Retail analytics for teams that trade every day.

Retail is a data rich business run on gut feel. Most UK retailers we work with have plenty of sales data — EPOS, ecommerce, loyalty, sometimes footfall counters — and very little time to turn it into anything a store manager or buyer can act on. That is the gap our retail analytics work is designed to close.

The metrics that matter

Every retailer measures sales. The useful part is what sits around it. Sales per square foot for physical estate. Basket size and units per transaction to understand how customers are shopping. Sell through and stock cover to keep the buying team honest. Margin at line, category and store level to spot promotional damage before the season ends. Footfall and conversion to separate demand from execution.

Ecommerce brings its own set — conversion, cart abandonment, product page performance, returns rate — and any analytics platform worth having should stitch physical and online into one view. Customers do not shop in channels; retailers who insist on reporting in them tend to make worse decisions.

Where the data usually sits

EPOS at head office or in the cloud, ecommerce in Shopify, Magento or a custom platform, stock and purchasing in an ERP, loyalty in a CRM or bespoke system. The right pattern is a small warehouse — Fabric or Snowflake tend to work well for mid market retailers — with a nightly refresh and a clean product and store dimension. From there the reporting layer is straightforward.

How we work with retailers

Retail moves quickly. Our default is to ship a trading dashboard first: the numbers the commercial team wants on a Monday morning, refreshed automatically. Once that is trusted, we extend into buying, stock and marketing analytics. Every piece is scoped to the meeting it needs to support, not built in isolation.

We have worked with clothing, homewares, food, hospitality and multi channel retailers. The metrics differ; the shape of the platform does not.

Related reading

Our Power BI for marketing case study covers the marketing side of retail analytics in detail, and the sales analytics case study walks through pipeline and win rate work that translates directly to a wholesale channel.