Clustering forms part of data mining, which analyses data and transforms it into information that you can understand and use in your retail business. This practice aims to help you to retrieve information in the fastest manner for the discovery of knowledge and unidentified patterns.
Clustering algorithms can be used to conduct a cluster analysis that is tailored to your market environment, business goals and data availability. This allows for the selection of an applicable clustering method that will ensure you achieve the goals you have set out for your category-based clustering.
Finally, you can use various applications such as cluster mapping and cluster profiling to understand each cluster in detail. With this information, you can make strategic, data-driven decisions.
What you can expect in our Cluster Analysis Ebook:
In the first part of this ebook, we take a look at the different clustering algorithms you can use to conduct a cluster analysis. We have discussed a few unsupervised machine-learning algorithms to help you assess each algorithm in terms of their suitability to your business and its goals.
Once you have selected a clustering algorithm, you must choose an applicable clustering method. You can select one or multiple methods/variables to conduct the cluster analysis and group your stores according to applicable factors such as store sales volume, customer demographics or behaviours.
Once you have clustered your stores for a particular product category, you can conduct further analysis on each of your clusters. Although you could always use basic data analysis, cluster mapping and profiling are applications that allow you to understand and utilise the information gathered from clustering.
Did you know that you can drive higher revenues and generate customer-centric assortment and space plans by creating category-based clusters for your stores? DotActiv’s clustering services allow you to add value to your retail customers’ shopping experience and offer them an assortment that meets their product needs.