# Rebuilding macro space planning on a modern analytics platform

> A leading UK supermarket group · Retail & Consumer

QuantSpark replaced slow, hard-to-maintain Excel space-planning models with a bespoke Python analytics platform that is now the backbone of macro space decisions across the estate.

## What was the problem?

A leading UK supermarket group relied on large Excel VBA models to collect bay-space and sales data for food and non-food across its supermarket and convenience stores and to optimise space against historic sales curves. Over the years the models had become slow and opaque, difficult to maintain, audit and run, with some analysis taking weeks to complete.

## What did QuantSpark do?

Working alongside the client's data science and engineering teams, QuantSpark ran a four-stage transformation: optimising the existing Excel tools for speed, interface and accuracy of recommendations; refining the modelling logic around store-specific customer behaviour; building a Python proof-of-concept model that calculates recommendations from location-specific missions, need states and sales curves; and developing a bespoke analytics platform to support the macro space workflow and accelerate the path from scenario planning to implementation.

## What changed?

The bespoke platform is now the backbone of macro space decisions at the retailer, spanning both supermarkets and convenience stores. It substantially augments the internal team's ability to make strategic and operational space changes across the estate and shortens analysis that previously took weeks. The case study reports capability and speed gains rather than a single quantified figure.

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