Customer churn prediction

Neil Williams | December 18, 2022

This post gives an overview of how to carry out customer churn prediction for a UK electricity supplier based on historical billing data.

The data artefacts needed

To perform customer churn prediction in the UK electricity industry, you will need a variety of data artefacts, or data sets, that contain information about customers and their interactions with the electricity company. Some of the data artefacts that may be relevant for customer churn prediction include:

Customer demographic data: This data includes basic information about customers, such as their age, gender, income, and location. It can help to identify patterns or trends in customer churn that are related to these factors. 2 . Customer billing and payment data: This data includes information about customers’ electricity usage, billing history, and payment behavior. It can help to identify customers who are at risk of churning due to high bills or payment difficulties.
Customer service data: This data includes information about customers’ interactions with the electricity company, such as calls to customer service, service requests, and complaints. It can help to identify customers who are at risk of churning due to negative experiences with the company.
Customer feedback data: This data includes information about customers’ satisfaction with the electricity company and their willingness to recommend the company to others. It can help to identify customers who are at risk of churning due to dissatisfaction with the company’s services.
Market data: This data includes information about market conditions and trends that may affect customer churn, such as changes in electricity prices, the availability of competing suppliers, and government policies.

By analyzing these data artefacts together, you can build a comprehensive picture of customers’ behavior and identify patterns or trends that are predictive of churn. You can then use this information to develop strategies for retaining at-risk customers and mitigating the impact of churn on the company’s business.

The Python modules needed

To carry out customer churn prediction within the UK electricity industry using Python, you will need to use a number of Python modules that provide specific functionality. Some of the modules that you may need include:

Pandas: This module is used for data manipulation and analysis. It provides tools for reading and writing data from various sources, such as CSV files and databases, and for cleaning and transforming data.
NumPy: This module is used for numerical computing and provides support for arrays and matrices. It is often used in combination with Pandas for data manipulation and analysis.
Scikit-learn: This module is a machine learning library that provides a wide range of algorithms for classification, regression, clustering, and other tasks. It can be used to build predictive models for customer churn prediction, such as decision trees, random forests, and support vector machines.
Matplotlib: This module is a visualization library that can be used to create graphs and plots to visualize data and trends. It can be used to explore and understand customer churn data, and to communicate results to stakeholders.
Seaborn: This module is a visualization library that provides more advanced graphs and plots than Matplotlib, and is often used in combination with it. It can be used to explore and understand customer churn data and to communicate results to stakeholders.

By using these modules in combination, you can perform a variety of tasks related to customer churn prediction, including data manipulation and cleaning, model training and evaluation, and visualization of results. You may also need to use other modules or libraries depending on the specific requirements of your project.

How to extract, transform and load the inputs

To extract, transform, and load (ETL) the data needed for customer churn prediction within the UK electricity industry, you will need to follow a set of steps to acquire the data, prepare it for analysis, and load it into a suitable storage format. Here are some general steps that you could follow:

Identify the data sources: The first step in the ETL process is to identify the data sources that will be used for customer churn prediction. These may include internal data sources, such as customer billing and payment records, and external data sources, such as market data or customer feedback data.
Extract the data: Once you have identified the data sources, you will need to extract the data from these sources and store it in a local or temporary location, such as a file or database. Depending on the data sources, you may need to use different methods for extracting the data, such as SQL queries, web scraping, or API calls.
Transform the data: After extracting the data, you will need to transform it into a form that is suitable for analysis. This may involve cleaning and preprocessing the data to remove errors, duplicates, or missing values, and aggregating or summarizing the data as needed. You may also need to perform feature engineering to create new variables or features that are relevant for customer churn prediction.
Load the data: Once the data has been transformed, you will need to load it into a storage format that is suitable for analysis. This may involve creating a database or loading the data into a data warehouse or data lake. You may also need to create indexes or schemas to enable efficient querying of the data.

By following these steps, you can extract, transform, and load the data needed for customer churn prediction within the UK electricity industry in a systematic and efficient manner. You may need to modify these steps depending on the specific requirements of your project and the characteristics of the data sources.

The algorithms used

To perform customer churn prediction within the UK electricity industry, you will need to use algorithms that can analyze data and identify patterns or trends that are predictive of churn. Some of the algorithms that are commonly used for customer churn prediction include:

Decision trees: Decision trees are a type of machine learning algorithm that creates a tree-like model of decisions based on the characteristics of the data. They can be used to predict customer churn by identifying the most important features or variables that are associated with churn, and using them to make a series of binary decisions about whether a customer is likely to churn.
Random forests: Random forests are an extension of decision trees that create an ensemble of decision trees and combine their predictions to make a final prediction. They are often more accurate than single decision trees and can be used to predict customer churn by identifying the most important features or variables that are associated with churn, and using them to make a series of predictions based on multiple decision trees.
Support vector machines: Support vector machines (SVMs) are a type of machine learning algorithm that tries to find the hyperplane in a high-dimensional space that maximally separates different classes. They can be used to predict customer churn by training a model on a data set of customers who have churned and those who have not, and then using the trained model to predict whether new customers are likely to churn.
Logistic regression: Logistic regression is a type of statistical model that estimates the probability of a binary outcome, such as churn or non-churn. It can be used to predict customer churn by training a model on a data set of customers who have churned and those who have not, and then using the trained model to predict the probability of churn for new customers.

These are just a few examples of the algorithms that can be used for customer churn prediction. There are many other algorithms that may also be relevant, depending on the specific requirements of your project and the characteristics of the data.

The results of a control run

A control run is a test or evaluation of a machine learning model using historical data that has not been used to train the model. It is a way to assess the performance of the model on data that it has not seen before, and to compare its performance to a baseline or reference point.

In the context of customer churn prediction within the UK electricity industry, a control run could involve using a machine learning model to predict churn on a data set of customers who have churned and those who have not, based on historical data that has not been used to train the model. The results of the control run would then be compared to the actual outcomes to assess the model’s performance.

There are a number of metrics that can be used to evaluate the results of a control run on historical data with respect to customer churn prediction. Some common metrics include:

Accuracy: Accuracy is the percentage of predictions that are correct. It is a measure of the overall performance of the model and is calculated as the number of correct predictions divided by the total number of predictions.
Precision: Precision is the percentage of positive predictions that are correct. It is a measure of the model’s ability to correctly identify positive cases, such as customers who are likely to churn, and is calculated as the number of true positive predictions divided by the total number of positive predictions.
Recall: Recall is the percentage of actual positive cases that are correctly identified by the model. It is a measure of the model’s sensitivity to positive cases and is calculated as the number of true positive predictions divided by the total number of actual positive cases.
F1 score: The F1 score is a composite metric that combines precision and recall into a single score. It is calculated as the harmonic mean of precision and recall, and is a balance between the two.

By evaluating the model’s performance on these metrics, you can gain insight into the model’s strengths and weaknesses, and identify areas where it can be improved. You may also want to compare the results of the control run to a baseline or reference point, such as the performance of a simple prediction model or the performance of the model on a different data set.

In addition to these performance metrics, you may also want to consider other factors that can influence the results of a control run on historical data with respect to customer churn prediction, such as the size and quality of the data set, the complexity of the model, and the assumptions made by the model. By considering these factors, you can gain a better understanding of the limitations and strengths of the model, and identify opportunities for improvement.