Store Clustering:

How to meaningfully categorise different store types to help expansion and refurbishment decisions

A Leading Fast Food Chain was looking for a data-driven classification/clustering of their restaurant estate to help identify opportunities within and across their stores to generate additional ROI.

They also wanted to understand where the gaps and challenges were in their data.

Solutions

Segmentation Model

Looked at a total of 132 variables to be narrowed down to 31 to build a segmentation model

Clustering

Used a k-means algorithm which considered all of the different attributes to group the most similar restaurants together, split across 12 clusters

Recommendations

Set out the data challenges faced and provided recommendations for addressing these in order to improve efficiency and accuracy of analysis.

Outcomes

• A filterable report where a leading fast food chain could investigate and benchmark:

            restaurant clusters, and

            restaurants within their respective clusters.

• A set of ‘pen portraits’ of each of the clusters setting out key attributes including performance, customer types

• Ability to use these clusters to optimise their estate planning operations and improve ROI. This helped to roll out EOTF and new branch   openings in areas where the analysis suggested strong performance.

© 2019 Beyond Analysis Ltd

  • White Twitter Icon
  • White Facebook Icon
  • White LinkedIn Icon

Email:  info@beyondanalysis.net

UK:   +44 (0) 203 432 4323  

Australia:   +61 (0) 411 423 459

Lithuania: +370 620 75752