Segmentation in Machine Learning

What is Segmentation in machine learning?

Allocating resources in order to keep at minimum CPA – cost per acquisition and, at the same time, to increase return is one of the primary difficulties that marketing teams must overcome. Segmentation, the technique of splitting customers into separate groups depending on their attributes or behavior, makes this possible.

  • Customer segmentation in machine learning can help you save money on marketing initiatives by reducing waste. You’ll be better able to target your campaigns to the proper people if you know which consumers are similar to each other

Other marketing responsibilities, such as up-selling methods, product recommendations, and pricing can benefit from customer segmentation.

Customer segmentation used to be a difficult and time-consuming operation that required hours of human labor combing through several tables and querying the data in the hopes of discovering ways to group customers together.

Machine learning, or artificial intelligence algorithms that detect statistical regularities in data, has made it considerably easier in recent years. Customer data can be processed using machine learning models, which may then be used to find repeating trends across a variety of variables.

Machine learning algorithms may often assist marketing analysts in identifying client subgroups that would be difficult to identify using intuition and manual data analysis.

Customer segmentation is a fantastic example of how artificial intelligence and human intuition may work together to create something better.

The k-means algorithm

Machine learning algorithms come in a variety of flavors, each tailored to a particular task. K-means clustering is one of the techniques that are useful for customer segmentation.

Unsupervised algorithms lack a labeled data against which to evaluate their performance.

  • The basic concept underlying k-means is to group data into clusters that are more similar

The difference between the consumers’ age, income, and spending scores are used to calculate the similarity between clusters in this scenario.

You indicate the number of clusters you want to divide your data into while training a k-means model. The model begins with centroids that are randomly placed variables that determine the center of each cluster.

The model examines the training data and assigns them to the cluster with the closest centroid. After all of the training, examples have been categorized, the centroids’ parameters are re-adjusted to put them in the middle of their clusters.

Additionally to k-means clustering, the elbow approach is an effective data segmentation technique for determining the ideal number of machine learning segmentation clusters.

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Importance of segmentation

By evaluating their distance from each of the cluster centroids, your machine learning model can decide which segment new clients belong to once it has been trained. There are numerous applications for this.

  • Your machine learning model will assist you in determining the segment of your client and the most prevalent products linked with that segment

Machine learning algorithms for customer segmentation will assist you in fine-tuning your product marketing strategies. For example, you may begin an ad campaign with a random sample of clients from various segments. After a period, you may look at which groups are more active and tailor your strategy to only show advertising to those who belong to those parts.

You could also run many versions of your campaign and use machine learning to segment your clients depending on their answers to the various efforts. You’ll have a lot more tools to test and fine-tune your ad campaigns in general.

  • K-means clustering is a machine learning segmentation method that is quick and efficient

However, it isn’t a magic wand that will change your data into logical client categories in an instant. You must first choose the target audience for your marketing efforts and the elements that will be important to them. If your advertisements will be focused on specific locations, for example, geographic location will be irrelevant, and you’ll be better off filtering your data for that region.

Similarly, if you’re pushing a men’s health product, you should filter your customer data to solely include guys and avoid using gender as one of your machine learning model’s attributes.

In other circumstances, you’ll want to offer additional information, such as things they’ve already purchased. In this situation, you’ll need to make a customer-product matrix, which is a table with customers as rows and things as columns, as well as the number of things purchased at each customer and item’s intersection.

If there are too many products, consider establishing an embedding, in which the products are represented as values in a multidimensional vector space.

Machine learning is a powerful tool for marketing and customer segmentation in general. It will almost certainly never be able to replace human intuition and decision-making, but it can help boost human efforts to previously unattainable levels.