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Customer segmentation with machine learning

Machine learning (ML) is gaining in acceptance as a useful tool for boosting customer loyalty and reducing errors. ML techniques can be applied in a wide range of customer segmentation and customer profiling tasks, such as:

  • segmentation and clustering

  • customer profile synthesis

  • customer-facing technologies

  • customer-facing applications

Customer segmentation and customer profiling tasks are all important for boosting customer satisfaction and boosting conversion rates.

Customer segmentation

Customer segmentation is the process of segmenting customers based on e.g., their purchase behaviour over the previous time (e.g., years), or on preferred products offered by the company. Examples for main characteristics of a possible customer segment are as follows:

  • They are highly mobile customers, usually residing in urban areas.

  • They are typically customers of a mobile operator.

  • They generally have a low income and / or members of a demographic group that is equivalent to more mobile customers.

  • They are generally loyal customers, who renew their subscriptions at the same time they can receive promotional offers from their mobile operators.

Machine Learning

Machine Learning (ML) is an effective and common method of data collection and processing in the data mining field. As a result, it is often referred to as data mining.

But is it right for customer segmentation?

The short answer is yes. Customer segmentation results from the right mix of factors. For example, it can be due to the offer made by the company, the product received by the customer, the environment in which the customer was located at the time the offer was made, and the product or service performed by the product or service recipient. For example, a poorly performing customer relationship can be attributed to a number of different factors, such as the offer made by the company. Good customer profiling results from strong ML methods to cluster the mix of factors right for customer profiling.

There are many ways to perform clustering. The typical technique is to first analyze the data on various data points and dimensions. Then, you perform clustering on these data points, learning new things about the customers through the clusters (i.e., their interactions with the products offered by the company). This is called latent-space based data mining, since in various methods, high dimensional attributes are projected into a low dimensional space. In general, research on models has also focused on dimensionality reduction, i.e. on the number and types of features that the model can process at once in computing to get a meaningful supplied data clustering.

One of the most popular techniques for dimensionality reduction in customer segmentation is Principal Component Analysis (PCA). This is a popular technique in data mining as it allows you to cluster the data and obtain good clustering results with a relatively small number of features. It is a technique that works for continuous and discrete data. Principal Component Analysis (PCA) is a popular method of data mining because of the wide range of features that it can process at once. You can process data in a certain number of dimensions and still get interesting and meaningful clusters. You can then perform linear or non-linear analyses in order to get the most out of your machine learning model and segmentation.

Deep Embedded Clustering (DEC) is a deep learning technique that was originally developed to perform dimensionality reduction in data mining. It has since spread to other areas. A recent analysis of deep learning and clustering concluded that Deep Clustering (DC) is an effective high-performance machine learning method for clustering of data. Deep Embedded Clustering (DEC) is a novel deep learning technique because it allows you to embed data representations in higher dimensional data. In technical terms, embedding data is where you compress and transform the data before you analyze it. In general, data compression and data transformation are very important for moving data from one format to another. In customer segmentation, data compression and data transformation are very important because it allows you to learn more about the customers through their interactions with the products offered by the company. This allows you to e.g., make better product recommendations.

Some research into segmentation and customer profiling has focused on models, where the input data is a single variable to e.g., only illustrate a single process, or product-customer relationships. However, most recent research has concentrated on multi-variable models, where the input data consists of multiple variables to e.g., to illustrate multi-processes in customer, product, service, or financial relationships. Here, the whole the whole input data and output is more complex to process and more abstract.

Time series are more spatially and temporally varied than discrete time courses. It is therefore also possible and efficient to perform clustering on time-series, which allows you to obtain realistic and reliable clustering results.

What are the pros and cons of each approach?

There are many more approaches for clustering specifically used in marketing. In the previous sections of this article, we tried to describe some of the pros and cons of each approach in detail.