Clustering Algorithms The Future Of Marketing Without Cookies

The imminent end of cookies means that we have to rethink how we do digital marketing. We can no longer use this technology to track users and their consumption habits. Still, luckily other solutions allow us to do market segmentation while respecting the privacy of each user. One of these solutions is clustering algorithms.

What Are Clustering Algorithms

A clustering algorithm is a solution to group the elements of a data set according to their similarity so that different groups or clusters are generated that contain objects similar to each other. Clustering algorithms solve unsupervised machine learning problems where the data does not have any labels. We can’t tell if there are any hidden patterns in the data, so we let the algorithm find as many connections as possible.

Clustering algorithms have multiple uses, such as finding weather patterns in a region, grouping articles or news by topic, or discovering areas with high crime rates. In marketing, they are essential for market segmentation since they allow us to use our customers’ data to group them into different groups based on what they are like, how they behave, and their interests. All this allows us to carry out personalized marketing based on the needs of different users without the need to resort to the use of cookies.

Types Of Clustering Algorithms

• Based on density. In this type of clustering, data is organized based on areas with high concentrations of data surrounded by areas with low concentrations of data. The algorithm locates these sectors with a high data density and calls them groups. These clusters can take any shape, and outliers are not considered.
• Based on centroids. This clustering algorithm separates data points based on their distance from so-called “centroids”. This centroid is the natural or imaginary location representing each cluster’s center. Centroid-based clustering is most commonly used in machine learning and big data.
• It was based on hierarchies. Hierarchy-based clustering involves creating a “cluster tree” that organizes data from top to bottom. It is more restrictive than other clustering algorithms but beneficial for already hierarchical data, for example, those that come from some taxonomy.
• Based on distribution. Distribution-based clustering starts by identifying a central point. As a data point moves away from this center, the probability that it is part of the same group decreases. All data points are considered part of a group based on the likelihood that a point belongs to a given group. It is beneficial when we have an a priori idea of ​​the distribution of the data.

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