# Mapping emerging demand to localise a retailer's vegan range

> A leading UK grocery retailer · Retail & Consumer

QuantSpark built a probabilistic demographic model and heatmap to estimate vegan population density at postcode level, informing store-specific ranging that was rolled out nationally across more than 750 stores.

## At a glance

- **750+** stores in the national rollout

## What was the problem?

A leading UK grocery retailer, with more than 1,000 convenience and supermarket stores, wanted a robust way to localise its vegan-related range at postcode level. As an emerging category, it needed to anticipate ranging demand store by store rather than apply a blanket assortment.

## What did QuantSpark do?

QuantSpark developed a probabilistic demographic demand model, drawing on a literature review of vegan population characteristics, open-source location intelligence datasets, UK census data and postcode boundaries. Machine-learning-inspired methods estimated demographic density and built a heatmap of likely vegan population. Natural language processing on the range assortment apportioned the probability of products belonging to a vegan basket, and a supply-demand gap model at store level produced store-specific ranging and space recommendations. The work analysed more than 500 million product-level transactions over five years.

## What changed?

The model and location intelligence module informed the retailer's entire vegan ranging strategy, which was subsequently rolled out nationally across more than 750 convenience and supermarket stores. The approach is reusable for other customer-profile demographics using open-source data.

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Canonical page: https://quantspark.ai/case-studies/location-intelligence-vegan-ranging
More about QuantSpark: https://quantspark.ai/llms.txt