Why use a clustering solution
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Ability to cluster at a single category or multiple category levels.
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Enhance customer satisfaction through correct store or cluster product ranging.
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Determine successful products in clusters.
Why we use a "bottom up" approach
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Store clusters have traditionally been determined using "top down" attributes such as geographical regions, store size, sales value, store locality, demographics or supply chain depots. However, this approach may only result in operational savings and may not meet consumer expectations.
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The "bottom up" attributes analyse the product sales mix by examining category, sub category, and sector attributes, among others. This analysis reveals product trends, consumer behaviour, shopping missions, and localised/regional assortments.
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Not only will this achieve operational savings, but will also satisfy consumer expectations.
What we deliver
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Our solution utilises a bottom-up approach to group stores based on the user-nominated sales mix.
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The k-means method is utilised to identify the grouping of stores.
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The solution allows for the creation of several clusters by utilising product attribute drivers specified by the user.
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The solution can identify the most accurate cluster by evaluating the proximity of stores to the cluster's centre.
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Clusters can be named by users, and changes can be made manually or excluded.
