How To Easily Cluster Customers With Kmeans
Master the art of customer segmentation and unlock powerful insights to drive your business forward. By learning K-means clustering techniques, you'll gain the ability to effectively group your customers based on key characteristics, allowing you to tailor your marketing strategies and product offerings with precision.
Customer Segmentation with Clustering
Customer segmentation with clustering is a data-driven approach to divide a company's customer base into distinct groups with similar characteristics or behaviours...More
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K-means clustering is a popular unsupervised machine learning algorithm used for grouping similar data points together. It aims to partition n observations into k clusters, where each observation belongs to the cluster with the nearest mean (centroid).
The algorithm works iteratively. First, k initial centroids are randomly chosen. Then, each data point is assigned to the nearest centroid, forming k clusters. The centroids are then recalculated as the mean of all points in each cluster. This process of assigning points and updating centroids repeats until the centroids no longer move significantly or a maximum number of iterations is reached.
K-means is widely used in various applications, including customer segmentation, image compression, and anomaly detection. Its simplicity and efficiency make it a go-to choice for many clustering tasks. However, it does have limitations, such as sensitivity to initial centroid placement and difficulty with non-globular cluster shapes.
Hi. Welcome. And in this video, you're going to have a look at how you can cluster the data to find different segments with inside of your marketing customer base. This is important because you can, for example, take data, analyze that data and figure out exactly what different types of audience segments you have and how you might build effecting marketing strategies. Based on that different types of personas or groups. One tactic that we're going to be using here.
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