail & data
Retail Insights
A retailer ran on spreadsheets and overnight exports, and the numbers always arrived late. We turned them into four live dashboards the whole team opens every morning.
Try it: switch dashboards, pick a region, hover the chart. Sample data, not the client's.
dashboards: sales, deliveries, returns, and which customers make money.
every morning the numbers are fresh, before the first store opens.
to plan a season or a promotion. It used to take weeks.
manual exports. Nobody copies data into a spreadsheet any more.
By the time the report arrived,the week was already over.
Stores in several regions, an online shop and a warehouse, each keeping its own numbers. Someone had to export them, clean them up and paste them together before anyone could see how the business was doing. So every decision was made on last week's picture.
- MonExports pulled from each system
- TueAn analyst cleans and merges spreadsheets
- WedReport emailed, already questioned
- ThuMeeting argues about whose number is right
- FriDecision, one week late
- 02:00Data collected and checked, automatically
- 06:00Dashboards refreshed
- 08:30Everyone opens the same numbers and acts the same day
Every night,nobody touches it.
Tap any step to see what happens there. The whole run finishes while the stores are closed, and a bad batch of data is stopped before it reaches a dashboard.
Check. Duplicates are removed, store and product names are matched up, and anything that looks wrong is held back and flagged instead of quietly ending up in a report.
Each screen answersone question.
We did not start from charts. We asked each team what they check first thing in the morning, and built one screen per answer.
Are we selling?
Revenue, orders and basket size by day, store, region and channel. Store managers see their own store first.
Checked by Store managersIs it arriving on time?
On-time rate, late orders, and which warehouse or carrier is causing them, before customers start calling.
Checked by OperationsWhat comes back, and why?
Return rate and reasons by product, so a sizing problem or a damaged batch shows up in days, not at stock-take.
Checked by BuyersWho actually makes us money?
Profit per customer after returns, delivery and discounts. The one nobody had before.
Checked by FinanceThe biggest customerwas not the best one.
Sales reports rank customers by how much they buy. But some of the biggest buyers also send the most back, need the most deliveries and only buy on discount.
Once returns, delivery and discounts sit in the same place as sales, profit per customer can be worked out for the first time. Flip the switch to see what changes.
The usual report. Northgate looks like the best customer in the business.
New question?No new project.
Most dashboard projects stop at the dashboards, and the next question means starting again. Here the real work sits underneath: one organised home for all the data, refreshed every night.
So when finance asks about margins by supplier, or a buyer wants returns by colour, the answer is a new view on data that is already there, not another round of exports.
Map the data
Every system that holds a number, who owns it, and which reports people actually rely on.
The nightly pipeline
Automatic collection and quality checks, with alerts when a source is late or looks wrong.
One data home
A central database where sales, deliveries, returns and customers link up by order.
Dashboards and training
Four dashboards built with each team, then a handover so they can filter and explore on their own.
For the technical reader.
Runs and schedules the nightly pipeline, retries failed steps and sends alerts.
The central data warehouse, modelled as a star schema around orders.
Pre-built tables for sales, products, customers and stores, so dashboards stay fast.
The four dashboards, hosted on Azure, filterable by region, store and week.
Common questions
What did SeedInov build for the retailer?
A nightly data pipeline, a central data warehouse and four Power BI dashboards for sales, deliveries, returns and customer profit.
Which technology does it use?
Apache Airflow, PostgreSQL with a star schema, data marts, and Power BI on Azure.
How fresh are the numbers?
Data is collected and checked overnight, and the dashboards are refreshed by 06:00 every day.
Is the data on this page real?
No. The interactive dashboard uses sample data, not the client's.
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