{"id":10906,"date":"2022-06-21T07:19:21","date_gmt":"2022-06-21T05:19:21","guid":{"rendered":"https:\/\/mio.one\/blog\/what-is-clustering-and-what-are-its-benefits-for-marketing\/"},"modified":"2026-07-09T10:20:36","modified_gmt":"2026-07-09T08:20:36","slug":"what-is-clustering-and-what-are-its-benefits-for-marketing","status":"publish","type":"post","link":"https:\/\/mio.one\/en\/blog\/what-is-clustering-and-what-are-its-benefits-for-marketing\/","title":{"rendered":"What Is Clustering, and What Are Its Benefits for Marketing?"},"content":{"rendered":"\n

Clustering isn\u2019t a new or trendy concept, but it is becoming increasingly important in the world of marketing and advertising, due to the growing amount of data on users, audiences, and customers, and the opportunity that exists today to create more precise and effective clusters for campaigns. Still not sure what clustering your database can do for you? In this post, I\u2019ll explain what it is and what benefits it offers for marketing. Let\u2019s get started. <\/em><\/strong><\/p>\n

Companies and marketing experts often talk about segmentation as a way to fine-tune their communications, since it allows them to tailor their messages based on the common characteristics of that segment.<\/p>\n

We could say that customer segmentation<\/strong> consists of a set of actions through which common characteristics within the customer base are identified and the base is divided into different groups: each of these groups is a customer segment.<\/p>\n

However, clustering<\/strong>\u2014which is often confused with segmentation or seen as no different from it\u2014involves identifying groups of similar customers based on similar variations within each group. This is all done using mathematical models and machine learning<\/strong>. <\/p>\n

What is the main advantage of clustering over segmentation?<\/strong> Well, basically, with segmentation, you only group based on business criteria, which\u2014once they are no longer relevant to defining that group\u2014can make that segmentation less effective. With a cluster, by using mathematical machine learning criteria, the groups are constantly being updated, making them much more effective for any subsequent actions you take with them. <\/p>\n

Today, this method is used primarily in computer science, marketing, the business world, and the arts, and it is extremely effective.<\/p>\n

In our field\u2014marketing\u2014this is particularly important and relevant because, thanks to these clusters, we can group our target audience or our customers\u2014if we\u2019re working on loyalty initiatives\u2014based on complex variables such as motivations, purchasing behavior, interests, and so on\u2026<\/p>\n

Ultimately, the goal of cluster analysis is to accurately segment customers<\/strong> in order to achieve more effective data clustering through personalization<\/strong>.<\/p>\n

An example that everyone is sure to understand is Netflix<\/strong>. As you know, this platform uses a recommendation system based on clustering algorithms. In fact, the company has about 2,000 clusters of communities with similar tastes, which creators use to gain firsthand insight into viewers\u2019 preferences and develop new original series for that audience. And doesn\u2019t it work? <\/p>\n

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What is clustering used for?<\/h2>\n

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Clustering<\/strong> is essential if your brand is truly customer-centric\u2014that is, if the decisions your business makes regarding product or service design, customer experience, and communication and marketing are based on customers\u2019 needs, motivations, interests, and behavior.<\/p>\n

Broadly speaking, clustering helps you<\/strong> segment or categorize a group of customers based on certain characteristics they have in common. This allows us to<\/strong> create subcategories within our target audience to better tailor our message and increase the efficiency of our campaigns. You can do the same with your points of sale, products, and more\u2014since clustering can help you separate and better understand whatever you group together. <\/p>\n

The first thing you need to consider is data quality<\/strong>. Remember that, unlike segmentation, clustering uses mathematical models and machine learning. If the data isn’t good, the results will be inaccurate; therefore, you’ll likely need to normalize<\/strong> the data before you begin. <\/p>\n

To do this, it is essential to have an effective data strategy<\/strong> that takes the following into account:<\/p>\n