What to look for in segmentation output
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This document discusses segmentation analysis and how to interpret the results. It provides guidance on what to look for in segmentation output, such as whether segment averages are above or below population averages and how membership is shown. It also gives a template for expressing the clusters discovered in a segmentation analysis by describing the clustering variables, outcome, number of clusters identified, and characteristics and recommendations for each cluster.
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What to look for in segmentation output
- 1. Reading Segmentation output What to look for in Segmentation output ?
- 2. Hands on Segmentation¡ Using Kmeans to find clusters 3 Segment average Is it above or below population average ? Hiow does this help characterise the segment? 4 Shows membership of each segment 1 2 Execute clustering algorithm Shows cluster statistics
- 3. Segmentation Business Narrative template How to express Clusters discovered ? ¡°A segmentation analysis was conducted to examine the behavioural clusters. ? 4 < clustering vectors > variables were simultaneously entered into the model: Humidity, Solids, Viscosity, temperature and past defect density count. < outcome > ? Together, these 4 < predictor count > vectors resulted in 5 clusters< Cluster count> ? The outcome 5 clusters discovered were labelled as follows ? Cluster-1 ? Cluster-2 ? Cluster-3 ? Cluster-4 ? Cluster-5 ? Cluster-1 characteristics ? Actions recommended would be