What are store clusters?

Clustering is a key pillar in Endeavour’s proposition to deliver the right products, in the right store, for the right customers. Here we talk to Assortment Optimisation Manager, Mark McNaught, on how we use them to drive optimal outcomes for both our customers and our suppliers.

The opportunity to understand common trading patterns across our network allows us to adjust new and existing products in line with our customer’s expectations.

How are they determined?

Our clustering model is driven by the way our customers shop with us. Historical product sales trends and machine learning are used to assemble our network in such a way that the resulting stores in a group (cluster) are more alike than those in other groups. The model allows for known variances across our customer base by applying an initial state lens, before being run at a planogram category level – Red Wine, Premix, Craft Beer, and so on. This allows delivery of a highly tailored range across the different locations within each store.

How do we use clusters?

The outcome of clustering are variable sized groups of stores across our network that are known to have similar customer behaviour and product trading patterns. We are able to leverage these known patterns to build cluster-level demand forecasts that help adjust product distribution across the varying fixture sizes that exist across stores within each cluster.

This process allows Endeavour to continue to deliver our customers the largest possible product catalogue, targeting products to the right clusters at the right time, and adjusting product distribution inline with customer interest. Recent examples include the expansion of Asian Spirits, which started as small subsets of clusters that now has an expanded, and tailored offering due to increasing customer demand.

Inversely, where product innovation may have traditionally started at a national level - like Craft Cider - our cluster demand models have reduced distribution to the clusters where it is most impactful.

Customer-driven insights

As we continue to grow our product and customer attribution datasets, we are able to deliver more insights on the targeted opportunities across our clusters. Ability to group products based on their most granular attributes allows improved understanding on which clusters have the greatest likelihood of commercial success for new product developments, and where to further expand in the future. This seeks to improve the product life cycle, allowing distribution growth in line with quantified success and customer interest.

The use of clustering within Endeavour is designed to provide more avenues, with greater granularity, to understand customers and products across our store network. These understandings help drive more optimal distribution adjustments and targeted new product decision making. These insights will continue to become more granular as we grow our product attribution catalogue and develop our systems to provide store specific forecast models.