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Retail Sales Analytics

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Case Study

Driving Better Decisions Through Data

This case study highlights how Amesium Analytics transforms raw data into meaningful insights that support smarter business decisions. By analysing challenges, implementing tailored data solutions, and delivering clear outcomes, we help organisations improve efficiency, strengthen operations, and achieve measurable growth.

Retail Sales Analytics

Modernising sales reporting with real-time insights across 200+ UK retail locations.

Client

  • UK Retail Chain

Duration

  • 12 Weeks

Team Package

  • Standard Package (3-Person Team)

INTRODUCTION

We worked with a leading UK retailer facing delays and inefficiencies due to manual, Excel-based reporting. Their store managers had limited visibility of daily sales performance, which slowed down operational decision-making across more than 200 locations.

THE CHALLENGE

The retailer relied on scattered data sources and manual reporting processes, resulting in:

  • Slow, inconsistent sales reporting
  • Limited visibility across stores and regions
  • Heavy manual effort for daily and weekly reports
  • High dependency on Excel files
  • No centralised analytics platform

They needed a scalable, automated solution to improve reporting accuracy and speed.

THE SOLUTION

We delivered a modern Azure analytics platform that unified all store-level data, automated ingestion, and enabled real-time performance dashboards

  • Azure Synapse Analytics for central data modelling
  • Automated pipelines using Azure Data Factory
  • Unified semantic layer for sales, inventory, and store KPIs
  • Power BI dashboards for regional and store managers
  • Daily refresh and data validation automation

The new platform replaced manual Excel processes and created a single source of truth for sales analytics.

OUR APPROACH (4 Steps)

1. Discovery & Assessment

Analysed existing reports, data sources, and operational workflows across 200+ stores.

2. Azure Architecture Design

Designed a scalable Synapse + Data Factory architecture to centralise sales, POS, and inventory data.

3. Data Pipeline & Model Development

Built automated ingestion and transformation pipelines, creating a clean semantic model for reporting.

4. Dashboard Delivery & Handover

Developed Power BI dashboards for store, regional, and leadership teams, followed by training and documentation.

OUT RESULTS

The retailer achieved measurable improvements across speed, accuracy, and efficiency:

  • 35% faster reporting across all stores

  • £150K annual savings by removing manual reporting overhead

  • 300+ active users accessing dashboards daily

  • Real-time KPIs for store performance and sales trends

  • Improved operational decisions with accurate, automated insights

TECHNOLOGIES USED

  • Azure Synapse Analytics

  • Azure Data Factory

  • Azure SQL

  • Azure Storage

  • Power BI

  • SQL Server

  • Python

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