Behavioural Cluster Planning™

 The Retail Industry's only behavioural-based clustering solution for consumer insight and analysis

Overview

In the past clustering has been a manual process, requiring the mathematical expertise of scientists. This has led to most retailers grouping stores based on top down attributes such as: Store size, Total sales, retail banner, supply chain, common demographic or geographical groups as these are easy to extract and analyse

By using average attributes to group stores, it enables a retailer to operate at increased efficiency aiming to satisfy the needs of the average consumer better than their competitors. The risk when using this approach is failing to meet localised demand on a store-by-store basis, resulting in a potential loss in sales and customer disillusionment.

A behavioral clustering approach means the clustering process begins with the consumer. Individual categories are analyzed using granular product performance data, be it historic sales, market data or manufacturer data. This enables you to analyse the performance of a category across the chain to group stores with similar performance.

Galleria’s Behavioral Clustering approach was specifically developed to be used by category and product managers. It enables you to add mathematics to your merchandising without the mathematician. The rich analytics of our Behavioral Cluster Planning™ enables you to cluster stores based on how products and categories perform, naturally identifying clusters in just a few easy steps. Further analysis can reveal more detail on how clusters perform relative to others enabling greater understanding as to why clusters perform the way they do.

Clusters can even be plotted on Google Earth allowing for a quick visual of where clusters lie relative to each other. Overlays can then be used to determine local factors that would be absent using normal methods such as proximity to schools, hospitals and even competitors.

Key Benefits

• Easy Clustering for ease of use and faster analysis

• Behavioral-based performance analysis, not pre-set assumptions and constraints

• Ability to automatically create bottom-up category clusters

• Combination of top-down and bottom-up clustering facilitates key performance analysis

• Ability to apply rules to reflect retail objectives and constraints

• Clusters of stores more accurately reflect customer demand

• Days of stock are reduced, availability is improved

• Sales opportunities are optimized

• Fast return on investment

• Export identifiable clusters to Google Earth for further analysis