{"id":2409,"date":"2021-01-20T10:00:05","date_gmt":"2021-01-20T10:00:05","guid":{"rendered":"https:\/\/thenextweb.com\/?p=1334511"},"modified":"2021-01-20T10:00:05","modified_gmt":"2021-01-20T10:00:05","slug":"how-machine-learning-enhances-customer-segmentation","status":"publish","type":"post","link":"https:\/\/www.londonchiropracter.com\/?p=2409","title":{"rendered":"How machine learning enhances customer segmentation"},"content":{"rendered":"\n

One of the key challenges that marketing teams must solve is allocating their resources in a way that minimizes \u201ccost per acquisition\u201d (CPA) and increases return on investment. This is possible through segmentation, the process of dividing customers into different groups based on their behavior or characteristics.<\/p>\n

Customer segmentation can help reduce waste in marketing campaigns. If you know which customers are similar to each other, you\u2019ll be better positioned to target your campaigns at the right people.<\/p>\n

Customer segmentation can also help in other marketing tasks such as product recommendations, pricing, and up-selling strategies.<\/p>\n

Customer segmentation was previously a challenging and time-consuming task, that demanded hours of manually poring over different tables and querying the data in hopes of finding ways to group customers together. But in recent years, it has become much easier thanks to <\/span>machine learning<\/a>, artificial intelligence algorithms that find statistical regularities in data. Machine learning models can process customer data and discover recurring patterns across various features. In many cases, machine learning algorithms can help marketing analysts find customer segments that would be very difficult to spot through intuition and manual examination of data.<\/p>\n

Customer segmentation is a perfect example of how the combination of artificial intelligence and human intuition can create something that is greater than the sum of its parts.<\/p>\n

The k-means clustering algorithm<\/h2>\n
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\"k-means<\/a>
<\/i><\/a>K-means clustering is a machine learning algorithm that arranges unlabeled data points around a specific number of clusters.<\/figcaption><\/figure>\n<\/p>\n<\/figure>\n

Machine learning algorithms come in different flavors, each suited for specific types of tasks. Among the algorithms that are convenient for customer segmentation is k-means clustering.<\/p>\n

K-means clustering is an <\/span>unsupervised machine learning algorithm<\/a>. Unsupervised algorithms don\u2019t have a ground truth value or labeled data to assess their performance against. The idea behind k-means clustering is very simple: Arrange the data into clusters that are more similar.<\/p>\n

[Read: How Netflix shapes mainstream culture, explained by data<\/span><\/a>]<\/em><\/p>\n

For instance, if your customer data includes age, income, and spending score, a well-configured k-means model can help divide your customers into groups where their attributes are closer together. In this setting, similarity between clusters is measured by calculating the difference between the age, income, and spending score of the customers.<\/p>\n

When training a k-means model, you specify the number of clusters you want to divide your data into. The model starts with randomly placed centroids, variables that determine the center of each cluster. The model goes through the training data and assigns them to the cluster whose centroid is closer to them. Once all the training instances are classified, the parameters of the centroids are readjusted to be at the center of their clusters. The same process repeats, with the training instances being reassigned to the finetuned centroids and the centroids readjusted based on the rearrangement of the data points. At one point, the model will converge, iterating over the data will not result in training instances switching clusters and centroids changing parameters.<\/p>\n

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