Real-Time Retail Sales Analytics Across 12,000+ Stores

Real-Time Retail Sales Analytics Across 12,000+ Stores
SKUs Tracked

150+

SKUs Tracked

Retail Chains Covered

12

Retail Chains Covered

Stores Monitored

12,000+

Stores Monitored

Automated Data Processing

4x

Automated Data Processing

Background The Client Challenge

Our client is the local branch of a global consumer goods group a business operating in 180+ countries, serving roughly 800 million customers, and employing 55,000 people worldwide. This particular branch handles distribution for 150+ products through a network of 12 major retail partners, covering more than 12,000 individual stores.

Running a network that size meant dealing with a constant stream of retailer data, none of it arriving the same way twice:

  • Every one of the 12 retail partners sent sales figures in its own format, on its own schedule, so analysts spent most of their time wrangling spreadsheets instead of analyzing anything
  • There was no single place to see stock levels, sell-in volumes, and sell-out volumes together across the full network of stores and products
  • Spotting a sales trend, comparing one product or store against another, or estimating where growth might come from all required pulling numbers together by hand
  • Growth metrics that should have been simple like how a store's sales had changed month over month took real calculation time and were an easy place to make mistakes
  • Sales and marketing staff had no way to explore the data themselves; any new question meant a new manual request

The company partnered with Versich bringing 10+ years of expertise in data analytics and BI services to design and build a unified data analytics solution that would process and consolidate all retail sales data and deliver advanced sales analysis capabilities across the full store network.

Our Solution

Architecture and Data Intake

Architecture and Data Intake

  • Mapped the file formats and submission patterns used by each of the 12 retail partners to understand what the ingestion process needed to handle
  • Designed a structure built around three core measures retailers actually report what's shipped into a store, what sells out to consumers, and what's left sitting on the shelf so every retailer's numbers could be compared on equal footing regardless of their original format
  • Built a browser-based portal, hosted on the company's own IIS server, where the sales and operations team can upload retailer files directly, see exactly what's been submitted, and correct mistakes by rolling a bad upload back without IT involvement
  • Gave the portal a full upload history, so anyone can trace back what was submitted and when if a number ever looks off

Centralising the Data

Centralising the Data

  • Stood up an MS SQL Server database as the central store for everything coming in from all 12 retailers the first time this data has lived in one place rather than scattered across retailer-specific files
  • Built validation into the ingestion process itself, catching format and consistency issues before bad data could reach the warehouse
  • Structured the warehouse so every store and SKU combination is tracked consistently, regardless of which retailer originally supplied the data
  • Gave the operations team a manual override to push fresh data through to the analytics layer outside the normal refresh schedule, for whenever something needs checking immediately

Turning Data into Analysis

Turning Data into Analysis

  • Used SQL Server Analysis Services to build a multidimensional model on top of the warehouse, refreshed on a weekly and monthly cycle
  • Structured the model around the dimensions that actually matter for this business time period, where in the retail hierarchy a number sits (chain, then store), and product category so any cut of the data is a few clicks away
  • Built in the growth and performance calculations the team used to compute by hand, so a number like "how did this store's sales change this quarter" is now instant rather than a spreadsheet exercise
  • Connected the model to Power Pivot in Excel, so sales and marketing analysts can build their own views, slice by any combination of store, product, retailer, or time period, without ever opening a support ticket

Business Impact

One network, one dataset

One network, one dataset

Sales, stock, and shipment data from all 12 retail partners and 12,000+ stores now lives in a single warehouse, replacing what used to be a pile of inconsistent retailer files.

Hours of manual work, gone

Hours of manual work, gone

Growth and performance figures that analysts used to calculate by hand are now generated automatically the moment new data lands.

Self-service for the team that needs it

Self-service for the team that needs it

Sales and marketing analysts explore the data directly in Excel, cutting it by store, product, retailer, or time period without waiting on IT.

A real view of what's working

A real view of what's working

With trends, store performance, and growth potential visible across the whole network, leadership can make faster calls on sales strategy, marketing spend, and retail partnerships.

REVIEWS

What Clients Say About Us

5.0
Full starFull starFull starFull starFull star

We hired Versich to rebuild our analytics stack after an internal project stalled. They came in, assessed the situation quickly, and delivered production-ready Power BI dashboards within weeks. Their DAX knowledge and data modelling skills are exceptional.

Marcus Webb

CTO
5.0
Full starFull starFull starFull starFull star

Versich understood our finance workflows from day one. They built dashboards that connected directly to our ERP and gave our leadership team real-time visibility into cash flow, margins, and budget vs actuals. The quality of the work and the speed of delivery were both outstanding

Priya Nair

Finance Director
5.0
Full starFull starFull starFull starFull star

Before Versich, our reporting was scattered across spreadsheets with no single source of truth. They built us a Power BI environment that connects our warehouse, finance, and sales data in one place. Our operations team now makes decisions in hours instead of days

Daniel Okonkwo

Head of Operations