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PIESTACK

Retail data analytics

Clearer Insights Across Products, Inventory, and Stores.

Sales come from point of sale, stock from inventory systems, and costs from finance. PieStack brings that information together so you can understand product performance, stock movement, and store operations in one consistent view.

Book a Discovery CallSee the challenges we solve

Client feedback

The team has been fantastic to work with, providing great insights and feedback on tasks. Their dedication and commitment have been refreshing.
Matt SaricicekFounder, BiyoPOS (point-of-sale software)

Common challenges

Where retail data gets stuck

The problem

Why retail reporting breaks down

  • Sales and stock tell different stories

    Point-of-sale, inventory, and finance systems each count differently, so the numbers rarely reconcile.

  • Branches can't be compared fairly

    Without shared definitions, store performance comparisons turn into debates about the data.

  • Problems show up after the season

    Excess stock and missed sales are only visible once it's too late to act.

How PieStack fixes it: one governed pipeline from source to decision

Data SourcesLandingBronze · RawSilver · CleanedGold · Business-readyERPSQL ServerExcel / CSVPoint of saleInventoryProductionSchema checksDuplicatesValidationAnomaly flagsClean & standardizeReconciliationReports · Dashboards · AIContinuous pipeline monitoring

What we do

How PieStack helps retailers

01

Sales and store performance

Comparing stores and branches is difficult when each one reports differently.

How we help

  • Agree shared definitions of sales and performance across locations
  • Standardize data from different point-of-sale and ERP systems
  • Report trends by day, week, and season
  • Store and branch performance reporting
  • Consistent KPIs across locations
  • Sales trend views
Store performance
Store AOn time
Store BLate file
Store CDifferent format
  1. StandardizeMap each store's data to one format
  2. Shared MetricsApply one definition of sales and margin
  3. ValidateCheck totals against store reports

Branch comparison

DailyWeeklySeasonal
02

Inventory and stock movement

Stock and sales data are disconnected, so availability and ageing are spotted too late.

How we help

  • Connect stock data with sales across stores and warehouses
  • Highlight slow-moving, excess, and out-of-stock items
  • Show stock movement and ageing by location
  • Stock-on-hand and movement reports
  • Slow-moving and excess stock views
  • Availability by store
Stock ageing
  1. Supplier
    Delivery lagReceipts recorded days after goods arrive
  2. Warehouse
    Stock mismatchSystem stock differs from shelf stock
  3. Store
    Missed salesFast movers out of stock in key stores
  4. Customer
03

Product, category, and margin analysis

Teams can see what sells, but not what actually makes money.

How we help

  • Combine sales with cost data where it exists in your systems
  • Confirm what the data can support before reporting margin
  • Compare products and categories on consistent measures
  • Product and category performance
  • Margin reporting where cost data is available
  • Category trend views
Category performance
POS & financeSalesProduct costsDiscounts
Cost coverageCategoriesReturns
  1. Data AggregationCombine sales, costs, and discounts
  2. Margin ModelCalculate margin where costs exist
  3. Data ValidationFlag items with missing costs
Category reportsProductCategoryStore
04

Promotion and product performance

It's hard to tell whether a promotion created new sales or simply moved them.

How we help

  • Line up promotion periods with product and store sales
  • Compare performance before, during, and after promotions
  • Look for shifts between related products
  • Promotion performance views
  • Product comparison reports
  • Store-level breakdowns
Promotion analysis
Promo calendarDates mapped
POS salesConnected
Stock levelsGaps found
  1. Align PeriodsLine up promotions with sales and stock
  2. CompareBefore, during, and after each promotion
  3. Check ShiftsSpot sales moved between related products

Promotion analysis

By productBy storeBy period
05

Reporting automation

Sales and inventory reconciliation depends on manual spreadsheet work every week.

How we help

  • Load data automatically from POS, ERP, and inventory sources
  • Add reconciliation and data-quality checks
  • Schedule reports for store and head-office teams
  • Automated data pipelines
  • Reconciliation checks
  • Scheduled reports
Automated pipeline
POS exportManual export
ERPConnected
Excel stockFormat errors
  1. Scheduled IngestionLoad POS, ERP, and inventory data automatically
  2. ReconciliationMatch sales and stock movements
  3. Quality ChecksFlag missing and duplicate records

Scheduled reports

StoreHead officeInventory

Business questions

Questions your data should be able to answer.

  • Which products and categories sell well, but carry low margin?
  • Which stores are running out of fast-moving items, and which are overstocked?
  • How much stock has not moved in the last 60 days, and in which branches?
  • Did last month's promotion increase sales, or just shift them between products?
  • Why do sales and inventory systems disagree on what was sold?
  • How do our branches compare on the same metrics, week by week?

Typical data sources

  • Point-of-sale systems
  • ERP and accounting systems
  • Inventory and warehouse systems
  • Microsoft SQL Server and other databases
  • Excel and CSV files
  • E-commerce platforms
  • Other business applications

Retailers use very different point-of-sale, ERP, inventory, and finance systems. We start by mapping the sources you actually use.

Technical walkthrough

Building a governed lakehouse from raw, messy files

The same layered approach we use to turn inconsistent sales and stock files into analytics-ready tables on Databricks.

Read the walkthrough

We also build and grow e-commerce operations for retail and consumer brands. See our commerce case studies.

How we work

Our approach: start focused, then expand.

  1. 01

    Understand the business challenge

    We start with the decisions you need to make and the questions your team cannot answer easily today.

  2. 02

    Connect and prepare the data

    We access the relevant sources, then clean, standardize, and model the data around agreed metrics.

  3. 03

    Deliver useful analytics and insights

    Your team gets reports, dashboards, or conversational analytics, validated against your own numbers.

  4. 04

    Expand into production and more use cases

    Once the first use case proves its value, we harden it for daily use and extend it to new areas.

FAQ

Retail analytics FAQs

We use different systems in different branches. Is that a problem?

No. Mapping and standardizing data from different systems is a core part of our work. We agree consistent definitions so branches can be compared fairly.

Can you report on margin and profitability?

Yes, where cost data is available in your source systems. We confirm what the data can support before committing to margin reporting.

Which technology do you use?

We choose technology based on your needs. Databricks is an important platform behind our current solutions, particularly where several data sources need to be combined and governed.

How do we get started?

Book a discovery call. We will discuss your sales and inventory reporting and suggest a practical first use case, which can be a focused pilot such as the Lakehouse Experience.

Start with a focused pilot

One business question, one or two data sources, and a working demonstration.

Explore the Lakehouse Experience

Talk to our team

Tell us about your retail data challenges and we'll suggest a practical starting point.

Book a Discovery Call