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Learn how to predict customer churn with Data Mining and Machine Learning

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Machine Learning

One of the main assets for insurance companies are their customers, that is why most of their efforts are focused on them, being the main activities the attraction and retention of them. While recruitment is related to new customers, retention has an impact on loyalty and the identification of possible leaks.

The loss of clients in insurance companies is a phenomenon that is currently being tried to alleviate, in many cases relying on Data Mining and Machine Learning techniques that help reduce the commercial efforts of insurance companies and improve the relationship with their clients, detecting the problems reported about their services in advance.

And what do we mean when we talk about Data Mining and Machine Learning?

Although it is difficult to establish the barrier that allows us to clearly differentiate between the concepts of Machine Learning and Data Mining, we can start with the fact that each one has different objectives. On the one hand, Data Mining aims to discover unknown patterns with a highly exploratory function, while Machine Learning focuses on predicting previously known patterns. In a more formal way, we can define both concepts as

Machine Learning (ML) is an artificial intelligence (AI) discipline that creates systems that learn automatically. What does it learn? It learns to identify patterns, predict behaviors, sort data, and improve autonomously.

Data Mining refers to the set of automated techniques that allow you to explore and extract hidden knowledge from large volumes of data, making it possible to uncover patterns of behavior and relationships in that data. To this end it makes use of statistical practices and in some cases, of search algorithms close to Artificial Intelligence and neural networks.

Within customer relationship management, one of the most important tasks is consumer segmentation, which is defined as the process of identifying and grouping customers with similar characteristics or requirements for the purpose of improving service and identifying different types of users.

A wide variety of methods are available to carry out this segmentation, including clustering methods based on unsupervised learning techniques with the ability to identify patterns with similar characteristics from "raw" data, i.e. with data that has not been previously typed or classified.

Another large group of methods applied to customer segmentation are those based on supervised learning techniques, and among the most popular predictive models are those:

  • Decision Trees, which represent a series of options created from rules that classify the dataset.
  • Supervised Neural Networks, these are mathematical models based on the functioning of biological neural networks that process information.

Many studies related to the identification of leakage patterns make comparisons of performance offered by these supervised techniques in the achievement of this task and some interesting works delve into the following topics:

  • Evaluation of the use of techniques such as decision trees and neural networks.
  • Comparison of the predictive performance of the model generated with Support Vector Machine (SVM) with the efficiency of logistic regression techniques and Random Forest.
  • Combining data mining techniques to obtain a classification of clients, and using statistical methods, clustering, decision trees and neural networks to demonstrate the benefits of data mining in the insurance industry.

Although all these techniques are characterised by their great capacity to extract knowledge from large volumes of data, as they are supervised methods they require that the initial data should include among the characteristics of the clients: prior knowledge of the segment to which the client belongs or the value to be estimated, and this is where unsupervised techniques come into play, given that these techniques do not have this restriction.

When we address the problem of customer leakage analysis in the insurance sector we must determine the most appropriate techniques to solve the leakage prediction, for this we have to take into account some aspects from the analytical approach of data mining, such as: the nature of the data, previous knowledge of the types of customers, the number of variables or characteristics of the customers that are held to perform the study.

We have then that given the variety of methods that can be applied in the data mining process (both in the field of artificial intelligence and statistics) and taking into account the nature of the client data to be used in the analysis process (with a medium-high number of variables and without previous knowledge of the existing client typologies), the techniques to be used range from: visual data mining, unsupervised networks, decision trees, regression techniques or supervised neural networks.

Using some of the previous techniques and always taking into account the one that best suits the case to be addressed, and with the data that insurance companies have on the history of their customers, the leakage prediction model is built. This model is a tool that will allow us to classify the clients with the greatest tendency to leakage, once classified, it will facilitate the performance of tasks such as

  • To keep clients with pretension of escape.
  • Focus resources on the clients who need the most retention efforts.
  • Improve and strengthen relationships with your customers.

As a final point, an analogy between financial institutions and insurance companies, from the point of view of their concern and the efforts they make to retain their clients and increase their portfolio, the ECB (European Central Bank) carried out a study showing the benefits generated by the use of these leakage prediction models, as it decreases the rate of clients leaving the company, which demonstrates the benefits and clear value contribution of leakage prediction models for insurance companies.

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